January 19, 2018
Why a Customer Data Platform is Key for Effective Marketing Measurement
By Sam Carter, Fospha
“One of the biggest pitfalls for performance measurement is to measure the ‘part’ with ignorance of the ‘whole,’” wrote Pearl Zhu, author of the Digital Master book series. Her quote perfectly reflects the growing need for businesses to understand marketing effectiveness across the ‘whole’ customer journey – rather than the individual value of a conversion from a single touchpoint (the ‘part).
But whilst the need for end-to-end marketing measurement is not a secret – with a survey by BrightFunnel (2017) finding that a whopping 91% of marketers agree that measurement of their efforts is a top priority - few find themselves able to achieve this goal, with only 13% of respondents ranking their measurement abilities as ‘excellent’ in this same survey.
Having the right tools in place is undeniably key to helping marketers accurately measure the impact of their work, yet 51% of marketers only use Excel spreadsheets and haven’t yet implemented CRM, marketing automation tools, or a dedicated attribution platform.
Multi-touch attribution modelling accurately assigns value to a marketing touchpoint based on its position in the entire customer journey, rather than based on an arbitrary metric such as first or last click. For this reason, it is the pinnacle of effective marketing measurement.
Underpinning multi-touch attribution modelling is a clean, single source of data. And for this, a Customer Data Platform is key.
Here are Fospha’s top three reasons why a Customer Data Platform is key for effective marketing measurement.
1. Single Customer View
Marketing measurement is contingent upon every step in the customer journey being measured. A Customer Data Platform gathers, integrates and centralises customer data from various sources to provide a single source of truth of what steps a customer has taken on their path to purchase. Data sources are uniquely tagged in order to ensure that customer data is being captured at every possible stage of the customer journey. With this single customer view, marketers can ensure that every interaction, across multiple channels and devices, has been taken into consideration when measuring the effectiveness of their marketing mix. Multi-touch attribution modelling is similarly contingent upon the data source being as rich as possible, and the Customer Data Platform’s single customer view therefore easily acts as the single, accurate source that should be input into the attribution modelling algorithm.
2. Integrate Online and Offline Data Sources
A Customer Data Platform also possesses the capability to integrate both online and offline data sources; an act that is often over looked by rudimentary measurement systems such as first or last click attribution. Without measuring both sources of data, marketers will never be able to fully understand how their marketing activities relate to customer conversions. Much like the single customer view acts as the single source of accurate data across the entire customer journey, a Customer Data Platform will easily integrate and stitch online and offline data points in order to enrich the customer view – whereas other integration tools do not necessarily possess this capability.
3. Connect Multi-Device Data
Shoppers use an average of 5 connected devices in their purchase process (CampaignLive, 2015) – an increase from 2.8 devices from the previous year. Clearly, the customer path the purchase is becoming increasingly complex, and marketers need to be able to take this into account when understanding how their marketing activities relate to conversions. For instance, are customers more likely to convert when they browse on a mobile and buy online, or is mobile advertising sufficient to induce a purchase? A Customer Data Platform easily accounts for this complex customer journey, by bringing together multi-device data so multi-touch attribution modelling can take into account the relationship between marketing activities and customer browsing devices to provide the most accurate measurement of value.
In summary, marketers know that accurate marketing measurement is key to effective marketing. But, in order to do this, they need to ensure they have an accurate source of data from which to carry out measurement techniques – such as multi-touch attribution modelling. The result of using a Customer Data Platform – with its ability to integrate and stitch various data sources – is an accurate display of a customer’s path to purchase across a myriad of marketing channels, devices and sessions.
January 17, 2018
CDP Vendor Classification: One More Draft
David Raab, CDP Institute
Remember the CDP classification project that’s been percolating since last September? Last week I circulated another draft approach to the CDP Institute Board of Advisors for comment. As always, their feedback was immensely helpful. In particular, they’ve convinced me that we can’t entirely separate “What makes a CDP?” from “How do CDPs differ from each other?” Answering that second question remains the primary goal for this project. But the answers to the first question are needed to put the second question in context. My solution is to present both sets of answers but to separate them so people can focus each as needed.
Stepping back a bit: this project began because I heard frequent complaints that about the wide variation among systems classified as Customer Data Platforms. This was causing confusion in the marketplace and leading to questions about whether CDP is a meaningful category.
My first line of defense was to clarify the CDP definition itself, for example in this Library paper and, more visually, this blog post. But that doesn’t help users who need to a rapid way to narrow their choices among 50+ CDP vendors. So the next step was a project to clarify which CDPs did what.
The result of this project must be easily understood by people who just starting to explore buying a CDP. These are likely to be marketers or marketing technologists who know the problems they face but are unfamiliar with the details of CDP systems. This argues for starting with use cases, which potential buyers should understand, and relating these to CDP features, which potential buyers won’t know much about.
But most CDPs support nearly all use cases to some degree, so that discussion immediately gets lost in nuances that will only confuse buyers looking for clear direction. Detailed feature checklists would be even more baffling to people who are just starting out. My solution in the past has been narrative descriptions of system features, which can capture some of complexity that checklists mask. But narrative descriptions also rely heavily on readers knowledge of they imply.
Our recent CDP Industry Update pointed towards a solution. Looking for an easily understood way to present information from that report, I found it made sense to group vendors into three broad categories: systems that only build the unified customer database; systems that offer the database plus analytics (especially list building, a.k.a. segmentation); and systems that include the database, analytics, and “customer engagement”, which really means message selection. Those are fairly easy distinctions that any marketer or marketing technologist should understand regardless of what they know about CDPs. Equally important, most buyers should have some idea of which they want their system to do, so this sort of classification should meet our primary goal of helping them decide which products to examine more closely.
This insight – that the classification scheme should be based on what marketers already know about their needs – lends itself to a further subdivision based on channels supported, since that's another thing marketers know from the start.. As it happens, CDPs vary considerably in which channels they support – and unlike generic use cases, support for a particular channel can easily be tied to specific system features.
The combination of broad functions plus channels supported should allow marketers with no knowledge of CDP technology to quickly identify which CDP vendors might suit their needs. Assigning the vendors to these categories is fairly easy since the decisions can be based on a few key features that can are relatively easy to assess. This won’t capture the subtle differences between systems but that’s something we leave to the marketers themselves. Indeed, it’s something we want marketers to do because only they can determine which of the hundreds of fine differences matter in their situation. And even if we did build that list, it would soon become out of date.
This line of reasoning led to the feature list I sent to the Advisors. Their comments led me to add a few features that distinguish CDPs from other types of systems. By definition, these apply to all CDPs, so they don’t help with choosing one CDP over another. But they do help marketers understand why they’d choose a CDP over a non-CDP. A list of shared features also helps clarify that the other listed items are NOT essential CDP requirements. The Advisors’ comments showed that’s wasn’t necessarily clear.
In short, we’ve ended up with two classes of CDP features:
- Shared CDP features, which are present in all CDP systems but not necessarily in other types of systems. So they constitute both a baseline for understanding what a CDP can do and a benchmark for comparing CDPs with alternatives. All are related to data management.
- Differentiating CDP features, which are present in only some CDP systems. This includes three subclasses of data management, analytics, and customer engagement.
It’s important to stress that even though all CDPs build a database, the features listed under "data management" are not required to do that. Rather, they are advanced features that support specific use cases. Channel-specific features illustrate this clearly: every CDP can load Web data by importing a flat file but not every CDP provides cookie management.
It’s also worth noting that the channel-specific features all relate to data management, not to analytics or engagement. Some CDPs also provide channel-specific engagement features, such as injecting personalized messages into a Web site. That is the sort of detail we’ve decided not to include. What we do capture is which systems have special features to manage data from a given channel and which do some type of engagement management. If a system manages data from a particular channel and does engagement, there’s a good chance it does engagement in that channel. You can be even more confident of the opposite: a system that doesn’t manage data from a particular channel almost certainly won’t do engagement in that channel. That’s all we need to know to quickly eliminate systems that don’t match the user’s needs. Some of the remaining systems will later be eliminated after closer examination, but that's okay: our job is only to reduce the number of systems that must be examined closely.
We’ll get to the actual feature list shortly. But first I want to state again that this is not intended to be a comprehensive list of CDP features. In fact, the goal is the exact opposite: to find the minimum list of features that can distinguish CDP vendors in each category. This simplifies matters for buyers, vendors, and whoever is assembling the data. The lack of detail also forces buyers to look more deeply at the individual products to tell them apart. That’s important because buyers too often rank products based on feature lists without bothering to understand which features are important in their particular situation.
Let me also clarify that one CDP can belong to several categories. A CDP could have analysis or engagement features without doing advanced data management features. Or it could do advanced data management in some channels but not others. The intent is for buyers to decide which of the features they need and find vendors that have them.
Without further ado, here is the list. It’s not yet cast in concrete, so please comment publicly or privately if you see something you think should change..
Shared CDP Features
Every CDP can do all of these. Non-CDPs may or may not.
- Retain original detail. The system stores data with all the detail provided when it was loaded. This means all details associated with purchase transactions, promotion history, Web browsing logs, changes to personal data, etc. Inputs might be physically reformatted when they’re loaded into the CDP but can be reconstructed if needed.
- Persistent data. The system retains the input data as long as the customer chooses. (This is implied by the previous item but is listed separately to simplify comparison with non-CDP systems.)
- Individual detail. The system can access all detailed data associated with each person. (This is also implied by the first item but is a critical difference from systems that only store and access segment tags on customer records.)
- Vendor-neutral access. All stored data can be exposed to any external system, not only components of the vendor’s own suite. Exposing particular items might require some set-up and access is not necessarily a real time query.
- Manage Personally Identifiable Information (PII). The system manages Personally Identifiable Information such as name, address, email, and phone number. PII is subject to privacy and security regulations that vary based on data type, location, permissions, and other factors.
Differentiating CDP Features
A CDP doesn’t have to do any of these, although many do one and some do all. These are divided into three subclasses: data management, analytics, and customer engagement.
These are features that gather, assemble, and expose the CDP data.
These apply to all types of data.
- API/query access. External systems can access CDP data via an API or standard query language such as SQL. It’s just barely acceptable for a CDP to not offer this function and instead provide access through data extracts. But API or query access is much preferred and usually available. API or query access often requires some intermediate configuration, reformatting, or indexing to expose items within the CDP’s primarily data store. Those are important details that buyers must explore separately.
- Persistent ID. The system assigns each person an internal identifier and maintains it over time despite changes or multiple versions of other identifiers, such as email address or phone number. This allows the CDP to maintain individual history over time, even when source systems might discard old identifiers. CDPs that use a persistent ID applied outside of the system do not meet this requirement.
- Deterministic match (a.k.a. “identity stitching”). The system can store multiple identifiers known to belong to the same person and link them to a shared ID (usually the persistent ID). This enables the system to connect identifiers indirectly: for example, if an email linked to an account is opened on a particular device, subsequent activity on that device can also be linked to the account.
- Probabilistic match (a.k.a. “cross device match”). The system can apply statistical methods and rules to identify multiple devices used by the same person, such as computers, tablets, smart phones, and home appliances. While many CDPs rely on third party services for this sort of matching, this item refers only to matching done by the CDP itself.
Schema-Free Data Management.
This refers to loading data without defining its contents in advance. This greatly reduces the effort to add a new data sources or new data types within an existing source. It is most relevant when dealing with unstructured or semi-structured sources such as Web logs, social media comments, voice, video, or mages. It can also load data from structured sources such as transaction systems. Semi-structured and unstructured data are typically managed with “big data” technologies such as Hadoop. Nearly all CDPs use some version of this technology but it’s only essential if clients have unstructured or semi-structured sources and/or very high data volumes. Some CDPs handle very high data volumes in structured databases such as Amazon Redshift.
- JSON load. The system can accept and store data through JSON feeds without the user specifying in advance the specific attributes that will be included. Additional configuration may later be required to access this data. There are some alternatives to JSON that offer similar capabilities.
- Schema-free data store. The system uses a data store that does not require advance specification of the elements to be stored. Examples include Hadoop, Cassanda, MongoDB, and Neo4J.
This refers to interactions with the company’s own Web site, whether on a desktop computer or mobile device.
- Cookie management. The system can deploy and maintain Web browser cookies associated with the client’s own Web site. The cookies can be linked to customer records in the CDP database.
This refers to interactions with mobile apps created by the company.
- SDK load. The system offers a Software Development Kit (SDK) that can load data from a mobile app into the CDP database. It must be able to associate the data with individual customers in the CDP database. This is usually done through an app ID. Other SDK features such as message delivery are not a requirement for this item.
This refers to interactions through display advertising networks, including social media networks.
- Audience API. The system has an API that can send customer lists from the CDP to systems that will use them as advertising audiences. The receiving systems might be Data Management Platforms, Demand Side Platforms, advertising exchanges, social media publishers, or others. Ability to receive information back from the advertising systems is not a requirement for this item.
- Cookie synch. The CDP can match its own cookie IDs with third party cookie IDs to allow the marketer to enrich profiles with external data or reach users through advertising networks.
This refers to interactions managed through offline sources such as direct mail and retail stores, where the customer’s primary identifier is name and postal address.
- Postal Address. The system can clean, standardize, verify, and otherwise work with postal addresses. This processing is reduces inconsistencies and makes matching more effective. Systems meet this requirement so long as the address processing is built into system process flows, even if they rely on third party software. Systems that send records to external systems in a batch process do not meet this requirement.
- Name/Address Match. The system can find matches between different postal name/address records despite variations in spelling, missing data elements, and similar differences. As with postal processing, systems can meet this requirement with third party matching software so long as the software is embedded in their processing flows.
Business to Business
This refers to companies that sell to other businesses rather than to consumers.
- Account-level data. The system can maintain separate customer records for accounts (i.e., businesses) and for individuals within those accounts. This means account information is stored and updated separately from individual information. It also means that selections, campaigns, reports, analyses, and other system activities can combine data from both levels.
- Lead to Account Match. The system can determine which individuals should be associated with which account records, using information such as company name, address, email domain, and telephone number. This excludes processing done by sending batch files to external vendors.
These are applications that use the CDP data but don’t extend to selecting messages, which is the province of customer engagement.
- Segmentation. The system lets non-technical users define customer segments and automatically send segment member information to external systems on a user-defined schedule. Ideally, all data would be available to use in the segment definitions and to include in the extract files. In practice, some configuration may be needed to expose particular elements. Systems meet this requirement regardless of whether segments are defined manually or discovered by automated processes such as cluster analysis.
- Incremental attribution. The system has algorithms to estimate the incremental impact of different marketing activities on specified outcomes such as a purchase or conversion. Attribution is a specialized analytical process that relies on the unified customer data assembled by the CDP. Algorithms vary greatly. To qualify for this item, the algorithm must estimate the contribution of different marketing contacts on the final result. That is, fixed approaches such as “first touch” or “U-shaped distribution” are not included.
- Automated predictive. The system can generate, deploy, and refresh predictive models without involvement of a technical user such as a data scientist or statistician. This usually employs some form of machine learning. There are many different types of automated predictive; systems meet this requirement if they have any of them.
This refers to applications that select messages for individual customers. It does not include content delivery, which is typically handled outside of the CDP.
- Content selection. The system can select appropriate marketing or editorial content for individual customers in the current situation, based on the data it stores about them, other information, and user instructions. The instructions may employ fixed rules, predictive models, or a combination. Selections may be made as part of a batch process.
- Multi-step campaigns. The system can select a series of marketing messages for individual customers over time, based on data and user instructions. The message sequence is defined in advance but may change or be terminated depending on customer behaviors as the sequence is executed.
- Real-time interactions. The system can select appropriate marketing or editorial content for individual customers during a real-time interaction. This requires accepting input about the customer from a customer-facing system, finding that customer’s data within the CDP, selecting appropriate content, and sending the results back to the customer-facing system for delivery. The results might include the actual message or instructions that enable the customer-facing system to generate the message.
There you have it: 26 relatively simple items that I think offer a meaningful way to differentiate among CDP systems. What do you think?
January 12, 2018
Signs You Need a Customer Data Platform
By David Raab, CDP Institute
Someone asked me the other day for a list of reasons someone would buy a CDP and what they'd get. The results seemed worth sharing. Here they are.
Signs You Need a CDP
• Your existing customer-facing systems capture useful data about your customers, but you can’t collect it all in a single place.
• You can collect customer data from multiple systems but can’t enhance it with data from external sources.
• You can collect customer data from multiple systems but can’t connect items that refer to the same individual to create a Single Customer View.
• You can build a Single Customer View but only have access to predefined summary information, such as segment assignments, and not underlying details such as details of transactions or Web page views.
• You can build a Single Customer View with detailed data but can’t analyze it, build predictive models, or run machine learning algorithms against it.
• You can build and analyze a detailed Single Customer View, but the data isn’t available to systems that create outbound marketing campaigns (e.g. email or advertising audiences) or real time interactions (e.g. Web site personalization)
• Your IT department has promised to provide a Single Customer View but they haven’t said when it will be available, what it will cost, or what it will actually do. Or they’ve made promises and failed to deliver.
• Your marketing cloud vendor has promised to provide a Single Customer View but they haven’t said when it will be available, what it will cost, or what it will actually do. Or they’ve made promises and failed to deliver.
• You have a detailed, actionable Single Customer View but it’s hard to add new data sources or change how data is processed.
• You have a detailed, actionable Single Customer View but it’s hard to connect it to new systems so they can use its data.
• You have a detailed, actionable Single Customer View but the data is days or weeks out of date.
• You have a detailed, actionable Single Customer View but it costs a fortune to operate.
• You have customers in the European Union and can’t meet the requirements of General Data Protection Regulation (GDPR) and ePrivacy to capture permissions, track usage, respond to data access and removal requests, etc.
Benefits You Can Get from a CDP
• Uncover data quality problems and inconsistencies by comparing data from different systems about the same customer
• Create a ‘golden record’ containing the most accurate information about each customer across all systems.
• See patterns in customer behavior across large numbers of customers.
• Analyze customer journeys based on detailed data.
• Compare behaviors of customers from different sources, with different attributes, who saw different marketing campaigns, who bought different products, etc.
• Calculate value of customers, in aggregate and by customer segment (based on source, attributes, etc.)
• Identify segments of customers with similar attributes and behaviors.
• Automatically assign customers to standard segments and use these segments in marketing campaigns.
• Build predictive models based on detailed customer data
• Calculate the value and predict behaviors of individual customers based on their personal history
• Find the best treatment for each customer in each situation, based on their personal history and other relevant information (product inventory, local weather conditions, competitive activities, etc.)
• Use customer data in personalized messages (and be sure it’s correct).
• Coordinate customer treatments across channels based on a shared view of their data.
• Synchronize customer data with other systems such as CRM and DMP.
January 2, 2018
CDP Industry Doubled in 2017: Latest CDP Industry Update
by David Raab, CDP Institute
The CDP industry continued its strong growth in the second half of 2017, with new companies appearing from outside the U.S. and more products offering customer engagement features.
That's the headline from the latest CDP Industry Update, the CDP Institute’s comprehensive analysis of CDP vendor growth and distribution. Compared with the first report, one year earlier, coverage has grown from 24 to 52 vendors. Some are brand new companies but most are existing firms that have repositioned as CDPs.
Companies outside the U.S. accounted for 35% of the vendor count, although just 29% of the employees, 19% of total funding and 12% of new funding, In other words, U.S. firms remain the heart of the industry even though their share is shrinking.
Similarly, CDPs with customer experience features such as personalization and campaign management have grown from 29% to 48% of the report, but firms offering just data assembly and analytics features account for 57% of employment, 72% of total funding, and 91% of new funding. Nearly all firms added to the list since the original report are in the customer experience category. (The report only includes CDPs, so firms in the customer experience category also provide data assembly and analytics.)
Total employment among listed firms more than doubled, from 2,000 to 4,400, as measured by LinkedIn. Employment at firms listed in the original report grew to 2,500, a 27% year-on-year increase. The balance of the growth was from firms added later. The lists of largest and fastest-growing individual firms were dominated by data assembly and analytics vendors, although customer experience firms showed some of highest percentage growth, starting from small bases.
In short, the report presents a vibrant industry that’s expanding both through organic growth at existing firms and by accruing new participants. If you’re looking for dramatic tension, there’s a conflict between the older, larger data assembly and analytics vendors and the newer, smaller customer experience products. The drama may be a bit artificial but it does point to a choice that marketers must make between using the CDP as a shared data source and using it to actively manage customer experience. Chances are both approaches will succeed, since different marketers will make different choices depending on their situations. But those who enjoy a good horse race should feel free to place their bets on which will emerge as the most common selection.
Download the full report here.
December 26, 2017
Survey: Most CDP Institute Members Want General Information
By David Raab, CDP Institute
It took three emails within 24 hours but enough members of the CDP Institute responded to our survey to provide useful results. The main goal was to understand what members wanted from the Institute. The answers were quite clear: most want general information and industry research. Smaller numbers wanted help with picking vendors, such as vendor comparisons and evaluation checklists. Even fewer want post-implementation services such as training, peer discussions, and conferences.
The survey also asked background questions about the respondent’s existing systems and status of CDP in their organization. There was a bit of differentiation: people with CDPs deployed or in process were a little less interested in general information and surveys, and a little more interested in case studies, peer discussions, and conferences. Company size and business type showed more of split: people from smaller companies were much more interested in practical resources like evaluation checklists, lists of consultants, training, and peer discussions. People from larger companies were particularly interested in vendor comparisons. B2B marketers of all sizes were more interested in industry research and surveys than B2C marketers, probably because many of the B2B marketers themselves sell CDPs or related products and services.
Since CDP Institute members are anything but a random sample of the marketing community, their responses don’t tell us much about the industry as a whole. Still, it’s worth noting that big B2C companies were much more likely than others to have many disconnected systems and to have a CDP deployment in process or planned for the next future. This supports the general view that B2C enterprises are the core of the CDP market – although in absolute numbers, there were more CDP deployments under way and planned at B2B and small B2C firms.
You can download the full survey report here. Enjoy, and thanks again to those who participated.
December 21, 2017
Retaining Customes Requires Understanding Customers
By Amy Cross, NGDATA
For any business that provides a product or service to customers, the act of finding, targeting and obtaining new customers is always going to be among its top priorities. But, what many businesses tend to forget is that once a customer makes the first purchase, there is much more to be done in the customer relationship. Smart businesses know that the first transaction is really just the beginning, and that the real business value lies in retaining that customer.
It’s far less expensive – not to mention much easier – to keep existing customers than it is to find and convert new customers. Because of this, customer retention is more important than ever to organizations, especially those that consider customer lifetime value. In order to grow customer retention rates, you need to focus on engaging customers, delighting them with their experience, exceeding their expectations and building their loyalty. According to consulting firm Walker Info, “By 2020, customer experience will overtake product and price as the key brand differentiator.”
Of course, keeping customers happy so they keep coming back is a critical component of your business’ success. Organizations implement customer retention programs to retain as many customers as possible and to make the entire customer journey a valuable one.
Retaining customers requires giving them an excellent customer experience throughout their entire lifecycle by personalizing every interaction. The only way to deliver truly personalized and relevant experiences is by completely understanding your customers.
That’s where your customer data comes into play. Your customers generate data at every touchpoint, but harnessing all of that data is a nearly impossible task without utilizing data-driven technology. You can eliminate the data silos, connect the dots of a customer’s interactions across multiple channels, and build a comprehensive, holistic customer profile to gain a real-time, trending, and complete view. When you truly understand your customers, you can act on potential churn well before your customer considers it.
For further interest:
December 18, 2017
A Single View of the Customer is Essential to Your Success
By Patrick Tripp, RedPoint Global
The modern customer creates a veritable treasure trove of data through dozens of digital and physical touchpoints, all of which can be actioned to craft more relevant messaging and power more engaging experiences. The problem is that much of this data is split into internal silos based on specific technologies and closely guarded by internal departments.
It’s because of these data silos and fragmented solution portfolio that brands have largely failed to maximize the business value of their customer data. This must change. Brands can ill afford to continue the current state of disconnected data and point solutions. What brands need is a solution that can unify disparate consumer data into the single view of the customer that will power contextually relevant experiences regardless of channel.
The Power of a Single View of the Customer
Consumers live a connected, omnichannel life with seamless transitions between digital and physical touchpoints. The same customer who might browse the racks at a retail store could simultaneously be checking competitors’ websites for a better price or searching for coupons on the retailer’s own site. Adding to this environment’s complexity is that consumers expect to have a consistent brand experience across all channels and touchpoints; they also want to be recognized as loyal, repeat customers and gain all the benefits that entails. Native online retailers have already accomplished this goal, and consumers expect that every brand should be able to do the same.
Legacy brands across all industries have failed to create a common brand experience across online and offline engagements. This failure is tied to the lack of a unified view of the customer. Recent CMO Council research bears this out, with only seven percent of brands able to deliver real-time data-driven engagements across online and offline touchpoints. The single view of the customer, also known as a “golden record,” is vital for success in the modern business landscape.
Most brands store customer data in functional and channel-specific silos tied to solutions that don’t share information. Having a single view of the customer means breaking down internal silos and blending anonymous and known data into one composite customer profile that showcases a consumer’s entire interaction history with the brand. Once this customer data is unified, brands can more readily deliver the right interaction at the right time, just like how becoming one with the Force allowed Luke Skywalker to fire the proton torpedoes that destroyed the first Death Star.
Brands may not be fighting a Galactic Empire, but the lack of ability to understand customer behaviors and preferences does have a dramatic impact on revenue. Boston Consulting Group recently found that personalization in retail, health care, and financial services will push a revenue shift of $800 billion over the next five years to the 15 percent of companies who get it right. Brands can’t personalize effectively, and capture that revenue bump, if they lack a single view of the customer.
Customer Data Platforms and the Single View
The desire to unify customer data into a central location is not a new one. Legacy data management solutions, such as data warehouses and data lakes, already accomplish this goal for a variety of data types and structures. But it needs to be said that collecting data into a central database is not the same thing as making that data available to business users. A single view of the customer is functionally useless if it can’t be easily accessed by the departments who need it most.
This is where customer data platforms (CDPs) come in. The idea behind a CDP is to maintain an always-on, always-processing customer golden record that is available at low latency throughout the enterprise. CDPs ingest any type of data, regardless of structure, at batch and streaming cadences and unify it into a central point of data visibility and control. The true power of a CDP lies in making this unified data accessible by the solutions and departments that need it most.
What this means from a functional perspective is that business users can access the data they need without making endless requests to the IT team. This enables IT to focus on tasks of greater strategic value to the organization, while also empowering business users to act at the speed of the customer. Because of its ability to unify data regardless of source, type, and cadence, CDPs are a foundational component of an effective omnichannel customer engagement strategy. The prowess of a CDP at connecting customer data also opens the door to effective real-time customer engagement, which is increasingly vital for the modern connected consumer.
Unified Data Leads to Engagement Success
McKinsey recently found that 50 percent of customer interactions now happen during a multi-event, multichannel journey. For most companies, this data is spread among multiple departments and multiple technology silos. This siloed data environment no longer functions as well as it once did; now, brands that can unify customer data will succeed where those who retain internal silos will falter.
CDPs are the technological solution to this problem, enabling brands to craft contextually relevant messaging that reaches consumers in the moment of need through the channel they prefer. The automotive service center company Monro recently experienced this when they deployed the RedPoint Customer Engagement Hub™, which is underpinned by our customer data platform. Monro used the RedPoint solution to unify its customer data and deliver more relevant messaging to consumers through multiple channels.
Achieving a single view of the customer is an important goal for the modern brand. Customer data platforms achieve that goal and, more than that, enable companies to operationalize their customer data in such a way that they more effectively engage with the connected consumer. Consumers have already moved on to a life with seamless integration between the digital and physical worlds. Brands must follow if they wish to retain their customer base and enhance revenue in the coming years.
December 14, 2017
The Good, The Bad And The Ugly: Beware of Outlandish GDPR Claims
By Jessica Davies, Tealium
Uncharted waters are something the digital media industry should be used to by now. But the General Data Protection Regulation is a whole new ballgame.
With six months to go until the law is enforced, there’s an equal amount of sense and nonsense being spouted about how to tackle it.
Here are some of the more startling claims overheard in the wild:
The aggressive sales pitch
Supposed “GDPR experts” are making loud claims. One publisher executive balked at being pinned by a third-party vendor with the pitch: “Without using our auditing services, you leave yourself open to extinction!”
It may seem hard to believe, but despite all the scaremongering around the GDPR, a lot of companies are still in denial about it. “The amount of times I’ve heard ad networks say, ‘GDPR and ePrivacy aren’t going to affect programmatic advertising’ — it’s crazy how many companies are still in denial about it,” said a digital marketing exec.
The artful dodger
Beware any niche third-party tech vendor that claims to already be 100 percent GDPR-compliant (there are plenty of them), as it’s highly unlikely they are. The final direction on how to obtain consent and share data won’t be published until next month, so avoid those claiming to be clairvoyants.
The lazy vendor
More than a few publishers’ jaws have dropped after receiving stony responses from vendors when inquiring about how they’re preparing for GDPR compliance. Some vendors seem to believe publishers alone will be on the hook for fines. As such, they’re sitting pretty, doing nothing and expecting publishers to gain consent on their behalf.
The worryingly complacent
Some are still not taking action because they think the outcome of GDPR enforcement will be akin to that of the European Union’s ePrivacy Directive in 2011: a load of fuss made about nothing. This is not the case, however. “Most worryingly, a company stated to me they wanted to do absolutely zero, as they cited it being the EU Privacy Directive all over again. [It’s] a massive gamble not just in terms of fines, but business reputation, too,” said Lindsay McEwan, vp and managing director of Europe for tag management vendor Tealium.
The blunt approach
There’s talk that some agency holding companies plan to cap programmatic spending to limit exposure to GDPR penalties, according to industry sources. Some are also looking to instigate new insertion orders with GDPR-related terms and conditions. “Agency operating groups have ordered [their agencies] to limit exposure by reducing the amount of programmatic spend to 20 percent of total digital spend up to the end of September, I suspect to boost their own internal supply marketplaces,” said an ad tech exec. “Programmatic is a shitshow. Makes sense to avoid it and be seen to avoid it.”
The bloodsucking lawyer
GDPR lawyers, consultants and information security companies are making an absolute killing, and that will only increase. It’s best to boost your own GDPR knowledge to make your business less at their mercy.
Image courtesy of UKseobiz.com
December 11, 2017
The Omni-Channel Customer Experience: Be Where Your Customers Are
By Amy Cross, NGDATA
Customer expectations have never been higher. We are living in an omni-channeled, constantly-connected and instantly-gratified world where customers want better value out of the brands they choose to do business with. And, in today’s multi-faceted, digital world, the way customers communicate is all-encompassing – email, voice, text, TV, social media, website. There are countless ways to engage and interact today.
However, customers don’t think in terms of communications channels. They simply want an answer to their question or a solution to their issue. They may reach out via voice, through your website or app, or through your social media platforms – and not always consistently. On top of that, as mobile is now king, the lines between channels can start to blur.
If you’re only able to see your customer conversations in silos, then you’re unable to have a complete view of all interactions. Being able to connect all of the dots is crucial for successful customer experiences. You need to have access to each and every customer’s communications preferences, behaviors and context to take into account every interaction on every channel. You want your customers to be able to connect with your brand via all of your communications channels, and have the ability to pick up where they left off on one channel and continue the conversation on another.
Once you are able to engage with your customers via all channels, you’ll be able to pin-point the channel that’s optimal for each point in the journey for each type of customer – at the optimum time. Truly customer-centric brands expect their customers to jump from one channel to another, be it digital or physical. But jumping channels needs to be 100% on the customer’s terms, not yours. Every time you interact with your customers, through any channel, you have the opportunity to extend the relationships and deliver value.
Are you able to provide your customers an omni-channel experience?
For further interest:
December 7, 2017
How Many Campaigns Are Too Many: Seeking the Magic Number
By Omer Liss, Optimove
As he’s clicking the ‘send’ button, every marketer wonders– is this the one campaign that will bring back my churned customers, or is this the one that could cause my unsubscribe rates to soar? These 5 steps will help you decide
One of the toughest quandaries every marketer faces is deciphering the number of campaigns they need to send to their customers: What should the frequency look like? What amount will produce the best results?
To start answering these questions, we should first understand where they originate from: sending out a large bundle of campaigns may cause the exact opposite reaction from the desired result – serving as a distraction. The ideal case is that each campaign is impactful, but for marketers, it’s a delicate balance. Campaign bombardment can lead to a reduction in the campaign status and can cause lower open rates.
The customer will become annoyed, and you’ll lead them to the one place you don’t want them to go – the unsubscribe button, that’s where you lose them, oftentimes for good.
To try and mitigate this dilemma, we will need to dive deep into our data in order to better understand our customers behavior and feelings. In these 5 steps, we’ll try to help you reach this magical number – the optimal number of campaigns
Step 1: Definition
Let’s define the variables we want to examine:
Campaign: what is a campaign, and what purpose does it serve? Does SMS fall under this category? What about push notifications? Or, do we only want to test our email program? It’s important to stress that defining the campaign means studying every email sent out to a specific customer. Hence – sending 10K identical messages to 10K customers, is 10K different entities.
Period of Time: this term also derives from the question posed earlier. Is the aim figuring out how many campaigns we need to send during a week? 10 days? A month? Take notice in our examples that will follow, that the time period is not set according to a calendarial term. As such, seven days is a week, no matter which day we began counting. We think of a customer who received three campaigns (on Saturday, Sunday and Monday), as a customer who received three campaigns in a row, although technically, they were delivered on two different calendarial weeks.
Maximum Break Between Two Campaigns: defining this term will allow us to establish what a sequence is. We can decide to leave this term empty – hence, there’s no consideration on the maximum break between campaigns. On the other hand, we can set the number to 1, determining that any campaigns sent in a row one right after the other will be considered consecutive campaigns.
Using this set of characterizations, we can map and define every sent campaign by looking back at the time period we set and the number of campaign sent before. Here are some simple examples:
In example A, the time period is seven days, with no set maximal break between campaigns.
In example B, the time period is six days, but the maximal break is 1. Therefore, although the campaign is the 5th during these 6 days period, it’s the 4th that answers our maximal break scenario.
Response: to define the effectiveness of a campaign, we need to establish what constitutes as successful. Is it opening the email, placing a deposit, or having an active day on the site? This must be a binary answer – yes or no questions and responses. In most cases, the response will be measured on the specific day the campaign was sent out. In the example we’ll show, the response was set as day of activity.
These definitions need to be well established, as they will help us come to more fundamental answers during our research. For this piece, all our testing will be based on example A.
Step 2: Mapping
The mapping process isn’t simple, and it’s quite technical. In this stage we define each campaign according to the definitions we set in step 1. After mapping them, we will look for a response from the customer. At the end of this basic process, we can check the division of the various types of executed campaigns, and conclude the response rates (simply divide the amount of “positive responses” with total amount of sent campaigns). You can also create this analysis from simple division using criteria such as – age, country, VIP level, lifecycle stage etc.
In the table below, we see a benchmark of 12 gaming sites (17 million emails), dated from the beginning of 2017. In this case, there was a second division of customers while they received their campaigns – to new customers and active customers – to better understand the frequency alterations during the different lifecycle stages.
By this mapping, it’s clear that most emails were 2nd campaign types. It’s also obvious that the campaign distribution for the 3rd and 4th campaigns is higher at the ‘New’ lifecycle stage. That’s because the marketing plan is more organized in the new stages of its customers.
Going forward, the next stage is understanding the response rate of different campaigns types. Look at the next table:
Here we can see the response rate for email campaigns – measuring the active days on when the customer received the campaign. Active customers respond better (26% to 17% on a non-weighted average), except for the first campaign sent, which is significantly higher for new customers.
This graph, which again gathers the data from 12 different brands, doesn’t show substantial changes. When conducting your research, try to look for the main highs and lows, in order to establish your threshold.
Step 3: Different Measures
After formulating the right threshold for each group, we can run the analysis again, while changing the criteria. The final results—that determine the right number to send for each group during a specific time--must be formulated after considering differing variables. And so, even if we see a massive downfall in the response rate of campaigns that are 4th in line, we should combine it into more tests in order to reach a conclusion.
The unsubscribe rate is a good example. A number of irregular campaigns or a high frequency of campaigns can rapidly raise the amount of unsubscribes, and cause some major losses.
This next table shows the unsubscribe benchmarks for different campaign types:
We can see how much higher the unsubscribe rate is in the 1st campaign in seven days, than the campaigns that follow. Again, that is probably supported by the fact that a 1st campaign creates a unique stimulation, and many customers will make their decisions according to this communication. We also notice that the unsubscribe rate for active customers begins to rise considerably after the 5th campaign in seven days. It’s something we will have to take into account when setting our final conclusion.
Step 4: Micro-Segmentation
As stated previously, we wouldn’t want to decide based on groups that are too general. To dive deeper into our customers’ behaviors, we need to create more sub-divisions – to reach more specific groups. Examining different lifecycle stages is always a good way to go, but consider whether any sub-groups that may act differently. Do VIP players need to be tested differently? What about other players with different product preferences?
In the next example, we divided our active customers from the previous benchmarks into different groups, defined by their frequency – the average time (in days) that passed between activities.
We can see from the first table that active customers who have a high frequency rate (F<3) responded better to numerous and more recurrent campaigns. We can alsosee that customers who are active less frequently (F>7) show a drop-in response rate as the number of campaigns increases.
From the unsubscribed table, we can learn that customers who are less frequent will experience a higher jump in their unsubscribe rates percentages from the 4th campaign on.
Step 5: Conclusion
After executing numerous tests, we can start framing an answer for each group we chose. In an absolute world, all our measurements will point to (?) the same threshold and we could determine the right number of campaigns and their frequencies. But among the results, we will probably encounter some contradictions. We may see a response rate hike on 2nd type campaigns in seven days as well as a rising unsubscribe rate. To settle these types of results we’ll include additional calculations. Always try to choose the right measurement for your needs that will best serve the objective of your target groups. For example: if we look at the ‘active customers who are at risk to churn’ group, the most important measurement for that group is the response rate, as we want them to be more active. As for the ‘active customers with no risk of churn’ group, the crucial measurement is the unsubscribe rate, because we want to be able to contact them.
Once you’ve decided on the numbers of campaigns, implement the frequency results in your marketing programs by creating a test group. Check whether the customers who are receiving the definite number you came up with are responding better than your other customers.
The method suggested here is just one of many, that deals with the important question – how many campaigns should I send and at what frequency? There are additional research methods to incorporate, depending on your data, but try and implement them wisely in order to reach that optimal, yet elusive number.
December 4, 2017
How to Achieve a Dynamic, Single View of the Customer
By Lindsay Bloom, SessiomM
Many brands are struggling to meet customer expectations because technology is changing at such a rapid rate. This is changing how consumers are capable of engaging with a brand and their perception of a brand's ability to engage with them. Many of the problems and barriers that prevent a brand from meeting those expectations start with data silos and bottlenecks across the different systems that a brand uses for customer engagement.
When we’re talking about the customer engagement ecosystem, we’re referring to marketing campaigns that a brand may be delivering to a customer. That may be customer experiences across different channels like web, apps, in-store; it could be related to commerce experiences, online or with mobile devices; or it could also be related to service, or clienteling. Data moves at a certain rate from all these systems into the 360 degree view of the customer. However, even though brands have a good repository of information about customers they engage with, there’s still a massive gap between insight and action. Latency gaps between a brand recognizing they have a particular customer engagement issue to organizing the organization and their technology around addressing that issue to then providing execution around that issue, all require a tremendous amount of time.
That latency results in bad customer experiences, missed opportunities and lost revenue. What is the alternative? A dynamic, single view of the customer. In this blog post, we’re going to walk through some steps to help you modernize the environment in which you’re operating in order to make engagements with your customers become data-centric.
Align your organization around a digital transformation
You need to start by aligning your organization around a digital transformation. Many brands attempt their digital transformation by layering in technology across their organization. This is a mistake. In order to execute a successful digital transformation, you have to take a step back and look at how technology may be changing the way you engage with customers and rethink how your teams and your organizations must be structured to respond to that. The architecture for martech or for solutions within the context of organizations reflect the structure of that organization. Thus, the barriers that the organization introduce are very much human barriers, not just technology barriers.
Establish the golden record of customer engagement across your organization
You need to come to an agreement across your team and your organization to establish the golden record of customer engagement. This requires defining the type of data that needs to become available in real time to power the different channels that you have around customer engagement. Many times, it’s not enough just to store all the things that you know about a customer in one place. You need to have an agreement around all of that data in order to power positive customer experiences across all channels--whether that be customer care, in-retail, on your website, etc.
Motivate your customers to lean in and identify themselves
In order to achieve the personalization necessary to truly drive great engagement, you have to invent ways to motivate your customers to lean in, identify themselves and opt-in to experiences. Those experiences might come in a variety of ways. For example: downloading the mobile app and opting-in to certain types of personalized communication, or getting involved in some sort of incentive or loyalty program. These types of experiences are effective at creating a recurring reason for you to communicate with and engage with your customers.
Leverage tech to reduce latency in sharing the golden record across the organization
Once you’ve achieved ways to motivate your customers to lean in and identify themselves, leverage technology to reduce the amount of time it takes to share that golden record across your organization. Every new marketing system that you introduce in your marketing tech stack is going to increase the latency to recognize who the customer is and to deliver value to that customer. You should be evaluating technology that decreases the amount of time it takes to share that information. For example: if someone called customer care from his car in the parking lot of the storefront about an issue he had with a product and then walked into the store with the same problem, the agent at the store should be aware through some technology that he had just called customer care.
For most brands, that particular use case is not achievable. In many situations, every new channel is like starting at square one, which creates a continually frustrating experience for customers. Alternatively, with the right technology you can move to a world where the customer care person on the phone can actually introduce you to the in-store agent who’s going to be made available within 10 minutes at the storefront. That agent will already be aware of your problem and ready to help you. Reducing that latency by selecting the right technology will allow you to share the information necessary to fuel better customer engagement.
Once you’ve done these things, you’ll have a dynamic, single view of the customer...now what should you do? Well, now you’re capable of viewing customer behavior in context, so you’re able to view not only the state of the customer--that they had a particular issue or bought a particular product--but also where they bought that product; when they bought that product; if they returned that product; if they called customer service; what was the conversation; what was the outcome of that call. Not just viewing who the customer is, which is important, but also viewing that information in the context of everything else, applying rules of engagement and delivering the next best experience (reward, offer, content, etc.) allows you to create better, more profitable customer relationships.
November 30, 2017
The Problem with, And a Solution to, Inaccurate Customer Data
By Matthew Kelleher, RedEye
How accurate is the customer data that you hold? Could the development of your Single Customer View have been enhanced by improving the accuracy of your customer data? How confident are you that the email, phone number, postal address, customer name details etc. that you hold are accurate and up to date, enabling you to communicate with each customer effectively? Does your SCV hold all your customer data? Does your data allow you to run accurate personalised marketing campaigns?
Accurate data doesn’t just effect delivery of your marketing communications. Cleansing customer data will have a positive improvement in your customer deduplication, improve predictive analytics, provide a consistent customer experience, allow better personalisation, and even protect your brand reputation leading to increased Lifetime Value. Let’s explore this further…..
What does the Data Protection Act say about accurate customer data?
Principle 4 of the Data Protection Act states “Personal data shall be accurate and, where necessary, kept up to date”. The ICO goes on to state what steps should be taken to comply with this:
- Take reasonable steps to ensure the accuracy of any personal data you obtain
- Ensure that the source of any personal data is clear
- Carefully consider any challenges to the accuracy of information
- Consider whether it is necessary to update the information
As your customer and personal data is being used for marketing purposes, there is a duty to ensure you take necessary action to make sure customer information is accurate at the point of capture and kept up to date as they continue to have a relationship with your brand.
I would argue that you shouldn’t need the Data Protection Act or the ICO to tell you to keep personal data up to date, you should want to do this as standard across your Customer Data Platform. How can you continue to communicate effectively with each customer if you don’t?
Customer Lifestyle Changes and Challenges
Over time customer details can change. If they make you aware of this it is important that you have a process to update this information within your CDP. If they don’t make you aware that might mean that you no longer have accurate information to communicate effectively with that customer.
What happens in your database if a customer moves address? How is this information captured? Could this result in an additional customer record being created? Tracking your customers is important. If you are creating a new customer record each time a customer moves or provides a different email address or is provided with a new store card, how can you combine everything you know about that customer into one record on your SCV? How can you be confident in running personalised communication if transactional and behavioural data for the same customer is stored in different records?
Being able to highlight that a customer is no longer at the address you hold will stop postal communication being sent incorrectly, but being able to track where that customer has moved to will not only enable you to continue that communication but will also help link any previous activity or knowledge you’ve captured and provide an improved customer experience.
We should then consider the challenge that a customer may visit your website over 9 times prior to committing to any purchase, 88% of customers are just browsing, and customers can move between up to 4 different devices whist engaging with your site. This adds additional complexity when understanding each customer’s relationship with you.
This is just the start though. You then add in further channels – social, app etc. Being able to accurately connect all these different interactions within your CDP becomes increasing important to allow you to combine everything you know about that customer and run fully personalised communications.
It’s what the customer has provided to us, so we shouldn’t amend these details!
There is an element of truth in this statement. If the customer has provided you with certain information about themselves, that is what they want you to hold. As mentioned previously, customer details do change over time and they might not make you aware of this, even though it would improve their experience with you if they did.
However, sometimes the information captured initially is inaccurate. This might be because the customer has decided to give you inaccurate information. How many Mickey Mouses, Donald Ducks, Luke Skywalkers, Hans Solos, Joe Bloggs or similar do you have on your database? What if profanity has been populated within certain customer fields? Can you highlight test or dummy records within your data? These issues are fairly common when you’re reliant on customers populating personal information.
If this information is provided or stored it is impossible to correct these details. However, it is important that you can highlight these and flag appropriately. You would like to think that if a customer has populated inaccurate forename and surname details that any email, postal address, phone number or any other contact detail will also be incorrect – but can you be 100% sure that is the case?
You wouldn’t want to waste money sending communication to Roger Overandout when we know that isn’t the intended recipient. If the remainder of the contact information against that record is correct and the necessary permissions apply, then it could well get delivered and could harm brand reputation.
Not just updating…..
So, improving inaccurate data isn’t always about updating information in your CDP. It’s also about highlighting potential issues that you might find within your customer data that could lead to complications when running your multi-channel marketing campaigns.
Your CDP needs to be able to combine data elements from multiple different sources and you should be confident in the accuracy of the personal data that you capture to enable merging of data across all your channels.
Customer data is the most important element within any CDP. It not only allows you to communicate with each individual but also forms part of any deduplication process, leading to improved customer knowledge, customer experience and maintaining brand reputation. Ensuring this information is accurate is therefore key to having a fully functioning and effective CDP.
Looking for support with your data? Contact us at RedEye.
November 29, 2017
Classifying CDPs: Another Try
By David Raab, CDP Institute
I’m still working on the project to clarify differences among CDP vendors. The latest iteration is based on a functionality map I put together to help non-CDP vendors understand where they fit in the industry. This was based on the Data, Decisions, Delivery layers I often use to classify components of an over-all marketing stack. It applies to CDPs because many CDPs extend beyond the Data layer to offer Decision and Delivery functions. It also highlights that even many Data functions are optional.
The latest version of the map is below. As you see, it gives each channel (email, Web site, mobile device, etc.) its own set of possible functions for message delivery, content creation, optimization, and connectors. This makes sense because those functions usually channel-specific. I’ve cheated a bit by putting all channel-specific functions on the Delivery layer, including a few such as content creation and prebuilt connectors that I usually consider part of the Decision or Data layers. It’s just easier to understand the results when all functions related to each channel are presented together.
The remaining Decision and Data functions are not channel-specific. I’ve grouped them into broad categories (campaigns, analytics, ingestion, storage, etc.) and then listed specific capabilities for each category. This is a departure from the approach I described in my last post on this topic, which aligned all types of functions with specific channels. At the time, I noted that only about half of all the functions I listed actually did apply to a particular channel, even when I allowed pseudo-channels including un- and semi-structured data, real time processing, and B2B marketing. In reality, there’s fairly little connection between the items on different rows, so I don’t think we lose much clarity dropping the channel-based organization for decision and data layers.
|Marketing System Capabilities
||real time interact
||rules select messages
||scores in rules
||auto-classify (NLP, video)
||optimal spend by channel
||semi- & un- structured
||multi-table data model
||industry data models
||B2B data model
||cross device match
||lead to account
||anonymous to known
||external device graph
||external ID graph
||analytical data sets
What I like about the map is exactly that it encompasses all marketing functions, not just what’s done by a CDP. This lets it accommodate any function built into any CDP, now or in the future. This, in turn, lets us identify sets of functions needed to meet particular requirements. For example, the shaded boxes on the map I’ve published here are the minimum functions required to qualify as a CDP. Other collections might define functions that are common or support specific use cases such as online-only marketing or journey orchestration. But I’m getting ahead of myself.
The other big departure that this map makes from my last effort was the boxes contain general functions (“offline match”) , not specific technical features (“name/address standardization, postal address verification, similarity matching”). In some cases, additional technical criteria are probably needed to clarify what would or wouldn’t qualify as having that feature. Those could easily be appended to this map. But in many cases, the general function description is specific enough that it should be fairly clear whether or not a particular system qualifies. To help things along, I’ve added another version of the table below that gives more specific definitions for each box. I still wouldn’t necessarily ask vendors to decide for themselves whether they qualify for a particular box, but I think these are specific enough to make it pretty easy for an objective observer to make that judgement and get the vendors to accept it.
Keeping them somewhat general also fits with the broad purpose of this exercise, which is to help marketers understand which systems have the types of functions they need without trying to provide the details of how each system performs those functions. That more detailed analysis is something that marketers need to do for themselves based on direct interaction with the vendors. In other words, we’re only helping marketers exclude the clearly irrelevant products, not create a short list of the best systems for their purpose.
|Delivery (by channel)
||send complete messages and audiences in the specified channel (email transfer agent, Web CMS, mobile app, mailing list generation, Adwords bid, programmatic display ad bid, retail POS)
||automatically run tests and pick winning message (a/b or multi variant copy testing; possibly find best message per segment)
||the system provides tools to create content in the specified channel
||prebuilt API or SDK connectors to send message components to specified channel systems (templates & personalization variables inc. customer IDs; no configuration needed beyond providing credentials)
||ability to personalize anonymous users based on device, campaign, referrer, location, weather, behavior, etc.)
||prebuilt API or SDK connectors to send campaign audiences to specified systems (email lists, next best offer lists to Web personalization, advertising audiences, etc.)
|- display ads
||ability to continuously update audience segments, to send suppressions lists, to cookie synch with DMP/DSP; DMP functions
|- social ads
||ability to continuously update audience segments, to send suppressions lists
||prebuilt API or SDK connectors to ingest data from specified systems (e.g. MailChimp, Sitecore, Google Analytics, Adwords, Facebook, BlueKai DMP, NCR POS)
||collection of mobile device attributes and location, batch mobile data collection to save battery
||prebuilt API or SDK connectors to expose data to specified systems (e.g. MailChimp, Sitecore, Adwords, BlueKai DMP, NCR POS)
|no code interface
||users can set up marketing campaigns by filling out forms or drawing a flow chart; no writing of code or script
||one campaign can include a sequence of messages over time
||one campaign can include messages in different channels
||the system can track customers through user-defined journey stages or states; campaigns can be triggered when a customer changes state; state is available to use in campaign rules
||campaigns can be set to execute on a regular schedule e.g. daily, hourly, weekly
||campaigns can be set to execute when a trigger event occurs; triggers may be recognized immediately or at a given interval (e.g. nightly)
|real time interact
||campaigns can react in real time to customer behaviors
|rules select messages
||rules within a campaign can select messages (i.e., different customers get different messages depending on the rules)
|scores in rules
||rules within a campaign can use predicitve model scores or other modeled values (e.g. cluster membership) as inputs
||the system provides tools to define customer segments based on all data within the system
||the system provides tools to analyze all data within the system, such as cross tabs, profiles, visualization, etc.
||the system provides tools to identify an ideal customer profile
||the system provides tools for skilled users to create predictive models
||the system provides tools for unskilled users to create predictive models
||the system provides tools to generate product recommendations (best choice among many)
||the system provides tools to calculate the incremental value created by any single marketing action
||the system lets users apply templates to create content without coding in HTML, etc.
||the system provides a repository to store and access content to use in marketing messages
||the system includes workflow functions for content planning and approvals
||the system can create content that is used across multiple channels
||the system can create messages that contain rules to select content based on customer data and other variables (time of day, weather, inventory, etc.)
||the system can automatically execute tests that find the best content for a given campaign and deploy that content. Tests might compare complete pieces of content, different combinations of content components, and/or different content for different customer segments.
|auto-classify (NLP, video)
||the system can automatically tag content based on subject, tone, images, brand, etc.
||the system can track budgets and spending against budgets for marketing campaigns and other marketing expenses
||the system can track tasks to develop marketing campaigns, including due dates, status, resources assigned, approvals, etc.
||the system can track marketing plans including campaign schedules and expected results
||the system can simulate the results of a planned set of marketing campaigns over time
|optimal spend by channel
||the system can develop an optimal marketing plan including allocation of spend across different channels and campaign types
||the system can ingest structured data such as CSV files and relational database tables
|semi- & un- structured
||the system can ingest unstructured and semi-structured data such as text comments and Web logs; specific requirements include nested JSON objects
||users can set up a data feed into the system without writing program code
||the system can handle high volumes of input data (we need specific criteria)
||the system can ingest data via batch feeds such as CSV files
||the system can ingest data via real time feeds such as API calls
||the system can ingest data via data streams (we need specific criteria)
||the system can ingest data via API calls, not necessarily in real time
||the system can ingest copies of a complete data set and identify and store only changes since the previous version of that data set
||the system can store inputs without losing any details
|multi-table data model
||the system can store data that is organized into multiple tables or data types
||the system can automatically accommodate new attributes in input data (without manual adjustments to the data model)
||the system can manage personally identified information (including compliance with privacy regulations for access and security)
||the system can store data in-memory (for fast access and processing)
||the system can automatically accommodate changes in demand through features such as dynamic load balancing, addition of new processors and storage, etc.
|industry data models
||the vendor has prebuilt industry-specific data models (for retail, travel, financial services, communications, etc.)
|B2B data model
||the system has a prebuilt B2B data model with separate account and individual layers
||the system can maintain relationships among personal identifiers that are deterministically matched to the same individual
||the system can match offline data (names and addresses) with features including name and address standardization and similar-string matching
|cross device match
||the system can maintain relationships among devices that are probabilistically matched based on usage patterns
||the system can identify devices over time using cookies, device fingerprints, and other methods
||the system can assign each individual an ID that remains the same despite changes in identifiers (new address, new device, etc.)
|lead to account
||the system has features to match individuals (business leads or contacts) to business accounts (companies or departments).
|anonymous to known
||the system has features to retain identity links when an anonymous profile is linked to an identified cusstomer profile
||the system can select the most likely value for personal data (name, address, etc.) and expose it to other systems
||the system has prebuilt integrations with location data providers (data about venues e.g. store traffic; possibly data about individuals e.g. types of stores visited)
||the system has prebuilt integrations with intent data providers (data about topics or products individuals are interested in)
||the system has prebuilt integrations with personal data providers (data about individual name, address, phone number, income, education, interests, etc.)
||the sytems has prebuilt integrations with postal and address data providers (data about valid postal addresses and address changes)
||the system can connect in real time with external systems to look up specified data (local weather, recent behaviors, etc.)
||the system has prebuilt integrations with B2B data providers (data about companies and individuals e.g. company revenue, CIO name)
||the system can extract structured data from unstructured inputs (e.g., products mentioned in social media comment, products viewed from Web log)
|external device graph
||the system has prebuilt integrations with external vendors (e.g. TapAd, Crosswise, Google) who provide a list of devices associated with specific individuals
|external ID graph
||the system has prebuilt integrations with external vendors (e.g. LiveRamp) who provide a list of identifiers (in different channels) associated with specific individuals
||the system can generate batch extract files to share with other systems
||the system has an API that can be called to retrieve data it contains
||the system can accept SQL or related queries and return the results to external systems
|analytical data sets
||the system can create extract files in formats suitable for analytics (e.g., containing aggregates or with multiple tables flattened to a single row)
||the system has prebuilt connectors for specific external systems e.g. BI tools, predictive modeling tools, etc.
As you examine the second table, you’ll notice that the Delivery section shows one definition for each row, while the Decision and Data sections show one definition for each box on each row. I think you’ll see that makes sense. But I’ve also allowed a few exceptions in the Delivery section, such as “Web” under “manage connectors”, where I’ve listed specific technical criteria that apply to a single channel. This is simply an efficient way to present that information.
Over-all, I think this approach brings us closer to a workable solution. No doubt there are more items that could be added, although too much detail would be bad. There’s also an objection that function checklists like this are easily misinterpreted to suggest that systems with more functions are better. That’s certainly a danger, but I’m hoping that marketers know enough about their needs to look only at the functions relevant to their situation. This approach does avoid creating a vendor typology, which was implicit in the previous approach and seemed likely to cause confusion.
The next step is to get feedback from the community about whether this approach seems reasonable and about specific items they’d add or remove from the map. After that, we’ll need to assess the functions provided by individual CDP vendors – a great deal of work but something that is probably inevitable. Once that’s done, we can publish a proper guide to the vendor systems, making it easier for marketers to find products that meet their needs.
What do you think?
November 27, 2017
If the Martech Stack Only Had a Brain...
By Amy Cross, NGDATA
Today, we are in the Age of the Customer, an age that differs from its predecessors. The balance of power has officially shifted from the business’ hands into the customers’, with customers increasingly empowered by the amount of information they have access to. Gone are the days of the Age of Manufacturing, where industrial powerhouses ruled; the Age of Distribution, when globally connected supply chains had the power, or the Age of Information, where connected PCs dominated. Today, understanding the customer and anticipating their needs is the way that brands must succeed to remain relevant.
During the Age of Information, the real accrual of customer data started to explode with the availability of first-party data, third-party data and transactional data. Thanks to connected devices and social media, we have seen this explosion in data magnify as we transition into the Age of the Customer.
Data are the breadcrumbs that customers leave behind for brands to learn how to better understand and anticipate their needs. While data itself is invaluable, it’s actually the customer insights that are buried within the data that are the most powerful. As customers interact with your brand, analysis is needed to unearth insights. From there, acting upon those insights is needed to reap any value.
Today, we have so many marketing and customer engagement tools and technologies, and a plethora of customer data. But, what is missing is the “brain.” It’s the central nervous system that talks to all the tools in the martech stack – the critical component that knows what to do and when to do it.
The brain has three central components: data, intelligence and orchestration.
It should know what to do for each customer based on all the data, and have the ability to coordinate across every engagement channel. When you “plug” the brain into your marketing stack, a lot of the guesswork of segments is removed. And, efficiency is increased as you truly target each customer on the individual-level.
So, how does the brain work? First, all available data about your customers and prospects are brought together in order to unlock silos. This comes from internal first-party data sources (CRM, web, customer service), as well as external data sources (second-party partnerships and third-party data providers). This then leads to a holistic, dynamic and living individual profile, or Customer DNA, for each and every customer. But, this isn’t simply all the data aggregated in one place; intelligence is added to that data. The brain needs to be smart enough to know, for each individual, what all that data means for them. Through metrics and propensities, the brain can tell you what each customer wants – how and when they want it.
Once we have the Customer DNA, the final piece is using that DNA to drive customer engagement. This is, of course, achieved through the various tools and technologies typically available in the martech environment. But, how do these tools talk to each other? How do they coordinate? That is where, once again, the brain comes into play.
The brain aggregates the data, has the intelligence to know what all that data means for each individual, and can orchestrate the engagement channels. Thanks to the brain, each customer can have the most seamless, relevant and personalized experiences with your brand.
For further interest:
November 20, 2017
Who Should Be Responsible for Digital Customer Engagement?
By George Corugedo, RedPoint Global
Digital technologies have turned the traditional relationship between brands and consumers on its head. No longer can brands be assured of controlling the customer journey from awareness to sale – consumers now direct their own buying journey, and expect brands to deliver a seamless experience in digital and physical touchpoints alike.
Many brands have not been able to deliver. Legacy technologies, customer data locked in silos, and inflexible traditional processes have hamstrung efforts to adapt for many organizations. McKinsey recently found that digital disruption has shaved 45 percent off incumbents’ revenue and 35 percent off their pre-tax earnings. McKinsey also reported that digital transformation has only currently reached mainstream penetration in one of 10 industries likely to be affected. More companies will need to alter their business models if they want to survive and thrive in the modern age.
Part of this transformation involves translating your brand promise into an engaging brand experience. Digital disruption provides this opportunity in spades; customers increasingly interact with brands through digital channels, which enables greater capacity and opportunity to translate the brand promise into brand experience. To do this effectively, however, you need to first determine who owns the brand experience.
At first thought, it may seem obvious. But keep in mind that enterprise customer engagement covers everything , including sales, service, marketing, and IoT. The brand experience is also everywhere and all the time, which is why it is vital to designate who in the organization has ultimate responsibility to ensure that it is consistent. Who has the right skills for the job? Should a new role be created or new responsibilities given to an existing employee? These questions must be answered if the organization is to succeed at digital customer engagement.
Customer Engagement and the Chief Digital Officer
One possible owner of the brand experience is the chief digital officer (CDO), who has taken on new organizational importance in the face of digital transformation. The CDO has become more strategically important in the wake of broad-based digital transformation, evolving beyond implementing new technologies or changing a few processes. The modern CDO or the equivalent – e.g., senior vice president of innovation – now directs and emphasizes the integration of digital thinking and processes throughout the organization.
The CDO’s role often includes deploying digital technologies and processes that enhance the customer experience. Because of this, it has become increasingly common for the CDO to be responsible for digital engagement. The RedPoint Global team and I have seen this happen frequently at our clients. CDOs and similar roles have wide-ranging mandates to spread digital throughout the organization, and they are often forward-thinking and cross-functional. Because the CDO works with every internal department, they are often a natural fit for managing the customer experience.
For all their technical expertise and understanding of digital systems, however, brand management is not typically a CDO’s core competency. This is a problem because the CDO needs to understand the intricacies of defining and delivering on a brand promise if he or she is to be responsible for the customer experience.
CMOs, CDPs, and the Brand Experience
The CMO is another possible owner for digital customer engagement. Marketing has historically owned the customer experience, and this shows in the marketing focus of most modern customer engagement platforms. The challenge most traditional marketers have is their mastery of technology and the related ability to leverage digital technology to extend the brand experience. This is the exact opposite problem of a CDO. CMOs understand the ins and outs of providing a consistent brand experience; in fact, brand management is one of the traditional responsibilities of a CMO. At issue is whether the CMO is skilled enough in advanced digital systems and process to leverage those new tools effectively. Given these limitations, should the CMO be responsible for delivering the brand experience in a digital world?
Take customer data platforms (CDPs) for example. CDPs are a foundational technology for modern customer engagement, enabling an always-on, always-processing golden record that facilitates a unified and complete view of the customer available at low latency across the enterprise. CDPs are a powerful technology solution to the problem of fragmented technology stacks and siloed customer data that have bedeviled brands’ efforts to provide a consistent customer experience across channels.
Industry analyst David Raab, who has written extensively about CDPs, has consistently defined the solution as “marketer-controlled.” This idea has carried through to other industry analysts as well, with a recent Gartner report placing customer data platforms in the marketing camp. But should marketing really be tasked with owning customer data and thus being responsible for the brand experience? If they are, the CMO needs to have support and authority from other senior staff to pull everyone in the same direction.
Support Your Local Brand Experience Owner
The brand experience is too vital to your success in the age of digital transformation to be without a leader. Someone within the organization must be responsible for driving digital customer engagement and for ensuring the brand experience is consistent across touchpoints. The person who is responsible for doing so must have the capabilities to leverage digital technology and understand enough of the nuances of brand management to be effective. If it’s your CMO, then he or she needs to comprehend the value and function of new digital technologies; if it’s your CDO, then he or she needs to be comfortable with the necessities of maintaining a consistent brand.
Your customers have already shifted to experiencing your brand through digital channels. Now the onus is on you to follow along, ensuring that your organization has the right tools and the right people in place to execute at the highest level. Whether that is a new role or an existing person, it is critically important to meet your customers where they are with a consistent brand promise to succeed in an age of digital disruption.
November 16, 2017
Foreign Exposure: Reaching the Unreachable Customers
By Moshe Dimri, Optimove
The countless ways the world has changed over recent years totally modified customers’’ habits, making them very hard to influence. But the same changes in our world make it impossible for our consumers to be completely anonymous.
It’s just impossible to keep track. The changes are so vast, and it’s affecting every aspect of our daily lives. It covers all fields and industries: Manufacturing, Medicine, Agriculture, Automotive, Corporate Services, Energy efficiency or Digital Economy. The use of deep learning technologies, big data analysis, cloud development, cyber security and many more, has brought on a dazzling number of inventions into the very tips of our fingers, and nothing will look the same again.
And the rhythm is gonna get you. Every year tops and doubles its former. Since Deep Blue beat Garry Kasparov 20 years ago, the world saw an explosion of ideas that touched and changed our habits, day-to-day routines, caused rifts in our attention and behaviors until the point of no return. From the autonomous car to shoes that tied themselves, from the bionic leg and artificial skin to smart umbrellas. This list is just a drop in the bucket, and what will be added to it in just a few months, is too hard to even imagine.
One of the more expected result of this era is the rapid change in customer habits. What was done on Sunday become outdated on Monday, and where people went during the summer was completely deserted when winter arrived. One of the clear demonstrations for this phenomenon is this startling study by Omnicom Media Group Agency Hearts and Science: 47% of adults aged 22 to 45 are consuming absolutely no content via traditional TV platforms. Less than one-third of the TV and video watched by people in this age group (Gen X and millennials) is accounted for by traditional measurements. The rest is being watched on smartphones and other channels like Apple TV and Roku.
While it doesn’t mean that these subjects are indisputably not watching TV content or seeing ads anymore, it does suggest the ways to consume content today have changed, which makes it much harder to reach these exact group. That’s a big problem for marketers these days.
Anonymous? No such thing
Look at it as you will - this is not a philosophical argument, for now – there are ways to connect with the most aloof of a customer. Whether it’s scratching the hard surface layer with a chisel to expose the hieroglyphics or scanning the depths of an ocean for the remains of a hunted ship. If Bigfoot does exist, and you’re probing for his footprint in the Cascades’ snow, look no further – just open your laptop. He would have probably left a digital footprint by now.
Customers can no longer be anonymous. And a more personalized approach through multi-channels, which work across every stage of each customer’s journey, will maximize the marketer’s ability to reach those who seems unreachable. Marketers should reach their customers where they are, and much of users’ screen time is spent on Facebook, Twitter or surfing the Web. The fact that you have an email address for a customer, doesn’t mean much if that same customer spends hours every day on Twitter. Customers are everywhere, and you should always aim to be where your customers are. In addition, customers today have much more control over the buying process than marketers do. As a result, marketers must constantly develop and coordinate highly orchestrated touch points and micro-campaigns that span multiple channels fluidly, in a way that the customer finds meaningful and trustworthy. Keep in mind that different types of messages work better over different channel, and what fits an email, shouldn’t automatically be sent using SMS.
The Mix and Match
Here are some ways marketers can use multi-channel marketing to improve campaign results. There are countless others of course, and that is where creativity and resourcefulness come into play.
Sequential Messaging Across Channels – Run campaigns that present a sequence of messages, like telling a story across multiple channels. For example, we can use the 97% open rate of SMS messages to deliver a short teaser regarding a subsequent campaign message to come via email, and thus dramatically increase the open rates of that email when it comes half an hour later.
Message Blast – Send the same offer via every channel at once. This works best for a great, time-limited offer that is highly focused to a small segment of customers.
Channel Competition – Run campaigns with the same message/call to action via different channels, to different random segments of a target group to see which ones work best. Known as A/B/N testing, this approach can reveal which types of messages work best over which channels for your various target segments.
The Pros and Cons
Using all available channels doesn’t come without a price. In the table below you can see the relative strengths and weaknesses of some commonly-used channels, according to these attributes:
Cost – how expensive is it to communicate with each customer via this channel?
Attention – how effectively does this channel grab the attention of most customers?
Design – how much flexibility and creativity is available to deliver effective messages via this channel?
Annoying – how invasive or irritating do customers perceive this channel to be?
In the ecosystem described in this article, the-ever-changing-mind-blowing era, marketers are required to closely analyze customer behavior, loyalty and retention. They need to communicate with their customers wisely, in all sorts of ways and tools according to his or her particular preferences.
Using different, less traditional channels, and more of them combined, is really the right path to success. Examining how people are more receptive to different sorts of communications, not only guarantees leaving no customer behind, but also gives tremendous added value to the customer, with content, offers, and messages delivered to where they're at.
November 14, 2017
Mobile Customer Recognition – the Third Estate of Customer Identification
By Matthew Kelleher, RedEye
In preparation for a recent webinar on the growth of the Customer Data Platform I was struggling to fully articulate the value of one key part of it. Although it’s only recently begun to create a conversation in the market place, we’ve been discussing its merits for the last seven years. At RedEye, we call it Cross Device Identification. Simply put, it is the use of cutting edge customer identification solutions to allow organisations to recognise mobile browsers through their devices, personalise messages to them and retain that valuable behavioural data within a true Single Customer View database.
Now, every decade or so I have a singular thought, one that surprises me and sticks in my mind for some time. This decade’s thought waited to appear until mid-way through the seventh year of the 2010’s. “It’s the third Estate” was my thought, but although this originated in my own brain, it took some deciphering to work out what I meant.
The point I wanted to make in the webinar was that, in building a true single customer view, many people consider that there are only, when you boil it down, two types of data – online and offline. I think this is, if not wrong, then significantly misleading. If you read the database marketing press (and they should know, right?) you see them talking about online and offline data. The point I wanted to make is that this is far too simplistic, that there is a third ‘type’ of data that many organisations who build customer and marketing databases and claim to build single customer views do not accommodate – mobile data.
I do accept at this stage that we could boil data down to dozens of sources that would differ for every organisation out there, but that is not the point I want to make. I also accept that many of you will consider mobile data to still be online data, but hear me out… Because of the challenge in identifying mobile browsers, because of the specific requirement to ‘stitch’ together the various elements of the online customer profile through the use of a multitude of different identifiers, because of the unique challenge and opportunity this presents, then we need to think about it as a third area of data.
But before I expand my point I should probably explain the analogy, and I have to start with an admission… I really could not remember what the third Estate was and so I resorted to Google to remind me what I had learnt at university! Of course, as most of us know (and have forgotten) in pre-revolutionary France there were two ‘Estates’, also known as ‘Estates of the Realm’, the forces that governed society. These were the Clergy and Nobility. Then a chap called Sieyès argued that real power actually lies in the third estate – the people.
The analogy that my mind was unwittingly stumbling towards was that traditional database marketing was built on an edifice that one can liken to the original Two Estates. The clergy, in my analogy, was the offline data, built around a household de-duplication structure that bizarrely decrees that without a domestic address you don’t exist as a prospect or a customer… In the world of online marketing this is a seriously flawed perspective. Bolted uncomfortably on to the clergy in the governance of post-medieval French society was the aristocracy (for examples of the uncomfortable relationship look no further than Henry VIII and the dissolution of the monasteries – but I am in danger of digressing too far). Here my analogy is that traditional database marketing ‘de-dupe’ or customer identification solutions have tried to work by bolting an email address to a household record – another uncomfortable and ultimately failing alliance!
Well, we all know what happened to the concept of the governance of the Two Estates in France and subsequently around the world… (a few clues, Robespierre, the guillotine, the Declaration of the Rights of Man). And this is where I must bring my rambling analogy back to the point.
The current multi-channel marketing automation landscape cannot be optimised by organisations who continue to rely on this dangerously outmoded form of customer identification at the core of their SCV or marketing database. The concept that a customer’s (or indeed prospect’s) household address should be the primary key (with email bolted onto it) is no longer relevant. This type of approach simply cannot deal with the wealth and variety of online, mobile and digital data that makes up the real potential asset at your fingertips – your customer data.
I want to expand this argument to cover three areas that are important to me. Firstly, the core inaccuracy of traditional database marketing customer identification (‘de-dupe’) techniques, secondly that building marketing solutions in 2017 focusing on historical data only is no longer fit for purpose, and thirdly that the real value in data for the future lies in that ‘Third Estate’.
Taking the first point, the inherent inaccuracy of a household based de-duplication. Systems based on such a limited number of identifiers could never efficiently differentiate between Joanne Smith, Jo Smith, J Smith and that remains the case today. Twenty years ago, marketers didn’t really care, you could just send the catalogue to that address and hope someone called Smith opened it. The solution, these organisations then went on to claim, was to bolt an email address to the household address. But multiple email addresses piled on top of inaccurate household data only confuses the situation further and whilst email is a key for a lot of online activity it is often poorly leveraged and does not open the door to mobile recognition, for instance.
The second point I would like to make in support of up-to-date customer identification solutions, is that the latest analytics are based on current data, i.e. how customers are engaging with your brand today, not what they bought two years ago. Historical data has its place… For instance, to calculate lifetime value one obviously needs historical transactional information. But current trends, for instance, towards predictive and machine learning, are based on the application of current behavioural patterns allied to historical data (putting it simply…). If your single customer view cannot track your customers mobile browsing activity (or indeed any browsing and engagement information from mobile to app) then you will never be able to leverage predictive analytics. I come back to the example of lifetime value. Traditional marketing databases that manage historical transactional information at a household level can give you a lifetime value calculation, probably down at segment level. But what they cannot do is forecast future lifetime value, at a business, segment or customer level, or drive insight in terms of the current behaviours that could influence an increase in that LTV.
And finally, I have to ask the salient question. If customers are increasingly active online (ComScore, for instance, say that 68% of all browsing is done on a mobile device), and if you agree that behavioural and engagement data is critical to customer understanding and up-to-date analytics as I do, then single customer views that do not incorporate this ‘Third Estate’ of data are increasingly anachronistic… just as the clergy and aristocracy were in the governance of France in the late 18th Century! It’s time for the guillotine…
You can get in contact with RedEye if you have any queries.
November 10, 2017
The Good Ol’ Campaign: Birthdays Still Matter
By Omer Liss, Optimove
Birthday emails are one of the most-common tricks up a marketer’s sleeve. New Optimove research probes just how well they still perform.
Blow out the candles, cut the cake, and go straight to your inbox to open your email. The birthday email is one of the best-performing personalized marketing campaigns, and has been for years. On that special day, this gesture strikes us all at our core — though few willingly admit — all we want is just to be remembered.
But is the birthday email still relevant? A recent research done by Optimove gathered new data that showed the birthday email performs better than ever. Direct emails tied to birthdays get the highest response rates, compared to many other customer-related communications. The data was received from over 1 million birthday campaigns sent by businesses since mid-2016, and whether consumers received coupons, free delivery, a gift or else, they all revealed their effectiveness. the new data was gathered from over 6 million customers in the gaming industry and more than 1.2 million from the e-commerce field.
Across the Sectors
The first thing worth mentioning is that birthday campaigns are statistically significant, and when compared to a control group (people who did not receive a birthday campaign/discount), the campaign significantly affected those who received a shout out.
The chart below shows the response rate for test and control groups (the test receives the campaign with a discount and the control doesn’t), and the percentage in which the test group is greater than the control.
First, we separated gaming and ee-commerce. Although there was a higher response rate in gaming, we can see clearly how well birthday campaigns perform in the e-commerce sector, as well.
After recognizing that birthday campaigns have a powerful impact on customer behavior, we wanted to examine how different groups react to these campaigns. When we implemented gender segmentation, we found that in both gaming and e-comm, men are more active on their birthday than women. The numbers in the chart show the exact percentage of men who were more active (new word) on their birthdays in contrast to women from those groups.
We can see from the chart that in gaming, men are 57% more active than women, and in e-commerce, they’re 72% more active (new word) than the women.
Men of a Certain Age
Our next segmentation is according to age. Here, gaming and e-commerce perform differently. As seen in the charts, there is a clear trend in gaming; the older the participant, the more activity on their birthday (to put it simply, people in higher age groups are more active than their younger counterparts). In e-commerce, however, that is not the case. The most active groups are the youngest, and then activity drops to the lowest point and raises slightly as age increases.
In gaming, the trend is consistent. Men order more than women in all age groups, and the trend stays the same (more active as age goes up). In e-commerce, the trend is similar, but we see that in some age groups, women order more than men.
Attempting to explain these numbers is more of a behavioral thing. Young people tend to be more involved on their birthday, and therefore, are less probably to play online casino games, but will take the time to treat themselves and place an order.
To sum it up: putting age, gender and sector aside, overall, people respond well to birthday campaigns. In this ever-changing world of marketing where marketers must be agile and adapt quickly, it’s important to tailor these campaigns to customers’ expectations, all while utilizing technology to reach specific targets.
November 7, 2017
CDP Institute Marks Its First Anniversary
By Davidi Raab, CDP Institute
Last week marked the first anniversary of the CDP Institute, which officially launched on October 31, 2016. Here are a few key statistics:
- Web traffic: 58,000 page views by 29,000 unique visitors
- Library papers downloaded: 3,600 including 2,500 Sponsor papers
- Members receiving daily newsletter: 1,600
It’s been quite fascinating to see which Library papers, newsletter entries, and blog posts get the most readership. Among Library papers, there has been a clear preference for introductory topics including industry overviews, advice on choosing a CDP, and the ever-popular CDP vs DMP distinction. There's less interest in case studies (a bit surprising) and vendor profiles. The best read blog posts have also been about CDP basics and differentiators.
Newsletter readers are perhaps more likely to be industry insiders: they’ve been most likely to click on items about CDP vendors, including funding, new entrants, and product updates. They’re also interested in surveys about personalization and other marketing practices. They’re less interested in news about data sources, privacy, and the Facebook/Google duopoly. But we're convinced those are important so we'll continue to cover them anyway.
None of this would be possible without our Sponsors, who have generously funded the Institute and offered much valuable behind-the-scenes advice. They’ve also been remarkably tolerant of published opinions that don’t align with their individual business needs. So here’s a big thanks to them.
Still, the real constituents of the Institute are you, the marketers and martech professionals who work every day to build better experiences for your customers. The Institute’s mission is to help you succeed by providing information about CDPs and customer data management in general. We’re constantly asking ourselves what more we can do to be useful.
But that's really a question we should be asking to you. So please take a moment and comment below or send your thoughts on what you like and don’t like about the Institute and what else we should be doing. We look forward to hearing from you!
November 6, 2017
CDP & GDPR – A Match Made in Scrabble Heaven
By Steve McGrath, RedEye
There are already a number of different pieces of UK legislation that cover data protection and data security. The predominant one currently is the Data Protection Act 1998 (DPA). We also have the Privacy & Electronic Communications Regulations (PECR… no sniggering at the back!), as well as others that cover individuals' privacy and data protection. And unless you have been living under a rock or are perhaps, a contestant on Love Island, you will also know that we have the General Data Protection Regulations (GDPR) coming into force in May 2018.
Now if you add all these acronyms up, not only can you get a pretty decent Scrabble score (my best was 6 letters – read to the end for my word), but you also have rather a lot to think about when it comes to data security.
A quick re-cap if you don’t have your GDPR notes in front of you; from May 2018 individuals about whom you hold personal data will have the following rights:
- Rectification (… you’ve got to fix it if it’s wrong!)
- Erasure (no, not the right to listen to 80’s pop)
- To restrict processing
- And the right to restrict profiling
All of which needs clarification by the powers that be over the next 11 months but one thing remains certain… your data platforms need to be able to achieve all of the above.
Wouldn’t it be great if you had somewhere you could put all your customer data that would address many of the regulations, existing and future, allowing you more time to get on with using that data for some brilliant marketing?
Drum roll please….
It’s called a Customer Data Platform (or CDP).
Now, you may be saying to yourself ‘Enough with the acronyms! I am still trying to work out what 6-letter word you had earlier in this article’! But hear me out, and I’ll explain how a CDP can help you, particularly in regards to some of the upcoming regulations in the GDPR.
I am not going to explain the benefit of a CDP from a marketing point of view. This is done far better in other articles on this site. What I will say is that a CDP is an evolution of a traditional customer database that combines data from multiple platforms to give a single view of the customer. It utilises profile, transactional and past behavioural data to predict future customer behaviour, all the while being marketer controlled and easy for external systems to push data in and pull data out from. It is intelligence driven marketing at its very best.
How does a CDP help with GDPR?
On top of this, there are some added benefits from a compliance point of view, particularly with GDPR in mind.
Subject Access Requests
Currently the DPA legislates for Subject Access Requests (the ability for a person to request all data from you that you hold on them). However, the GDPR will remove the ability to charge for this access and limit the time you are allowed to provide this data (a maximum of 1 month).
Having all your customer data in one place makes it more efficient to extract. You won’t need to engage your tech team to trawl though multiple data silos and you won’t need to spend hours collating all that information and putting it in a presentable format. If even 0.5% of your current customers asked for all the data that you hold on them, how much time and resource do you think that would currently take you to comply?
One of the main tenets of GPDR is more informed consent, but not only does it deal with whether you have gathered permission, it is also concerned with how you have gathered it. Not only that but the onus is on you to store and record how consent has been gathered. A CDP will store all this information and tie it all to the individual so it can be easily evidenced.
Linking back to the storing of consent, a good CDP will also store a complete audit trail of marketing permission, so a single customer state of consent can be shown at any given moment in their life cycle.
Using a CDP will mean all your subscription status’ and suppressions will be consistent and up to date across all of your customers.
In addition, the CDP will maintain a ‘single source of truth’ of your marketing consent and will make it easy for your other systems to access this ‘truth’.
Another facet of a good CDP is controlling access to stored customer data, particularly PII – Personally Identifiable Information. A platform like RedEye’s CDP can be accessed by external systems but only with the correct security privileges for access or secure methods for transfer such as SFTP. This is especially useful for areas of legislation that cover data protection and data security, meaning you will be mitigating your risk of a data breach.
Under the GDPR, breaches of some of the provisions could lead to increased fines of up to €20m or 4% of global annual turnover, so it is essential that you are doing all you can to mitigate your risk. At the same time, you are building trust with your customers by collecting, processing, storing and using their data in a transparent, respectful and legal way. A good CDP will help with many of these issues.
Finally, the 6-letter word was ‘carped’. Can you do better?
PS – we know there’s a potential 7-letter word, but we’re concerned we’d offend your delicate sensibilities with that one!
Questions or concerns over GDPR? Contact us now and we will try to answer your questions.
November 2, 2017
Getting Closer to Classifying CDP Systems
By David Raab, CDP Institute
It’s nearly two months since my last post on building a classification scheme for CDP vendors. Work has continued behind the scenes. I still don’t have a final solution but have made enough progress to warrant an update.
Where we currently stand is several of the CDP vendors have provided lists of technical features they see as important differentiators. I’ve reviewed these and classified them based on the core CDP functions they support. These functions fall into two categories:
- building the unified customer database itself (ingestion, transformation and identity unification, storage, and sharing with external systems). All CDPs do this.
- applications that use the database (analytics, campaign management, and personalization). Many CDPs offer one or more of these but it's not a core requirement.
Looking at the specific differentiators, it turns out that about half of them apply to nearly all customers who might want a CDP. I've labeled those "base". The rest relate to specific channels or uses. I’ve identified these as:
- semi-structured and unstructured data
- real time processing
- Web marketing
- mobile marketing
- B2B marketing
- offline marketing
I’m perfectly aware that these categories overlap: B2B marketers use the Web, Web market involves unstructured data, and so on. But each category is something that some marketers might use and others might not. Since the ultimate goal of this exercise is to help marketers find CDPs that meet their particular needs, breaking the features into categories related to those needs seems reasonable.
Naturally, two sets of dimensions (functions and uses) lends itself to a matrix. In practice, though, such a matrix would have many blank cells because so many items fall into the “base” use case. So to make the presentation a bit more manageable, I’ve presented two tables below: one that lists the base items only and one that has each of the other uses in a column of its own. In both cases, the rows are grouped by function. (To be clear, there’s no particular relationship among items on the same row in different columns. That said, it might make sense to have one row list the connectors appropriate for each use: B2B systems should have connectors to Salesforce.com; mobile systems should have connectors to SMS gateways, advertising systems should have connectors to DMPs, etc. )
If you read the entries carefully, you’ll see that I’ve marked several with questions, mostly about whether they’re too vague. Ideally the items would be specific enough that we could easily determine whether or not a particular vendor offers that item or not. So things like “has API” are too general, since APIs differ greatly. Tightening up these definitions, and adding more items as needed, is the next step in this process.
As always, I eagerly await your comments and suggestions.
(Note: tables converted from Excel using Tabelizer. Worth knowing about!)
||API load (specify features)
|batch file load
|client and server-side APIs for real time & batch connections;
|data exchange via webhook, data layer, pixes, firehose, CSV
|marketer can set up data collection without writing code
|prebuilt connectors (specify system, categories)
|extract input deltas (adds, changes, deletes)
|identify best value per element ('golden record')
|system-assigned customer ID (too vague?)
||data stores supported (specify)
|ingest and match 3rd party data (too vague? Not needed? Specify connectors?)
|modify data structure without technical skills (too vague?)
|open APIs to build custom features (need to specify API capabilities?)
|scalable tech (need specifics e.g. virtual servers? Dynamic load balancing?)
||API access to data (specify features?)
|prebuilt connectors to access data (specify channel? Functions?)
|SQL access to data (specify details e.g. speed, scale, all tables, SQL-like e.g. HQL?)
||Automated data normalization for predictive models
|Automated feature extraction for predictive models
|automated model building & deployment tools wihthin system (specify types? Features?)
|Automated model validation
|content analytics (specify details?)
|GUI interface for analysis, profiling, mapping, segmentation
|high throughput for batch recommendations (specify speed?)
|ideal customer profile
|Interpretable models (specify details)
|manual model building & deployment tools within system (specify skill level needed)
|marketer can define segments without writing code
|nature language processing (too vague?)
|product recommendations work with arbitrary catalog schema (too vague?)
|real time segmentation (specify speed, capabilities, GUI)
|system-generated incremental attribution models
|user-selected fractional attribution models
||automated segment delivery (specify features? GUI?)
|campaigns decision tree built with GUI interface (specify minimum features e.g. branches?)
|campaigns with multiple channels in same campaign
|campaigns with multiple waves in same campaign
|predictive model-based message selection
|real time trigger campaigns
|rule-based message selection
||personalize content (specify how e.g. variable substitution, rule-based dynamic content; specify channels)
||un- and semi-structured
||push to API to ingest continuous real-time data
||capture non-click behaviors (time spent, scorring, page tags, product categories, etc.)
||SDK for mobile load
|ingest nested JSON objects
||extract UTM parameters
||SDK: automatic collection of standard device attributes & location
||real time load (specify speed, features)
||SDK: batch data collection to save battery (vs streaming)
|convert unstructured to structured (feature extraction, etc.)
||unknown to known conversion (keep history)
||recognize devices and attach to customer ID
||lead to account match
||find name/address/company matches based on similarity
||postal address standardization & cleaning
|graph database (specify products?)
||account/contact data structure
|NoSQL database (specify?)
||query nested JSON fields
||20 millisecond latency on API calls for identification, etc.
||SDK connectors to access data
||API connectors to Google, Facebook, etc.
||real time access (specify speed, capabiliteis)
||SDK: push messages and in-app content to mobile
||continuously recompute audience assignments
||cookie synch w/DMP, DSP, etc.
||DMP functions (specify)
||ID synch with Google, Facebook, Liveramp
||templates let marketer build & deploy web experiences without writing HTML, CSS, JS
||include/exclude user segments for ad campaigns
||manage display, social audiences
||return recommendations in real time (specify speed?)
||personalize on anonymous users (based on device, campaign, referrer, location, weather, history, etc.)
||SDK for personalization (specify features)
October 24, 2017
Tealium Talks Customer Data Platforms with CDP Institute Founder David Raab
By Julie Graham, Tealium
Tealium recently sponsored a whitepaper authored by David Raab on “Customer Data Platforms: How They Work, What They Solve & Why Everyone Needs One.” I had the pleasure of sitting down with David Raab, Founder of the Customer Data Platform Institute (CDPI), to get an in-depth look at the Institute, his inspiration and what excites him most about where CDPs are headed in the future.
Tell us a little bit about the CDPI. Why was it founded and what solutions is it providing for martech professionals?
The CDPI grew out of discussions with several CDP vendors who were finding that CDPs met many clients' needs, but few companies knew that CDPs were an option. We decided to set up the Institute to increase awareness and understanding of CDPs as a category. Our theory was that this would help the individual vendors more than doing vendor-specific promotions. To that end, the Institute has focused on educating martech professionals about customer data management requirements in general, use cases for unified customer data, what CDPs are, and how CDPs differ from other types of customer data systems.
Why do you think it’s taken so long for CDPs to emerge into the space when the need for them has always been so large?
Historically, the only data available about most customers was their name and what they bought. It wasn’t until the Internet that marketers could capture detailed customer information outside of purchase transactions. The value of that data in predicting behavior is huge and it’s what triggered marketers to start thinking seriously about building comprehensive customer databases.
CDPs emerged to meet that need after marketers first learned how to gather data from source systems (think: web analytics) and then created custom systems to unify it (think: data warehouses). Only after they had built those custom systems could they then build standard software products to do the same thing, which is a CDP. The emergence of “big data” technologies and cloud-based systems helped too.
That’s the technology side. From the customer side, demand for better treatment emerged as consumers got used to personalization from leaders like Amazon. Once they realized that was possible, they felt every brand should do it, which pressured marketers to deliver.
Digital transformation is a business initiative that most brands are now striving for – how do CDPs help in driving that highly sought after digital transformation?
Digital transformation is an interesting use case for a CDP because it extends beyond marketing to the entire organization. Like personalized marketing, many digital transformation initiatives rely on a complete view of each customer. A CDP can deliver that and is designed from the start to make the data available to any system.
The CDP is a very logical centerpiece for many digital transformation initiatives. The biggest change… Click To Tweet
What excites you about where CDPs are headed in the future and the capabilities they’ll have? How do you see CDPs continuing to evolve and innovate in providing even more exceptional experiences for customers?
It’s a very dynamic category. We see vendors expanding their capabilities, sometimes to add features available in other systems and sometimes headed into new territory. Some of the newer frontiers on the horizon for CDPs are:
- Very fast real-time processing, which could let a CDP support real-time personalization and even replace a DMP for ad bidding (which needs response time under 30 milliseconds vs 1 second response for conventional real time interactions).
- Integrated machine learning and predictive analytics that will help guide customer treatments and other decisions.
- New technologies, including artificial intelligence, to process unstructured data with minimum human effort. Ex: Adding new data feeds becomes easier.
- Special purpose CDPs, such as systems for particular industries or small businesses. Specialization lets companies provide features that are especially suited to a particular set of users. This further speeds deployment, reduces cost and adds value.
Beyond new features, I’m excited to see more vendors entering the space, greater understanding of CDPs by marketers and technologists, and more recognition by other marketing technology vendors that CDPs can increase the value their own products provide to clients.
The General Data Protection Regulation (GDPR) goes into effect in just 9 months and businesses are starting to scramble to ensure their compliance. How can a CDP help a brand with data governance, privacy and security?
GDPR has specific requirements for privacy by design, transparency, presenting data to consumers, propagating data changes throughout an organization, documenting consent for specific uses of specific data elements, and tracing how data is used. All of those require centralized systems that can scour the organization for customer data, bring together all of the data related to each individual, and track data changes over time. And that’s exactly what CDPs are designed to do. Beyond all of that, CDP vendors are experts on customer data, so they should become critical resources as the GDPR deadline comes closer.
Thank you David!
Read the entire whitepaper here.
Wanting to learn more about how CDPs work, their key benefits and how they integrate with other solutions within a martech stack? Learn more here.
October 19, 2017
How to Prospect for Gold in The Wild West of Customer Data
By Laura Patterson, VisionEdge Marketing, Inc.
“Why go to California? In that ridge lies more gold than man ever dreamt of. There’s millions in it.” At least that’s what Dr. M. F. Stephenson said in a futile attempt to convince the miners to remain in Georgia rather than to flock to California to chase adventure and what might be an impossible dream. And we’re here to tell you there’s gold in your own backyard, within your current customer information.
Dr. Peter Fader, author of Customer Centricity, defines customer-centric marketing as looking at a customer’s lifetime value and focusing your marketing efforts on the high-value customer segment in order to drive profits. Customer-centric marketing requires placing the customer at the center of your marketing strategy in order to create and extract customer value. It is the essence of enabling marketing to serve as a value creator.
Being able to achieve this capability takes true prospecting tools that can uncover customer gold. With so much data available today and more being created every minute, being able to tell real gold from fool’s gold takes more than a discerning eye. We can take a page out of the gold prospector’s handbook in order to do this well. Successful prospectors test several streams or veins, make notes about the various types of minerals they catch in their sifters, and then evaluate which locations produce the best results. If nothing turns up or if the pieces are too small then they change locations or practices. If they find a good amount they hope it indicates the presence of a “motherlode.”
The practice of prospecting for gold applies to marketing as well. Imagine the power of being able to separate your worst customers from the gold nugget customers! This entails evaluating and understanding the value of both new and existing customers in terms of what it takes to attract, acquire, keep and grow their value. And in time, you would be able to determine which “vein” has the potential to yield the most gold. As a result, you can be more efficient in your prospecting process and more effective in hitting the “motherlode.”
Defining the Solid Gold Customer
Before you begin mining your data, you first you need to know what a solid gold customer looks like. This means you need to know who your ideal customer is. This process takes answering questions such as: What businesses are they in? What problem do they need to solve? What process do they take to identify solutions? What channels do they use?
If you don’t know the answers to these questions, you may need to do some research. You want to be able to create an accurate picture of your solid gold customer, their spending habits, what they care about, and what they need from you.
How to Pan for The Solid Gold Customer
Once you can characterize your ideal customer it’s time to start mining data so you can answer questions such as:
- Are there enough of these customer to pursue?
- Where are they and how do they find us?
- What are their roles, profiles, personas?
- When, how and why did they purchase from us?
- What touchpoints and content do they use?
Always Prospect with A Treasure Map
So you think you’ve found the motherlode! Now you need a plan to uncover your treasure map. . This means you will need to craft a plan that will help you
- Demonstrate you understand and solve their problem.
- Produce a coherent, tailored, relevant, and compelling content across all channels and touchpoints they use.
- Sync the content, channels, and touch points with their buying process.
- Ensure all customer facing parts of your company are on the same page.
Turning your raw data of information into actionable customer insights and mapping the customer buying journey takes both process and analytics. But it’s the only way to prospect for gold. Learn more about transforming data into insights and treasure mapping the buying journey. The gold rush is back and knowing where to starting panning will make all the difference.
October 17, 2017
Maximizing Data Investments With Identity Resolution Strategies
By Julie Graham, Tealium
Customer data onboarding is a practice that makes a crucial connection between offline and known data on a customer so it can be leveraged as online audience segments within a marketers strategy. Having a unified view of a consumer, across all devices, can help marketers in their pursuit of personalized and relevant omnichannel campaigns.
Onboarding with the ultimate goal of customer identity resolution isn’t a one-size-fits all effort though, so marketers truly need to familiarize themselves with the critical steps of the process to develop specifications to best match their own engagement strategies and applications. In thinking about the specific steps one first must understand the types of onboarding solutions available to a marketer.
Tealium recently licensed a July 2017 Forrester report, “Making the Most of Customer Data Onboarding” that breaks down these 2 primary types of customer data onboarding options:
1. Direct onboarding – brands can do this through any of the major media and social networks like Facebook, Google, etc. Direct onboarding works by taking the uploaded data and matching it to the audience within that specific type of record. The resulting audience segment is then used in targeted advertising and measurement within the platforms’ respective properties and networks.
For example with Facebook a brand would upload customer data where the matched records are added to Facebook Custom Audiences for deployment into Facebook, Instagram and the Facebook Audience Network.
2. Third-party onboarding – brands can do this through the services offered by third-party vendors they’re working with such as LiveRamp, Neustar and Throtle. Third party onboarding works in that these vendor suppliers collect audience data from a network of data providers, typically hundreds of publishers who have registered users associated with cookies and devices. The resulting audience segments are then distributed for activation as targeted ads.
Maximize the impact of onboarding investments and download the entire July 2017 Forrester report, “Making the Most of Customer Data Onboarding” today!
October 13, 2017
How to Use a Customer Data Platform to Increase Your Facebook Engagement
By Samuel Carter, Fospha
As of July 2017, Facebook had 1.32 billion users log on daily – arguably making it the most powerful social media platform for marketers today.
Marketers are aware they should be using Facebook, and taking advantage of this super platform, but not everyone is sure how best to use it to generate results, conversions and revenue. Indeed, simply having a large audience on a particular day is not enough for marketing success. If marketers aren’t showing potential customers what they want to see, there is little chance of these potentials becoming actual, revenue-generating customers.
So how can marketers ensure they are pushing the right message out to the right people? The key lies in harnessing the power of their data, and for that, a Customer Data Platform is key.
Here are Fospha’s top reasons why a Customer Data Platform should be used to supercharge your Facebook marketing.
1. Enriched data
Advertising through Facebook typically relies on individuals giving away pieces of information about themselves. However, the social media platform tends to only collect generic demographic details – such as gender, age and location. Whilst this can be used for basic clustering and targeting – say to find females between the age of 18 and 24 – successful Facebook targeting is contingent on delivering an interesting message to your target audience. And, unfortunately, in this example it’s highly unlikely all females aged 18-24 will be interested in the same advert.
So how can you improve this?
A Customer Data Platform gathers, integrates and centralises customer data from various sources to create a rich, single customer view. This provides granular detail of where customers are in their journey, as well as what channels, devices and content they have interacted with. When this data is integrated with Facebook, marketers are provided with a wealth of information about their customers – so you can further segment customers based on interests, engagement or purchasing intent.
2. Clustering customers
As touched upon, clustering in Facebook is normally contingent on very basic data. However, with the richer data that a Customer Data Platform provides, marketers are able to cluster potential customers on the basis of highly specific behaviours. For instance, "has only ever read blogs on Facebook". Marketers can additionally segment customers even further, by combining behavioural and demographic information – to create clusters such as "females ages 18-24, who engaged with 3 Facebook articles on your website, got to check out but dropped out, two weeks later they clicked on a retargeting banner but dropped out again". This way of clustering enables you to targeting segments of individuals based on both their engagement and intent, meaning you can personalise to every aspect of their journey.
A Customer Data Platform strengthens Facebook clusters in this manner, with clusters being created either through simple drag-and-drop, or through complex machine learning and AI technologies. In both instances, marketers can be sure they are targeting highly specific groups of individuals who are much more likely to respond to their ad and purchase something from their website.
3. Finding customers
Another important feature of Facebook is its ability to deliver adverts to ‘look-a-like’ audiences – that is to potential customers who share the same traits as existing, high value customers. Considering the fact that the Customer Data Platform enriches the clustering of existing customers, it is a logical extension that it will enrich the building of look-a-like audiences. Indeed, rather than the granular customer data being used to target existing customers, this data is fed into Facebook so that audiences are built from this richer context to target new potential high value customers.
4. Increase customer engagement
When you are targeting the right people, with the right content, at the right time, you increase the probability that they will engage and ultimately convert. As a result of the rich data it provides, a Customer Data Platform is therefore instrumental in increasing customer engagement with your Facebook adverts. For instance, for one of our clients, we saw a 25% increase in click through rates when re-targeting old customers who had not engaged with the brand in 6 months, through Facebook. Click here to learn more!
5. Reduce costs but increase revenue
With standard Facebook clustering and targeting, you may get potential customers clicking your ads – but that does not necessarily mean they will want to purchase your product and convert – a reflection of the fact that you are targeting a much wider, generic audience. However, when you take advantage of a Customer Data Platform to supercharge your clustering and audience building capabilities, you will begin to find that – even though you are serving to a much smaller, defined audience – the customers who are clicking are more likely to convert; a study by Marketo (2016) found that over 78% of consumers will only engage offers if they have been personalised to their previous engagements with a brand. So, although your overall clicks may be reduced, overall revenue will increase.
Clearly, Facebook is a marketing channel that is here to stay. And marketers should definitely be taking full advantage of the opportunities it offers. However, a Customer Data Platform is becoming key to making the most of this platform. If you would like more information on how Fospha integrate their Customer Data Platform with Facebook, click here.
October 11, 2017
A Primer To Channel Orchestration
By Subra Krishnan, Vizury
Channel orchestration is a technique by which marketers can intelligently sequence different marketing channels for a given segment of users to maximize both positive user experience and also user engagement.
Most marketing clouds/technologies offer features like “Journeys” etc. These allow the marketer to create a sequenced multi-channel campaign. E.g. you can start off by shooting an email to a segment, wait for 48 hours, look at respondents. And then to the remaining users, send an SMS. And so on.
This approach has one major limitation. It does not account for an individual user’s channel responses and preferences. And therefore pushes message at every level of the journey to a large number of users.
Let’s take the example of two bank customers.
Sam is active on the bank website. He visits the bank website multiple times every week to do various NetBanking transactions. Also, the bank has sent him at least 10 promotional emails in the last three months, none of which he has opened. Also, Sam is frequently present on Facebook, but has never clicked on a NewsFeed ad though the bank has shown multiple ads.
If the bank wants to send a message to Sam, the ideal channel sequence is: Try the bank Website. Then target him on Facebook. Email is the least preferred channel.
Anna, on the other hand, does not visit the bank website more than once a month. However, she has opened three of the 10 emails sent to her in the last three months. She has also signed in for the bank’s browser push notifications and has clicked each and every one of the three push notifications she has received. She is rarely on Facebook.
For Anna, the ideal channel sequence is Browser Push, then Email, then Website. Facebook is probably not a good idea and should be the last resort for Anna.
With user level orchestration, marketers can ensure that both Sam and Anna experience a unique journey most consistent with their channel responses. This has two major benefits.
- The most obvious benefit is in the form of increased engagement. By ensuring users get targeted across channels where they are most likely to engage in, marketers are getting the best engagement in the most efficient way possible. In fact, Vizury’s work with a Top 5 global bank showed that there is a 2X efficiency that can be achieved.
- As you dig deeper into orchestrated experiences vs. standard journeys, there are major cost benefits too. Traditionally adtech (paid media) and martech (owned media) have operated in silos. The same set of users could be getting targeted across both these channels for the same message. This creates a pretty large cost inefficiency. In the example above, neither Sam nor Anna actually need to be targeted on Facebook, which is a paid channel, till owned media channels are all used up. Orchestration allows you to intelligently sequence your paid media channels after free media channels.
Increasingly, the fragmentation that used to exist in the digital world of your users is vanishing. And as they do, channels become “dumb” end points. User data, as well as channel selection, becomes centralized. Algorithms overlaid on this central data platform can take intelligent decisions on orchestration. This is the next big frontier in marketing that you should be aware of and tap into.
And the best part? In the next blog post, I will delve into how you can organize your best of breed marketing software stack to achieve orchestration.
To know more about Channel Orchestration and its use cases relevant to your business, please feel free to reach out to me at firstname.lastname@example.org
October 5, 2017
Consumer Views on Data Privacy: What Brands Need To Know
By Julie Graham, Tealium
The European Union’s General Data Protection Regulation (GDPR) enforcement begins in May 2018 and organizations involved in the control and processing of personal data about EU citizens will need to review their strategy, policies and procedures to ensure compliance before that time. And GDPR isn’t just affecting businesses – EU consumers are becoming more aware of this new set of rights which they have been granted by GDPR.
DataIQ, in association with Tealium, recently surveyed consumers to better understand their perspective on data collection, consent, context and control to create an extensive report of the findings in the publication “General Data Protection Regulation 2017: Identifying the Impact on Marketers and the Consumer’s Moment of Truth.” We’ve compiled the key distinctions from the report to show brands how aware consumers are of the way data is collected from their digital footprint as well as their perspective and general attitude around it.
Findings on Sharing Personal Information
Consumer attitudes towards sharing their personal information have become significantly more positive:
- For every 1 person who says they prefer not to share (36%), there are 2 who are either happy to if the need is explained (42%) or are happy to share if they trust the company (21%)
- By contrast, 6 out of 10 are only vaguely aware (24%) or not aware at all (38%)
Key Findings on App-Tracking
- 4 out of 10 consumers (39.4%) say they would prefer not to be tracked online and by apps and avoid opting-in (as is their right under the ePrivacy Directive)
- Nearly 1 in 6 (17.2%) say they avoid sites and apps which they know are tracking them
Key Findings on Personalization
Consumers notice when their devices and the services they are accessing seem to reflect who they are:
- For nearly 2/3 (64.4%), it is awareness of their location which is most evident
- Consumers rate personalized services most highly which are based around convenience:
- Autofill (3 out of 5)
- Personalized offers (2.93)
- Personalized content (2.92)
- Interest-based content (2.91)
- Being recognized by the brand (2.91)
- Location-aware services scored lowest with a score of just 2.59
- Half of consumers (48.7%) adopt a rational attitude that personalization is ok if they have a choice
- However, negative feelings are expressed by a significant minority of 4 in 10 consumers, with 1 in 10 (10.2%) saying personalization feels creepy if taken too far, 15.5% feeling worried, but unable to do anything about it, and 17% maintaining that they dislike personalization.
Key Findings on Ad Blocking Software
- 1/3 (36%) of consumers already make use of ad blocking software and more than half (55%) are considering it
- However, while using a private browser window may have the same effect as ad blocking software, only one quarter of consumers (25%) currently make use of this option.
These key findings and the entire report show that consumers are more aware than ever about how their digital activity is leveraged to obtain data and how it is used to build tailored experiences from the brands they’re engaging with. Though a willingness to share data is on the rise (nearly ⅔ of all consumers are happy to share personal information), this willingness is dependent on a clear understanding of the purposes for which that data is being requested, the choices available to the consumer and/or their trust in the brand.
Take advantage of these consumer views and insights on data privacy and download the entire report “General Data Protection Regulation 2017: Identifying the Impact on Marketers and the Consumer’s Moment of Truth" today!
September 29, 2017
First Ever Customer Data Platform Summit at Shop.org 2017
By Karen Wood, AgilOne
This week, AgilOne hosted our Customer Data Platform Summit – the first conference of its kind focused entirely on Customer Data Platforms (CDPs) and how enterprise B2C brands can benefit from them. The action-packed half-day event was held at the Los Angeles Convention Center in partnership with Shop.org. Executives and practitioners from over 80 brands registered and attended the CDP Summit.
We were fortunate to have some of today’s hottest trendsetters and industry leaders presenting at the event. They shared inspiring success stories, innovative ideas, and tales from the trenches about managing customer data and leveraging it across systems of engagement. Our speaker roster included:
• David Raab, Founder, Customer Data Platform Institute
• Steve Miller, VP Marketing and Business Development, JOANN Stores
• Michelle Kelly, CEO, Lilly Pulitzer
• Carla Rummo, VP of Direct Marketing and Ecommerce, Serena & Lily
• Jeremy Muras, Digital Director, Lion Capital
• Julian Chu, Digital Marketing Executive, Castanea Partners
Highlights from the Sessions
The Customer Data Platform Summit featured four deep-dive presentations and a panel from a mix of speakers. Here’s an overview of each of these sessions.
The Rise of Customer Data Platforms for Enterprise Retailers, presented by David Raab, David shared his insights into the rapidly growing Customer Data Platform market, including how he sees the CDP market meeting omni-channel retailer needs, and where the market will evolve over the next year and beyond.
How to Implement a Customer Data Platform for Immediate and Long-Term Success, presented by Steve Miller Steve talked about the steps JOANN took toward choosing a CDP approach, selecting a CDP, and implementing a CDP that would deliver quick wins and future-proof JOANN for long-term success in stores and across digital channels.
Using Analytical Insights to Reveal Who Your Customers Really Are, presented by Carla Rummo Carla discussed how Serena & Lily used reporting to gain a comprehensive understanding of customers and to inform segmentation strategy.
Data-Driven Decisions and Organizational Mindset in an Omni-Channel World, presented by Michelle Kelly Michelle explored how data can be a change agent across the entire organization, and shared Lilly Pulitzer’s experience with leveraging insights from a Customer Data Platform to transform decision-making in many key areas of the business.
Interactive Panel: Customer-Centric Personalization for Retail Brands Michelle Kelly, Carla Rummo, Jeremy Muras, and Julian Chu discussed pressing issues pinpointed by the Customer Data Platform Institute, including: What is the best approach for managing customer data? How can marketing insights feed into the rest of the business? What is the best way to leverage data from stores?
AgilOne’s “Unsummit” – Roundtable Discussions for All Participants
One of the goals of the event was to facilitate meaningful conversations among the marketing executives and practitioners in attendance. The middle part of the day featured two back-to-back “Unsummit” roundtables. Here’s how it worked: Attendees were instructed to choose two topics of their choice from a list of five. Then, they were assigned to the roundtable matching their preferred topic, where a discussion leader had background information prepared and 5-10 discussion questions.
The room came alive with lively dialogue across all the tables – impassioned statements, pressing questions, productive conversations. Here were the five topics attendees discussed:
How First Party Customer Data Can Help with Acquisition/Adtech Use Cases Adtech systems are quickly becoming customer addressable, such as with Google Customer match, Facebook Custom Audiences, Criteo and DMPs, plus onboarding companies making first party data available. This allows marketers to targeting media using actual customers rather than broad demographics. Google claims Customer Match will be their biggest focus for 2017. For example, we found that using Facebook custom audiences reduces acquisition cost by 50%, using Criteo matching reduces media costs by 20%, and increases conversion rates by 2x. These are individual examples, but we'd love to learn from your experience or thinking.
Real-Time Use Cases Driven by Customer Data Platforms Real-Time is a buzzword in customer experience discussions. Depending on the context, some may be referring to near-real-time experiences also by this term. Let's discuss the real-time use cases that are driven by the Customer Data Platforms. What are the realistic use cases driven by your CDP, which are already implemented by you? Which use cases do you expect to be realistic in the next 6-12 months? Which use cases will be realistic in a longer-term time horizon? And how do you measure the effectiveness of these use cases?
Getting the Most from Store Promotions One of the best ways to build awareness of your brand, engage your existing customers and attract new business is through event marketing. But figuring out what kinds of events to host and how to organize them can be daunting. The challenge to planning and pulling off successful marketing events is to make sure they grow organically out of your business. You don't want to tack an event onto your brand without giving any consideration to what your brand means, who your customers are and why you're hosting the event in the first place.
Solving for Issues Today and Tomorrow 66% of retailers agree that results will continue to erode unless they find a way to incorporate technology as part of their store experience (Forrester). Close to 50% of sales start with mobile search/information gathering. 80% of Internet users have smartphones, 40% read their email on mobile. Mobile provides location data crucial to store/geo-based engagement marketing. How are you planning to solve for trends such as the Internet of Things and other future issues and use cases?
Creating Urgency Within Your Organization to Set up a Customer Data Platform One of the challenges in setting up a Customer Data Platform is getting your organization behind the initiative. A person seeing the value of a customer data platform needs to get support from members of your marketing team, executives of the company and IT staff. Once everyone is on board, a person needs to create urgency around a CDP project that will benefit the business. But customer data lives in many more places than just marketing. Companies struggle as a whole with aligning around customer data, centralizing data, and leveraging it for use cases that go beyond what marketers need.
Stay Tuned for More
Now that we have the first Customer Data Platform Summit under our belt, get ready for more! We plan to replicate this template in more cities to come.
September 19, 2017
Achieving True Cross-Channel Personalization: “Live” & “Push” Channels
By Vijay Chittoor, Blueshift
Customer Data Platforms are the key to achieving Cross-Channel Personalization and Orchestration. One of the key roles of CDPs is to help unify customer data from multiple silos, and create a living breathing customer profile. An equally important role is to enable marketers to “activate” that data, in the form of personalized campaigns across all channels.
As we think harder about this opportunity for true cross-channel personalization, we must keep in mind that marketing channels come in two flavors:
- “Push” Channels: This includes channels like email, direct mail, SMS and app-notifications. The interaction is asynchronous, where the customer may open or view the message at a different time than when it’s “served” or sent. The number of “sends” per customer is typically limited to a handful or less per day across all the messaging channels. Most Push Channels involve known customers, albeit with different identifiers (email addresses, device tokens, phone numbers or mailing addresses). The customer identifier remains largely static over time.
- “Live” Channels”: This includes owned channels like your website, in-app content, as well as paid channels like Display Advertising or Facebook. These channels are synchronous in their interaction, and the content is consumed as it’s being served. The volume of impressions on Live channels can be huge for highly interactive sites: customers might consume tens of page views in a single session. On owned Live channels, customers may go back and forth between between being known (logged in) and anonymous. The anonymous behavior could extend across multiple browsers, and cookies could change over time. On Paid Live Channels, identifiers have to be synced with 3rd parties who own the media inventory, or with middlemen in the ecosystem.
Traditional Barriers to Cross-Channel Personalization across Push & Live
There are some key differences in how these channels operate, creating barriers to true Cross-Channel personalization:
- Organizational: Typically, ownership of these channels is split between multiple teams. The Push Channels are the responsibility of a “CRM” team or equivalent. This team almost always belongs in the marketing organization. The Paid Live Channels are typically the responsibility of a Media Buying team, also within the marketing organization. However, the Owned Live Channels (websites, apps) are typically the responsibility of a Product team with some input from Marketing. ►Because of these organizational differences, teams typically end up using technologies with different views of customer data.
- Technology: Given the synchronous versus asynchronous nature of the channels, the different identifiers, as well as the different latency and throughput requirements, the technology required for these channels is often different. ►Because of these technology differences, legacy applications have typically served only one type of channels (either Live or Push), not both, further accentuating the silos.
The holy grail for cross-channel personalization is to place the customer at the center of the experience. In order to enable this, CDPs must not only unify customer data and customer identity, but also break the mold of legacy applications in being able to serve both Live and Push use cases.
A New Architecture for Cross-Channel Personalization
Unlike legacy applications, the key to achieving this holy grail is a micro-services based technology architecture that can both consume fast streams of data, as well as enable high velocity personalization. Each micro-service could then be optimized for its needs (e.g. low latency, high throughput) using a custom data store that best fits the needs. The micro-services would interact with each other to ensure that every element of customer experience is personalized using the full 360-degree view of customer data.
- The core cross-channel pieces of personalization, including “segmentation”, “dynamic content”, “content recommendations”, “measurement and attribution” each deserve their own micro-services optimized for the separate needs.
- The campaign execution framework would consist of a set of separate micro-services, optimized for Push and Live channels.
That is why we are excited to announce today that Blueshift’s CDP is adding support for Live Personalization in addition to Push Channels, enabled by a modern architecture. Blueshift customer Tradera, a division of Paypal, has seen significant advantages in using unified customer data & AI on websites & apps in addition to using it on Push marketing applications (email, mobile app notifications and SMS). “Blueshift has really changed how our marketing team as well as our entire company as a whole work - now we are able to create real value for our customers and business rapidly”, said Ferraud Norberg, head of marketing at Tradera.
Additionally, Tradera has seen substantial lift in revenue and conversion rates with Live Personalization, according to Norberg. Compared to previously deployed versions of manually recommended content, clicks per session was up 80%. Out of all sessions ending in a click on the recommended content, gross sales (GMV) per session was up by 125%.
Cross-channel personalization is no longer a pipe dream. Modern architectures and AI-Powered personalization make it a reality.
September 13, 2017
More on Classifying CDP Systems
By David Raab, CDP Institute
Last week’s post on classifying CDP systems yielded many insightful comments, both public and private. A few of the themes worth noting were:
- purpose of the exercise. Let’s start at the beginning: the goal of this exercise is to find a way to reduce confusion in the market about the many different types of CDPs. Both buyers and sellers suffer when marketers can’t easily figure out what different systems are good for, and thus which they should explore in detail as solutions to their particular needs. We're not trying to gather all the information a marketer needs to make a choice, but rather to help marketers quickly narrow the field of products they consider. If we don’t do that, the marketers will soon decide the CDP label doesn’t mean anything useful and the category as a whole will suffer. That would be a loss since CDPs do provide capabilities that marketers might not otherwise be able to find, either because CDPs would be lumped into still broader categories (e.g. “data management”) or be ignored in favor of less suitable solutions that are more clearly defined (“marketing clouds”).
- type of information. Because the purpose is to help marketers understand roughly which systems are good for which purposes, we need to keep the whole scheme relatively simple. Yes, it’s possible to rate each vendor against a 400-point checklist (in fact, the Evaluation Guides in the Institute Library contain just that). But such reports are not useful to marketers just starting their selection process. Instead, they need a handful of items that help them to quickly understand the vendor landscape before they dive into the details.
- technology vs applications. Should a classification scheme be based on system technology or on applications? There’s no question that marketers buy applications. Most neither know nor care what goes on under the hood. But suitability for use cases is an extremely subjective measure. Many systems can be stretched to support use cases they’re not really designed for. If one of our goals is to find a classification system that vendors are willing to apply (or let the Institute apply), we need something that much less open to debate. Specific technical features are objectively present or not, although there are still plenty of nuances. Technical features also tend to reflect a system’s original design goals, which are themselves usually applications. So there’s a broad correlation between technology and applications, which should be enough to guide buyers in the right directions. Moreover, a few critical technical features will correlate with many capabilities. So if we pick wisely, we should be able to keep the amount of information at an easily digestible level.
- vendors in multiple categories. There was some confusion among readers but my intent in the original post was that one vendor could belong to several categories. It still is. This could be true whether the attributes relate to technology or to applications: some technologies are compatible without being mutually required; similarly, some applications are independent of each other. More concretely, a system that’s good at resolving postal addresses might or might not also be good at cross-device matching. Each task uses technologies that are different but don’t conflict. Marketers who need one, the other, or both types of matching could look at systems with the right technologies to quickly find the ones that might meet their needs (or, more to the point, to exclude systems that don’t meet their needs). It’s true that vendors will want to be in as many categories as possible, even if they just marginally qualify. That’s where using relatively objective technical criteria makes things so much easier. So I think it’s a manageable problem.
So far so good, but this is just warming up to face the real question: what should the categories be? I proposed one set last week but didn’t consider it final. The feedback from that post included a good number of recommendations for other categories and ways of organizing the categories. I started from scratch by listing the items suggested and seeing what clusters felt natural. Three dimensions emerged these were:
- activity supported. Some items related to building the unified customer database itself, some to sharing the assembled data, some to analyzing it, and some to selecting customer treatments (a.k.a. campaign management, decisions, or orchestration). Those felt like distinct enough categories to be treated separately, even though there’s some overlap, especially between analysis and treatment selection. There were yet other items that related to vendors, such as pricing and services provided. Rather than lose them, I gave them a category of their own.
- data type. There were some items that clearly related to online data, such as capturing Web or mobile transactions. There were others that related to “big data”, such as use of NoSQL technology. A couple more related specifically to B2B or offline data. The rest seemed to apply to pretty much everyone so I not-very-creatively labeled them “general”.
- specificity. Remember that my strategy is to find specific items that can easily and objectively be judged as present or not. About half the items fell into this category, either inherently or because we could add specifics such as having prebuilt connectors to certain key systems.
A table with the results is at the end of this post. I’ve highlighted the different categories to make things a little easier to see. A few observations:
- nearly half the items relate to building the database. That’s good. After all, we're talking about Customer Data Platforms, so the differences in how they handle data is our main concern.
- Most of the variations in data type also fall into the data category. Again, that’s good news since it means we have a good chance of finding markers to identify the different types of systems.
- Nearly all the data build items are specific. That's great. The sharing category is also in pretty good shape. Analyze and treat are mostly general, but those are categories where not every CDP competes, so it should be easier to make broad judgements about who’s in. Vendor categories are really outside the scope of this exercise. Buyers can screen on them after they’ve figured out which systems meet their technical / functional needs.
- Excluding the vendor group, there are ten unique combinations of categories between the first two columns (build/B2B, build/big data, build/online, etc.). Those could become ten clusters. No cluster has more than eight line items and most have fewer, which is quite manageable. The total of 48 rows is a few more than I wanted but we could probably get rid of some. All very promising.
These clusters don’t match the ones I offered last week. That's perfectly fine. Nor do I think this is the optimal set. But I think it’s moving the discussion forward. The next step is to get more feedback from the CDP community (that’s you) and to start tightening up the definitions of the individual items so we can make the data collection and presentation as straightforward as possible.
I look forward to hearing your thoughts.
September 6, 2017
How to Classify CDP Vendors
by David Raab, CDP Institute
Gartner’s latest Hype Cycle (August 2017) showed Customer Data Platforms moving from early innovation to near the top, a.k.a. “peak of inflated expectations.” That’s good news in some ways, although it also implies the next step is a slide down the “trough of disillusionment”. Sadly, that is a reasonable expectation: CDP has now attracted enough attention that we can expect to hear about bad experiences from companies who bought a CDP without understanding what to expect or preparing properly for deployment. As the hype cycle makes clear by its very existence, this is a normal though unpleasant stage in a category’s development.
The Gartner report also noted the confusion created by the variety of vendors using the CDP label. This is something we worry about a lot at the CDP Institute. The context is often a suggestion that we tighten our definition of CDP to exclude vendors without particular capabilities.
The idea has its attractions: in particular, it would give CDP buyers a clearer idea of what to expect. But I’ve so far chosen to take a “big tent” approach of including any vendor who meets the basic CDP requirements (marketer-driven, unified persistent customer database, shareable data) and has CDP as its primary product. (That last constraint excludes agencies and multi-product software vendors.) Arguments in favor of the more inclusive policy include:
- a wide variety of CDP configurations lets CDPs meet a wide variety of user needs. This means it’s more likely that marketers can find a CDP that is right for them. It’s better for everyone if marketers enter the CDP department store and find the right booth, than if they search randomly through the entire shopping district.
- CDP vendors came from many backgrounds but are becoming more similar over time. This is a normal development in any software category, as companies learn from each other and from customer needs. It’s true there’s a limit to how much most products can change without being fundamentally reengineered (which rarely happens). But many of the features cited as missing from some CDP products can be added without a full rebuild.
- vendors will use whatever terms they please. If companies find the CDP label attracts a productive stream of prospects, they’ll call themselves CDPs whether or not the Institute agrees. Maybe we could create a “seal of approval” that would carry some weight (more on that in a minute) but I’ve no illusions about our ultimate market power.
- more Institute sponsors means more Institute resources. It would be foolish to deny that growing the Institute has some appeal on its own. But more resources also lets us do more research, speak at more events, publish more papers, and do other things that educate buyers and promote the category. We’ve purposely kept the sponsorship fees low so we could attract many sponsors rather than be dependent on a few. That means expansion depends in good part on adding sponsors.
Nevertheless, it’s still clear that the variety of CDP vendors does create confusion about the category. The Institute has spent much of the past year working to clarify how CDPs different from other types of software that manage customer data. We’ve summarized that in the following chart and Venn diagram, used in many presentations:
I think our next step is to work on establishing a similar understanding of differences between different types of CDPs. I’ve recently taken a tentative first step by defining clusters of features that belong to different subsets of the CDP universe. All CDPs support the first cluster, which is the core CDP definition itself. Many support at least one additional cluster and some support several. That means the clusters are more like building blocks than mutually exclusive categories. (In plainer terms: one CDP could fit several categories.)
You can also think of the clusters as checklist items – so once buyers know which vendors fit which clusters, they can match the clusters against their needs and have a relatively simple way to find the subset of CDPs that are worth exploring in detail. To continue our department store analogy, the clusters would be the signs pointing shoppers to the different departments. It’s still up to the buyers to look through the wares in each department to make a final choice.
It should still be obvious that vendors who qualify for a given cluster will still have major differences. And, of course, vendors would have every reason to claim they should be listed in a cluster as many clusters as possible. But it’s just possible that the CDP Institute carries enough authority that vendors would be willing to let us classify them – based on objective criteria, rigorously applied, of course. If we made agreeing to this a condition of Institute sponsorship, we might lose a few sponsors but I think the industry as a whole – including buyers – would be well served.
What do you think?
August 28, 2017
How a Customer Data Platform Can Unlock +30% ROI in Paid Search
By Sam Carter, Fospha
The customer journey is becoming increasingly complex – with customers using multiple devices, channels and sessions in their path to purchase. Often, however, information about a customers’ journey is siloed – meaning marketers can only see each of these interactions in isolation, and lack visibility on cross-channel cost and revenue. As a result, they lack the single customer view that is necessary to attribute revenue (and cost) to each step in the customer journey, and struggle to optimise the cost of customer acquisition, retention and lifetime value.
We often find that this problem in greatest in Google – which is rapidly becoming the most powerful digital marketing channel, seeing over 2.3 million searches per day. With all these interactions, marketers are now paying a premium to get their brand noticed, reflected in the fact that pay-per-click advertising has nearly doubled over the past three years.
Customer Data Platforms are the foundation from which marketers can solve the challenges of gaining visibility on their paid search activities, and overcoming the rising costs of customer acquisition and retention in this channel.
1. Data Integration
Many businesses continue to struggle with optimising their marketing campaigns. The simple fact is that they do not have a grasp on their data - and lack the single customer view that is necessary to effective marketing channel attribution.
A Customer Data Platform alleviates this challenge by gathering, integrating and centralising customer data from various sources to provide a single customer view. Data sources are uniquely tagged in order to ensure that customer data is being captured at every possible stage of the customer journey. These sources are then integrated in order to provide a cohesive view of a single customer’s journey to purchase. For instance, website visits, email opens, and app interactions can be integrated with things like paid search costs and revenue.
The result is a stitched customer journey and single customer view that accurately displays a customer’s path to purchase, across a myriad of marketing channels, sessions and devices.
2. Data-driven Attribution
This data forms the basis for data-driven attribution modelling. On a basic level, this model identifies a set of unique user events that contribute to in some manner to a conversion, and assigns an actual, accurate value and cost to each of these events. This means you are able to truly understand the revenue generated from individual channels, and helps marketers understand whether they are acting in a cost effective manner.
Our attribution modelling works within our Customer Data Platform, and calls on the Markov model, which provides the most accurate value for each event through algorithmic probability, and which continuously learns from customers – making it more powerful every day.
With paid search, marketers are often unable to tell which keywords play a role in each step of the customer journey, and which don’t. However, data-driven attribution provides visibility on high and low performing keywords and campaigns. With this knowledge, marketers can cut or reinvest spending from keywords that are not bringing in conversions, and are therefore not showing high ROI. It solves the age old problem of knowing that half of your keywords don’t bring in revenue, but not knowing which. Without the rich, granular data that a Customer Data Platform provides, marketers are making these decisions based on emotion – and therefore risk not making the most optimal choices. Evincing this, we have seen a client make a 30% increase in their ROI by cutting wasteful keywords in this manner.
3. Optimise at Scale
Whilst valuable insights can be gained from using a Customer Data Platform and data-driven, multi-touch attribution, the level of granularity that these systems go into can result in several thousand rows of data being outputted. Whilst all of these contain valuable information about marketing activities, the scale of data can be difficult to manually optimise in real time – that it, it is difficult for a single marketer to employ all of the necessary, ROI boosting changes. Integration with a bid management platform is therefore key to scaling your paid search optimisation. Combining the power of the Customer Data Platform to discover high and low performing keywords, with the automation of bid management platforms, enables spend on poorly performing keywords to be quickly reallocated – automatically optimising your paid search. Alongside this, the need for manual keyword inputs and segmenting is eliminated with this integrated – with the Customer Data Platform acting in real-time in response to customers’ interactions with your keywords.
Once you have taken these steps to optimise your paid search channels, you are left with a Customer Data Platform that is the foundation for broader, cross-channel marketing optimisation. This means that you can begin to tackle other priority channels – to reduce costs and boost ROI – simply by integrating that data source into your Customer Data Platform and applying the same data-driven attribution modelling.
August 21, 2017
What is the Business Value of Connected Data?
By Carol Wolicki, RedPoint Global
The concept of “connected data” is a simple one. It means that all data across the enterprise is stitched together, cleansed, matched, and made available through a central location that has context and interrelationships pre-determined. Connecting data through a central location is the first step toward constructing a “golden record” for a customer, product, or service.
The perils in connecting customer data are particularly striking for two reasons: customers generate extensive amounts of data, with varying structures at different cadences, and this data is created through multiple online and offline engagement touchpoints. Competing in a digitally connected world calls for a central point of data control, which is where Customer Data Platforms (CDPs) fit in.
Why Connected Data Matters
Connected data is a foundational concept for highly personalized and contextually relevant engagement in today’s digital world. Without connecting data across systems, brands can’t hope to understand their customers well enough to provide the right message. A Customer Data Platform is the foundation for ingesting the data, connecting it, and making it accessible throughout the enterprise.
This single point of data control creates the right conditions to construct, continually update, and provide the unified customer profile that is so crucial to fostering omnichannel customer engagement. Whether the data comes from the CRM, email service provider, web analytics, social media, or the Internet of Things (IoT), all interaction data can be stitched together and made visible through a unified view. The unified view of the customer allows the enterprise to understand customer behaviors, preferences, and trends across touchpoints throughout the entire customer lifecycle. This comprehensive single customer view can also drive advanced analytics and real-time engagement flows, personalizing interactions with consumers in contextually relevant ways to close the engagement gap.
Connect Data with a Customer Data Platform
Customer Data Platforms are a dramatic reversal of the way enterprise data has historically been managed. Businesses for centuries found it easier to divide their activities and the resulting data among specialists. Those functions split into channels such as call centers, stores, internet, email, etc. Unfortunately, because the modern consumer has so many options for when and where to engage, those siloed channels create impediments to engagement.
As a result of this channel balkanization, the customer experience has taken a beating. In a Customer Experience Impact Survey, 86 percent of customers said they would willingly pay more money for a better experience. Yet only one percent of customers felt brands met their expectations. Brands have failed to close this customer engagement “gap” in large part because most companies still leverage siloed data generated from a fragmented technology stack. Connected data counteracts this siloed approach to technology investment, arming brands with the right information, at the right time, to close the customer engagement gap and forge powerful customer experiences.
Connected Data is the Future
The volume of data every enterprise must contend with has exploded. Recent research from Northeastern University found that the amount of data in the world will grow to 44 zettabytes by 2020, with 2.5 exabytes of new data generated every single day. The sheer scale of data created across the enterprise is enormous, and most brands have it all locked down tight in functional silos … inaccessible to everyone who doesn’t work in that silo.
With the rise of digital engagement, including IoT and all the device data it entails, it’s becoming less and less viable to operate with siloed data. Customers increasingly leave brands that are unwilling or unable to provide relevant experiences or act in a customer-centric manner. Connected data allows marketers and other stakeholders to know their customers more closely and more completely than ever before. There is tremendous potential value in creating a connected data environment, and the brands that recognize that will be the ones who remain competitive in the age of the empowered customer.
August 8, 2017
But, Do I Need Both? The CDP vs. DMP Discussion Continues: A Choose Your Own Adventure Story
By Cory Munchbach, BlueConic
Without question, our best-read content is about the differences between CDPs and DMPs. More than a year since we published the original blog post on the topic and coming up on a year since we consolidated it all into an ebook, it’s time to advance the discussion further. Yes, we still get asked all the time, “What’s the difference between a CDP and a DMP?” but now, more often than not, folks have a firmer grasp on the differences and want to know:
Okay, but do I need both?
And though it might be everyone’s least favorite answer…
In an effort to provide a more satisfactory response, though, we’ve put together some general guidelines for what your life looks like with a CDP, with DMP, and with both. (Note that we’re using BlueConic as the basis for the CDP capability and painting with a bit broader brush on the DMP side of things. Since not all DMPs are created equal, if you have specific questions about a particular DMP, we can go deeper based on our clients’ experiences.) To begin, here are the critical points of comparison:
Anonymous data management
(more on this here)
Anonymous data storage; in other words, data that is collected from someone who may eventually become identified or known but isn’t right now/yet.
Anonymized data is data that has an identifier associated with it, but that PII has been specifically hashed or otherwise de-identified
Data sources (more on this here)
First party orientation: collecting data from all of the brand’s touchpoints and data sources to tie them to actual behaviors exhibited by a unique user, whether anonymous or known
Third party orientation: the data-set is based on millions of cookies from across the web and matched with non-PII attributes such as demographics and multi-site behaviors
Data expiration and storage
(more on this here)
The profile created for each user is stored persistently in a database (Apache Cassandra in our case), so the data doesn’t expire and your profiles have no limit to the amount of data you can store in them, giving you a progressively richer view as the person continues to engage
Using the cookie for data collection means that a DMP “profile” typically only lasts for 90 days before it expires (sometimes shorter, and sometimes longer as some DMP vendors offer additional storage for an additional expense)
Identity matching (more on this here)
Deterministic: BlueConic only matches and merges profiles based on a unique identifier (email address, subscriber number, etc.)
Probabilistic: Because DMPs were built to expand advertising reach, the segments and matching are typically based on algorithmically derived guesses about links between people for look-alike modeling and high-level personalization
(more on this here)
Real time, no minimum size:
When the marketer defines a segment in BlueConic, it is immediately active, so the profiles that match the segment’s criteria associate and disassociate in real time (24 milliseconds) as their attributes change. There also does not need to be a minimum number of people in a segment for it to be a viable definition.
24 hour processing, minimum number of people: Once a segment is created in Krux, it takes 24 hours before it becomes usable for ad targeting, etc. Each segment needs to have at least 20 people in it because the algorithms require at least that number to still have a target cohort once you assume 40-60% matching.
So do you need both? Potentially, yes, but perhaps not. As a marketer, a CDP without question gives you a better base of data to work with: it’s a dataset of your actual audience that can be activated for use cases across the entire life cycle: acquisition, engagement, conversion, and retention. There aren’t restrictions on the types of data you can collect, the segmentation is real-time, and it’s channel agnostic to ensure the seamless flow of data. If you want to:
- Collect first party data for anonymous and known individuals;
- Assemble segments in a few clicks that can be exchanged with any other system;
- Deliver personalization across channels to that individual based on their unique needs and context…
Then you don’t need a DMP. And in fact, will be underwhelmed by the DMP’s approach to doing this kind of thing where such functionality appears to exist.
However! If you have an extensive digital advertising and modeling program that relies on the DMP to:
- Find look-alike models elsewhere on the web, particularly on other companies’ pages;
- Gather data about target audiences’ interests based on their behavior on 3rd party sites;
- Align campaign messaging with specific audiences as they move throughout the internet…
Then you have good reason to keep using a DMP because you’re benefiting from what these solutions are built to do: get more of the right people interested in your brand.
Taken together, a CDP + DMP can unlock some pretty compelling use cases, including those that we’re currently running with many of our customers who:
- Pass the third party segmentation in the DMP to BlueConic for on-site personalization and distribution to other systems readily
- Use BlueConic segments as the basis for the DMP’s look-alike modeling so that the brand’s actual audience’s traits provide the starting point of a look-alike audience
- Make BlueConic the conduit for data into the DMP from a CRM, ESP, or POS (hashing the identifiable data to comply with the DMP’s privacy, of course) so that the DMP has access to some of the data in those systems
(Additionally, server-to-server integrations between BlueConic and DMPs 1) provide data about users who haven't even been to the website, 2) provide data that was layered in after the fact, and 3) assign segmentation based on rules that weren't there when a user was online, or that changed after they left.)
The scenarios above play out differently based on your industry-specific use cases as well, but we’ll save that for another time. Unless you can’t wait, in which case shoot us a note to chat.
August 2, 2017
Five Things To Look For In A Customer Data Platform (CDP)
By Julia Farina, Lytics
Background: What Is A CDP?
The 2017 edition of Scott Brinker’s iconic Martech Landscape Supergraphic revealed that there are more than 5,000 marketing technology tools on the market today. While the customer data platform (CDP) is a relatively new category to the scene, the need for a neutral hub in the middle of marketers’ tech tools has taken off. In fact, according to a recent MarTech Advisor whitepaper, 28 percent of marketers ranked their multiple, disconnected systems as the biggest problem they face.
Validated by analysts such as Forrester and Gartner, Customer Data Platforms (CDPs) solve the fragmented customer data problem and improve traditional marketing workflows. The vendor-neutral Customer Data Platform Institute defines CDPs as a “marketer-managed system that creates a persistent, unified customer database that is accessible to other systems.”
Having a defined foundation is a great start. However, customer data platforms can and should provide so much more: intelligence that helps you access meaningful insights about your first-party data — and of course — integrations with your marketing tools to help you achieve a quick return on investment.
Here are the Five Things You Should Expect:
1. Predictive Insights Using Data Science/Machine Learning:
As a centralized customer data hub in the middle of your marketing-tech stack, customer data platforms are better-positioned to offer customer behavioral trends (e.g., “Highly engaged across marketing tools”) and predictive insights (e.g., “Likely to Convert”) than individual marketing point tools (e.g., email service providers, website personalization tools, etc.), which only have access to marketing channel-specific data.
Having access to powerful data science—specifically machine learning algorithms—that can be used to evaluate many data sources make the marketer’s life much easier. The system constantly learns without the marketer having to pre-define and re-define arbitrary rules (e.g., “if someone clicks X and does Y, that must mean that they are likely to Z”). These rules tend to be the default for most marketers; however, us humans just can’t compete with how fast machines can learn.
Many CDPs on the market today either weren’t originally built with self-learning machine learning algorithms or they simply started as another type of tool entirely, such as a CRM that has since been retrofitted. A more robust customer data platform should offer behavioral insights (e.g., identifying a tire-kicker versus a binge user), predictive insights (“likely to engage” or “at risk of churning”), and other machine learning-based segmentation as a key feature — not an afterthought.
2. User-Specific Content Affinity Using Machine Learning:
Content marketing is a critical part of marketing strategy but to see any results marketers need to match relevant content to individual users. This sounds so obvious and basic, but it can actually be really difficult to parse out who out of your 100,000 visitors is interested in “data-driven marketing technology” over “Southeastern Asia politics” let alone track how these interests might evolve and change over time.
Customer data platforms should help out with this using machine learning because any other approach is either too simplistic or not scaleable by a human. Look for a content affinity engine that helps you categorize your website into nuanced topics (not just marketer-defined keywords), track topic-based affinity levels for each individual and quickly build affinity-based audiences that deliver marketing results.
3. Data-Processing Speed:
With so much information flowing into CDPs, the method and speed with which it gets processed is an important factor. There are a couple of approaches to consider. Relational databases process data at the time of the request (i.e., when you go to build a segment) which involves scanning and grouping users within each data source separately — scanning potentially tens of millions of data rows — making the process of audience segment-building quite time-consuming.
Event-processing databases, on the other hand, process all data events at the time they occur so that they only have to scan the user database once as you build your audience segments. For example, if an event comes in from email (e.g., user clicks on email and goes to the website), the CDP updates the user database immediately, the user becomes immediately placed in or out of any relevant segments on the backend and this information gets sent to all of your marketing tools (e.g., Facebook, Google, Email Service Provider, etc.). From a marketing perspective, this means that you can almost immediately trigger a personalized email or display a specific piece of content to the user on your website, which is a pretty awesome thing.
4. Ease of Use — and Flexibility:
Customer data platforms are extremely efficient because they allow marketers to create one audience segment and sync it across various tools (as opposed to the pre-CDP model of manually creating the same audience segment across ten or so martech tools). This might seem like a no-brainer, but your CDP should be intuitive for anyone to use. If building audience segments is a frustrating and cumbersome process, you’re less likely to do it.
5. Time to Value:
July 11, 2017
CDP Industry Grew Sharply in First Half 2017
by David Raab, CDP Institute
Back in January, the CDP Institute published its initial Industry Profile. We found 27 CDP vendors with combined revenue of $300 million and projected a 50% annual growth rate. Six months have now passed and it’s time for another look.
We’ll start with everyone’s favorite buzz barometer: Google Trends. Over-all numbers are still quite low, but there’s clearly been an uptick of interest in Customer Data Platforms since January.
On to more detailed statistics. The original report estimated revenue and client counts using several sources. We felt the results were reasonably accurate in aggregate but figures for individual vendors might be significantly off. On the other hand, investment figures came from Crunchbase and employee counts came from LinkedIn, which are both fairly reliable (although LinkedIn probably undercounts clerical staff and people outside the U.S.). Since we now have the previous report as a baseline, we’ll just compare the changes in investment and employee counts over the six month period. This lets us publish figures for individual companies. Of course, there could still be errors, so don’t make any major decisions based on this information alone.
The following table compares January and July data for the original 27 vendors.
There have been several noteworthy developments:
- average employee growth was a respectable 17%, which would project to 34% over a full year. Only two firms shrank significantly, and one of those (Omniata) was acquired, which probably means that people no longer list it as their employer. Eight firms grew by 25% or more.
- four firms took additional funding during the period, adding $54 million in total. That’s a modest 7% increase over the previous $747 million. Moreoever, $40 million went to just one firm (Reltio). Given that growth is strong, the most reasonable interpretation is probably that the main firms have enough funds on hand to support their needs.
- growth is concentrated among the bigger firms. The five largest firms (by July 2017 headcount) accounted for 71% (249/346) of the total headcount increase. The five largest included four of fastest growing in absolute numbers and three with the largest percentage increase. Four of the top five also came from the tag management industry. As with funding, the outlier was Reltio, which is only firm to enter the top five and is oriented to master data management.
- two firms were acquired: Omniata by game maker Activision and Tagga by marketing automation firm Campaign Monitor. Two other firms on the list were already owned by larger companies: Datalicious (acquired in 2014 by data vendor Veda, which itself was bought by Equifax in 2015) and iJento (purchased by the Blenheim Chalcot investment group in 2016 and merged with Fospha). TagCommander changed its name to Commanders Act but ownership is the same. A day may come when CDPs are purchased by big martech vendors, but it is not this day.
In the absence of better information, we might assume that revenues grew at a similar rate to employment. So does that mean the industry is growing closer to 34% per year than the 50% we estimated in January?
Not necessarily. Many of these firms are well established (15 of the 27 are over five years old). That means they may have reached the point where their revenue grows faster than then head count.
In addition, we’ve identified another 16 CDPs that weren’t on the original list. Here are their statistics:
With 844 employees compared to 2,357 for the original 27 vendors, this group accounts for more than 25% of the combined industry total. This group is younger (only four are over five years old) and taking funds at a higher rate ($19.5 million on a $69.2 million base, a 28% increase). Based on private conversations, we know a few not yet generating any revenue, but more are in the initial hypergrowth stage. So it’s probably safe to assume this group is growing faster than the original 27 and pulling up the combined growth rate.
Bottom line: it’s been a good half for the industry, which continues to expand and evolve in interesting directions. Stay tuned for further adventures.
June 22, 2017
Mobile-Centric Is Customer-Centric (So Why Isn’t Your Customer Data Platform?)
By David Spitz, mParticle
When technologies are young, it’s common to dismiss them as niche, even when they aren’t. Remember that day that you realized streaming music services, social media, and ride-hailing apps were being used by everyone we knew, even our parents?
The same thing often happens in B2B markets.
Many enterprise software buyers and sellers still consider mobile solutions niche. They think of mobile like some James Bond car that’s been outfitted by Q-Branch to submerge underwater or ski over a frozen lake -- good for a few highly specialized circumstances, but not your average Joe.
In reality, though, a customer data platform that’s not mobile-first is more like one of those cars you see cut in half at the auto show. It may be driveable, but you wouldn’t want to try it on the interstate. Something very important is missing.
As Comscore noted in The 2016 U.S. Mobile App Report, activity on smartphones and tablets now accounts for two-thirds of all time spent with digital media, with smartphone apps alone capturing roughly half. Forrester estimates that mobile influences more than a third of all retail sales today, and that the mobile payment market will reach $17 billion in transaction volume by 2019. It’s no joke.
And yet, the vast majority of options marketers are using or now considering as the central connectivity layer for their entire marketing and analytics stack aren’t focused on mobile and connected TV app data.
Sure, it’s on their roadmap. Or, they may have an API that could collect mobile data, but it doesn’t actually offer the schema necessary to support app-centric notions, like geo-spatial and push tokens. Nor do they have a light-weight, battle-tested SDK that’s able to collect mobile signals in real-time without slowing down or crashing the app. (If in doubt, you can use a tool like Mighty Signal, which monitors SDK installations, to check if a particular vendor’s SDK is actually installed in-app.)
If you want to label a mobile-first customer data platform like mParticle’s as a “mobile” provider go ahead, but know this: you don’t need to be Special Agent 007 to need mobile embedded at the heart of your data strategy. Mobile-first data strategies are being adopted by the likes of Walmart, Lily Pulitzer, David’s Bridal and a host of other top retailers, publishers, hospitality and entertainment brands right now.
It's the companies that aren't focused on mobile that are the niche providers.
June 15, 2017
Why is a Customer Data Platform Important?
By John Nash, RedPoint Global
Brands have long stored customer data in functional silos, with disparate business units possessing different information. This division of data worked when the buying journey was linear and only occurred via a handful of touchpoints, but it is no longer feasible in the face of an empowered consumer base that increasingly engages with brands through a broad variety of online and offline channels.
The changes in the buying journey – from linear to multichannel – have sparked a need for brands to better understand their customers’ behaviors and past histories. Without this understanding, brands fall short of providing highly relevant, personalized, and contextually aware offers and messages. A new class of solution, called a customer data platform (CDP), meets this need by enabling an always-on, always-processing golden record that facilitates a unified and complete view of the customer.
The ability of a CDP to build this golden record is a vital one. Without it, brands cannot hope to understand their customers’ full array of needs, wants, behaviors, preferences, and intents – or how enterprises can remove friction from their customers’ lives and create higher value for them. In an age of digital transformation, it is this deep understanding of customers that will separate the leaders from the laggards.
Digital Transformation and the Empowered Customer
Digital transformation is forcing brands to remake their operating models at lightning speed or fall behind. Legacy brands have struggled to adapt: McKinsey recently predicted that current levels of digital disruption will shave 45 percent off incumbents’ revenue and 35 percent off their pre-tax earnings in the years ahead. More companies will soon feel the impact of digital disruption as well; in that same research, McKinsey found that broad digitization has only reached 37 percent of all companies as of early 2017.
The current crop of empowered customers, who have access to more information than any previous generation, have compounded the marketplace turmoil. There are 207.1 million smart phone users in the United States, and recent research found that 61 percent of U.S. consumers used three or more devices on a regular basis. But if each consumer has three devices, the number of touchpoints is much higher, and includes the company website, banner ads, social networks like Facebook and WhatsApp, email, direct mail flyers, billboard advertising, call centers, television commercials, IoT devices, and many other possibilities. Gartner predicts that there will be more than 20 billion IoT devices deployed within the next three years.
Empowered customers expect a consistent brand experience across all of these touchpoints, regardless of any underlying complexities that enterprises need to resolve. The perception is that if newer internet-based organizations can do it, then any other company can as well. The combination of touchpoint proliferation and fragmentation has led to a gap between customer experience and expectations, with 86 percent of customers saying they would pay more for a better customer experience, but only one percent of consumers saying that brands have consistently met their expectations.
Where a Customer Data Platform Fits In
Customer data platforms are a new class of solution that enables brands to adapt to changing customer attitudes and behaviors. CDPs effectively resolve the fragmentation that resulted from the organic way customer engagement technology evolved, with multiple point solutions deployed piecemeal as each new engagement channel gained prominence. When email became popular, brands implemented email marketing solutions; when social media proliferated, brands added social media management tools to their technology portfolio.
The problem is none of these walled garden engagement systems are designed to share data. Different point solutions use unique customer identifiers, ingest data at different paces, and store data in distinct formats. Some solutions might only collect anonymous batch data, others might have phone numbers or addresses, still others may only use a social media account name. Because each system uses a different identifier, data structure, or format, brands have been unable to connect any of their customer data into the unified customer profile needed to meet customers where they are.
Customer data platforms conquer this data-unification barrier. A CDP can ingest data that moves at any velocity, regardless of structure or volume, and uses deterministic and probabilistic matching algorithms to resolve customer identities across touchpoints. More than that, the CDP makes that data available across the enterprise and at the speed of the customer. This enables brands to proactively engage at the right moments, meeting customer needs with contextually relevant interactions.
CDPs are best deployed as a foundational capability, with an open garden approach to drive the customer engagement stack. This includes ingesting data, integrating, matching and mastering the data, and making the data accessible to engagement systems without replacing the existing point solutions. This implementation approach allows brands to maximize their technology investment, without the expense of ripping and replacing any current infrastructure.
A Constantly Updating Golden Record
Think of a customer data platform as an always-on, always-updating golden record that is made continually available at low latency to all touchpoints and users across the enterprise. CDPs connect data throughout the technology stack, acting as a central point of data control that provides insights about customer behavior and past history with the brand at the moment of greatest need.
Customers have already moved into the digital world, and are more likely to stop doing business with a brand who fails to provide a consistent and valuable experience across touchpoints. CDPs empower brands with the robust single customer view necessary to meet consumers where they are and provide contextually relevant interactions to close the gap between customer experience and expectation.
June 12, 2017
CDP Is The Core of Digital Transformation
By Anand Thaker, Intelliphi
Customer _________ fill in the blank: Experience, Centricity, Journey, Intelligence, Marketing, Retention. Yep, we are all on board. We aspire to be in greater sync with prospects and customers in the marketplace. For many, enter digital transformation (DX). The fidelity of data, identity, interconnectivity all are being recognized as essential to create and deliver a more custom experience. According to IDC, nearly $1.2 trillion (with a T) is forecasted to be spent worldwide on the mission in 2017. While existing and innovative systems will represent the bulk of the spend, the challenges will be use of the technology and design of how information will be captured and used.
The fuel for all of this? Comprehensive, timely, accurate customer-identifiable data flowing through it all.
Over the last ten years, the surprise has not been how much digital technology has evolved, but how much it has become integral to our everyday lives. Obviously, the urgency and moving target to engage with the digital customers has created an understandable anxiety for enterprises. In a Harvard Business Review article, PwC shared result of a “Digital IQ” survey to over 2,200 enterprise-level executives. Another surprise: the respondents rated themselves with less Digital IQ this year than a decade ago. A primary driver for the drop was a lack of availability and effective utilization of data.
Even with digital transformation critical to build a resilient business, technology changes continue to outpace a company’s ability to remain relevant. Regardless of the technology advances or innovations, a core function will remain: acquiring, maintaining and utilizing intelligently customer data.
Okay, you get it: Customer data is important. So, what competitive advantages does a Customer Data Platform provide in a digital transformation?
Fidelity. Double meaning here: Trust and precision. One of the most obvious is the quality of engagement with customers whose expectations rapidly continue to rise. The better the relationship, the greater tolerance of errors and higher premiums due to trust.
Over an eight-year period, a Watermark Consulting study showed that leaders in CX outperformed the broader market, generating a total return that was 35 points higher than the S&P 500 Index. All the while, laggards trailed far behind, posting a total return that was 45 points lower than that of the broader market.
Engagement technology will continue to evolve, so it becomes more important not to simply collect large quantities of data, but to ensure Customer Data Platforms deliver the relevant attributes for the specific engagement. This timely relevant delivery of data at the core of digital transformation needs to be coupled with well-trained talent to ensure drastic improvements in customer experience.
Goodwill. Accountants are underrated. Even in this discipline, there is a line item to value the goodwill – the intangible asset that arises when a buyer acquires an existing business. While Customer Data Platforms have an inherent cost in building and maintenance quantified by traditional line items, the data housed within them represents the essence of the marketplace and how the brand engages with them. More enterprise companies are recognizing the value of this most important asset in the overall makeup of a company.
Know Thy Customer, Know Thyself. In this more digitally dynamic, interconnected world, a view of your customers in the marketplace is a reflection of your organization. Messages and actions from your advocates are far more powerful than messages from yourself. This direct correlation through CDPs provides executives with a new and necessary perspective to make smarter go-to-market decisions. Concrete and near real-time access to this information coupled with experience helps organizations overcome adaptation challenges in the highly competitive red queen landscape.
After working with various data-driven industries including energy, finance and now marketing technology for over 15 years reaching $600M of total value creation, I continue to see companies thrive not from their amount of data, but from the synthesis and the democratization of its use across the organization. Customers may not be predictable, but the need to build trust and develop meaningful relationships at scale never changes. Understanding customers and where they operate does deliver a competitive advantage. It is one of the primary reasons the CDP Institute was formed: to help others replace anxiety and guessing with confidence and better judgment through improved customer data management.
Adapt and subscribe to the CDP Institutes updates and stay ahead by reviewing the ever growing list of resources.
How would better knowing your customers improve operations within your company?
May 31, 2017
How Can a Customer Data Platform Help with GDPR Compliance?
By Anthony Botibol, BlueVenn
As I write, we are just under a year away from the date when the new General Data Protection Regulation (GDPR) comes into force across Europe.
In the UK, GDPR will supplant the Data Protection Act, which was first introduced in 1998 – a year, remember, when Google when was still in its formative years and Facebook was a twinkle in a then 14-year-old Mark Zuckerberg’s eye. I think we can all agree that the online world has moved on significantly since then and reforms to data protection laws are well overdue. However, that’s not necessarily to say that marketers will be pleased with it.
While GDPR is EU legislation, the significant changes to the way that marketers collect, process and use consumer personal data will affect marketers around the globe. Essentially, if your organization handles and uses the personal data of any EU citizen then it will need to do so within the guidelines of GDPR. The UK Brexit will not change the need to adhere to it. Operating outside of the European Union will not remove the need to follow it.
Despite this, companies worldwide (and a worryingly large number of them in the UK) seem completely oblivious or ignorant to the repercussions GDPR will have to their ability to acquire marketing consent, profile their customers, and how enhanced data rights of customers could change how they do business.
This ignorance is even more concerning when you consider the swingeing fines a business could face for contravening GDPR. Punishment can be as much as 4% of a company’s global annual turnover, or €20 million ($22 million), whichever is greater. For Facebook for example, that could mean a penalty that costs it $1.1 billion.
Staying compliant in the face of GDPR will require far more diligent data management, and this is where a Customer Data Platform can really benefit. For example:
Unified data is more efficient to extract: One peril faced by businesses are the changes to be made to Subject Access Requests (SARs). This is where a consumer can ask for all the data you hold about them, how long you’ve held it for and with whom you’ve shared it. They can also ask a business to rectify incorrect data and insist the erasure of data collected unlawfully, unnecessarily or which has since expired.
Previously, requesting a SAR came with a fee and a generous deadline to produce the report. After GDPR, businesses cannot charge for a SAR and the request must be completed within a month. Should the subject find any use of their data that distresses them, they have the right to claim compensation.
Say you have a million customers and just 1% of them demand a SAR. Say it takes one person four hours to collate the data. That’s still thousands of manpower hours, even before any potential compensation crisis.
However, if a CDP keeps your customer data in a unified marketing database (which it should do), this will make finding and extracting the personal data far easier (and quicker) than scouring countless individual databases.
Database audit trails improve compliance: If a data protection governing body asked you how and when you obtained consent to send them marketing communications, could you do it? Could you easily provide evidence to show how long you keep records, and to any third parties you’ve supplied them?
In the same way the Single Customer View (SCV) process makes replying to SARs more efficient, all good CDPs should create an audit trail to demonstrate your compliance and provide evidence should anyone question your marketing practices.
Thankfully, with a CDP using consistently refreshed and aggregated data, all your customer records should be compliant to the latest suppressions and unsubscribes anyway. Which should mean you are only communicating with permitted people.
The BlueVenn CDP takes GDPR permissions even further, with the ability to mask data within sensitive fields. This means personal data is hidden from any data exports or campaign outputs (so it cannot be accidentally used), while still making it usable for analysis or counts.
One positive to take away from using a CDP to better manage your data is that you are treating your customer data with respect and their increased trust in you should make those who consent to your marketing far more willing to share the details that help you do your job better.
April 17, 2017
Replacing “Marketer Controlled” in the CDP Definition
by David Raab, CDP Institute
Last month’s posts on Customer Data Platform objections and vendor replies have helped to clarify my own thoughts on CDPs. The most important insight has been that the “marketer controlled” piece of the CDP definition really represented the idea that CDPs are packaged software rather than custom development projects. It’s the packaged nature of CDPs, including pre-built connectors to source and delivery systems, standard data models, predefined processing flows, configurability, and standard deployment practices, that lets marketers control the systems with little technical support. These are also what makes using a CDP so much more efficient than having an in-house IT team, agency, or systems integrator create a custom system from scratch – even though those other groups can use exactly the same technology as the CDP vendors.
One practical advantage of this describing CDPs as packaged software is that it changes the discussion with IT. Instead of asking IT to assess suspiciously magical claims of solving a notoriously difficult problem, CDPs pose the familiar issue of making a build or buy decision. This is a choice IT groups face all the time. Even better, many IT departments treat buying as the default answer unless there’s a strong reason against it. That’s obviously a better starting place for CDP vendors and marketers than a pitch that seems to imply the IT group lacks some critical competency that only CDP vendors can provide.
Replacing “marketer controlled” with “packaged software” also removes “control” as an issue, or at least obscures it. This is good because control is always a sensitive topic and it’s often an unnecessary distraction: at most firms today, marketing and IT actually share control over marketing technology. In any case, IT always has some involvement in a CDP, even if it’s just to provide credentials for data access. So there’s no point to seeming to threaten them with a rogue system that in fact is not.
The “marketer” in “marketer controlled” is problematic as well. One of the objections to CDPs is that customer data is needed by all customer-facing departments, not just marketing. So defining CDPs as "marketer controlled" seems to imply that other departments will lack access to the data or that marketing needs will take priority over other departments. Both of these could happen, but there’s nothing inherent in CDPs to make it so. Getting “marketer” out of the definition would open up situations where any department can run the CDP, including sales, service, operations, or corporate IT. That seems like the right direction to move.
So, should we abandon “marketer controlled”? Before doing so, we should ask if there are any arguments in its favor. There is at least one, and it’s powerful: marketing departments are the main buyers of Customer Data Platforms. This is what vendors tell me and this is what I see in my own consulting. This may just be a matter of perspective: the equivalent of CDPs are already bought by other departments, notably customer success, but they don’t usually call them CDPs. Still, the last survey I saw about the broader notion of “customer journey” found marketing was in charge of it 54% of the time , compared with just 12% for sales, 5% for operations, and 4% for customer service/support. So it seems likely that marketing will remain the primary owner of CDPs for the forseeable future. Do we really want to throw away a label that’s accurate? And, if we did, would we scare off marketers who might think CDP isn’t for them?
Also, while it’s a separate issue, I’m also sold on the phrase “packaged software” as a replacement. “Packaged software” sounds like something that comes in a box, not a cloud-based Software-as-a-Service product, which is how most CDPs are delivered. “Packaged software” also sounds more rigid than most CDPs and doesn’t account for the services that CDP vendors provide. But if not “packaged software”, then what? “Packaged system?” “Productized software?” “Integrated solution?” Nothing jumps out at me, somersaults to its feet, and sings "I'm perfect".
Bottom line: I’m inching away from “marketer controlled” but need to settle on well-chosen alternative before making a change. Thoughts are welcome!
April 4, 2017
CDPs, SCVs and DMPs – How Can Marketers Get Their Heads Around the Acronyms?
by Anthony Botibol, BlueVenn
It is becoming increasingly important for marketers to be able to take better control of, and make better use of, their data. Yet many do not willingly take on the role of a data scientist, and the world of marketing technology’s insistence on confusing acronyms do not make matters any easier. What is the difference between a CDP and a DMP? Where does an SCV fit in? Do all these technologies work together? What about DSPs? Do marketers need all four in order to get the best use of their data? We understand the confusion.
Over recent years, brands have needed to tackle an explosion of inbound and outbound channels through which to communicate with their customers. These channels and engagements, understandably, generate huge amounts of data to be stored within their data systems.
The trouble is, most of the time these data systems are disparate and disconnected – meaning fragmented, duplicated records or unwanted time matching and preparing data manually between source systems. This wreaks havoc when trying to retain an accurate view of the relationship you have with your customers and ensure that campaigns hit the mark
What marketers need, for more relevant and targeted communications, is for all the data within these operational data systems to be matched, merged and deduplicated to form a ‘single source of the truth’ or ‘single customer view’, that can be used across all touchpoints. This job is performed by a Customer Data Platform (CDP), which blends data across disparate data sources into a single record for each customer, while maintaining the compliance and governance required when your marketing system uses Personally Identifiable Information (PII).
The CDP has another primary role: as the real-time data conduit to feed into your marketing systems. Effectively, creating a single customer view that can serve data in a suitable format to analysis, journey orchestration tools and online advertisement platforms.
A Customer Data Platform is also responsible for integrating the information your brand collects as first party cookies.
When a customer interacts with your website, a behavioral, factual and descriptive picture of a customer is built, based on their browsing and download history. These first party cookies are generated by a device (a PC, mobile, tablet, etc.) rather than a person. However, as one customer can use multiple devices and each device can create a unique first party cookie, the problem of fragmented customer data can reappear.
With a CDP, multiple first party cookies can be merged through authentication (such as when you log on to a service with an email). This attaches a device (or several devices) to a single person, allowing for a view suitable for analytics, targeting and segmentation, as well as real-time website personalization.
Third party cookies, on the other hand, are created by companies your brand has a relationship with. Third party cookies track the behavior of customers across many websites, with many other organizations. Essentially, creating a far bigger group of data, of all the behaviors, of all people across the internet. This data can be put to marketing use by a Data Management Platform (DMP): a central data repository for third party cookie information and first party data.
One of the key differences between a CDP and a DMP is that a DMP can be used to target and acquire customers with messages outside of your existing customer database. For example, after collecting device browsing behavior, a DMP can identify others exhibiting similar browsing behavior, for similar products.
These people can then be segmented as part of your target audience, with a tailored advertising creative aimed at them. Through a demand-side platform (DSP), marketers can then upload the creative, advert or message and the DSP will place it on the relevant devices of the most appropriate consumers, on the most relevant websites.
The main appeal of a DMP is their massive audience reach, bringing together third party data from a wide number of places and creating an environment where digital advertisers can hyper-target their customers (and other peoples’ customers) for acquisition, up-sell and cross-sell strategies.
However, one important thing to note is that a DMP cannot legally use or share PII – that is, data that can be used to identify an individual (such as an email address). A DMP can target a person based on how they behave and what they’ve bought. But not through who they are. So, an advert can be sent to a device Jeff has used to search the internet – not Jeff himself.
- A CDP can use both first and third party data. This data is unified from internal and external systems, cleaned, governed and turned into a single, compliant, trustworthy record. It’s your central customer data hub, sharing knowledge, activity and behavior to all your internal and external marketing systems to underpin selection, segmentation, targeting, marketing automation, metrics, modelling and more. A CDP can use anonymous data, but mainly focuses on PII for the creation of targeted, personalized communications.
- A DMP’s primary goal is to facilitate hyper-targeted digital advertising – placing relevant creative content on the other websites a customer uses. It uniquely identifies every devices using a cookie, aggregating first and third party data to reveal massive audiences to you. But it does not identify the individual and it cannot personalize every single experience because the PII can’t be shared.
As this (hopefully) explains, a CDP and a DMP might initially sound like they do a similar thing, but they are more like different sides to the same coin. You could say that a CDP lets you talk to the customers you know by name, while a DMP lets you share a message with those you don’t.
Does that mean you need both? You could easily argue yes. Do you need one over the other? That depends who you want a relationship with, and how you want to shape it.
Finally, while we’re demystifying acronyms, I’d also point out the more recent references to the ‘Golden Record’ or ‘Unified Profile’, which are both born out of the fact that marketers need to account for the growing variety of data from digital sources. Anonymous data, cookies and device information are of growing importance, as we’ve just looked at, and so these new terms are more holistic ways to describe the Single Customer View. But essentially, we’re talking about the same thing!
March 29, 2017
Case Study – Mike’s Bikes
By Laura Hazlett, Ascent360
Roll in Today for some Sweet Deals, a Quick Tune-up or a Group Ride – How Mike’s Bikes encouraged existing customers to return regularly to their stores increasing their retention rate and revenue.
Mike’s Bikes operates 13 very successful stores in the greater SF Bay Area and an ever expanding online presence, drawing customers from all over Northern California and beyond – creating thousands of new bike enthusiasts every year. With each of the Mike’s Bikes stores dominating its market, they are now the largest bike dealer in North America.
CHALLENGES FOR CLIENT
Mike’s Bikes wanted to increase return traffic to their stores and encourage their customers to ‘come back’ and visit in hopes of a repeat purchase. The focus was on those customers who had not purchased from a Mike’s Bikes location (or online at mikesbikes.com) in the past 90 or 120 days. Mike’s Bikes wanted to engage with these specific customers, but also needed a hands off – automated campaign that would run in the background so they could concentrate on other marketing initiatives at the same time.
An ongoing email campaign consisting of 2 automated emails sent after a customer has not made a purchase at a Mike’s Bikes in 90 days and then again in 120 days. The emails include messaging and an incentive to encourage a future purchase at a Mike’s Bikes location. Purchase history data from the clients Ascent360 database is used to trigger the emails which are then deployed from the ExactTarget email tool.
To execute this campaign, Ascent360 first created an automation that pulled a daily list from Mike’s Bikes CRM database of customers whose last purchase at a Mike’s Bikes location occurred 90 or 120 days prior. An email campaign was then set up to send to customers 90 days after their last purchase date and then again at 120 days after their last purchase date. The first email (the 90 day email) includes dynamic content & imaging based off the location of the customer’s last purchase - to remind the customer of their favorite, and conveniently located local Mike’s Bikes store. The second email (the 120 day email) is a limited time offer (a coupon for $ off next purchase of qualifying amount) designed to motivate the customer to visit their local Mike’s Bikes store to make a purchase.
The first ten months of this campaign produced great results:
- Total Revenues of $100,703 with an Average Order Value of $346 and Revenues per Email at $4
- Conversion rate of 1.2% - double the industry benchmark for multi-channel retailers.
- Open rates for the 90 day and 120 day emails averaged about 26% – again, far above the industry standards for Retailers.
For more information about Direct Marketing Services at Ascent360, contact Evelyn Kirby at: ekirby@Ascent360.com.
March 23, 2017
Frequently Asked CDP Questions
By David Raab, CDP Institute
Here's a list of common CDP questions and answers. These apply to CDPs in general but details may vary for specific systems. Thanks to Barbara Saxby of Accelant Consulting for formulating many of the questions.
What’s a CDP? The CDP Institute defines a Customer Data Platform (CDP) as “a marketer-managed system that creates a persistent, unified customer database that is accessible to other systems”. That is, a CDP ingests customer-related data from multiple source systems, cleans and unifies it to create a single customer view, and makes it available for other marketing systems to use. Some CDPs provide additional functions such as analytics, predictive modeling, content recommendations, and campaign management. Note that CDPs are commercial software products; custom-built software or a collection of commercial components could do the same thing but they wouldn’t be a CDP.
How does a CDP differ from a DMP? Data Management Platforms (DMPs) support Web display advertising by maintaining a collection of Web browser cookies with attributes such as interests, demographics, and behaviors. Users select sets of cookies with desired attributes and send them to advertising systems, which deliver ads to those cookies when they appear on a Web site. Key differences from a CDP are: DMP cookies are generally anonymous, whereas CDP records are linked to identified individuals. DMP data is a list of attributes, while CDPs store very detailed information such as purchase transactions and details of Web behaviors. DMPs work primarily with Web data, while CDPs nearly always include data from offline systems such as CRM. Some DMPs have added CDP functions but it’s important to recognize that it’s not what they were originally designed to do.
How does a CDP differ from a data warehouse or data lake? Data warehouses and data lakes are usually enterprise-wide projects, which means they are not tailored to marketing needs. Data lakes are collections of data, usually in the same form as the original source systems, while data warehouses process the raw data to make it more usable. But neither type of system generally does the cross-channel identity resolution needed to build a single customer view. Data warehouses are designed primarily to support analysis, not direct customer interactions. Among other things, this means they are usually updated daily, weekly, or less often, while CDPs usually ingest data in real time and make it available quickly if not instantly. Most CDPs use the same data storage technologies as data lakes. The difference is the CDP has built-in features to do additional processing to make the data usable, while a data lake does not.
How does a CDP differ from CRM? CRM systems exist primarily to engage with customers. They capture data generated during those engagements but aren’t designed to import large volumes of data from other systems. Nor are they built to unify that data by matching different identifiers. Their database designs are optimized for direct customer interactions, which needs a different approach than a CDP, which is optimized for bulk processing and extracts. Adapting CRM designs to CDP purposes is pretty much impossible because the new design would be inefficient for the systems’ primary purpose. When CDP data is used by CRMs, the CDP data is usually restructured and supplemented with indexes so the CRM can use it effectively.
Can my company build its own CDP? Most CDPs are built using common technologies that your company could buy for itself, such as Hadoop. The main difference is that CDP vendors have added features such as connectors for common source systems, data preparation flows, and identity resolution processes. You company would need to build those for itself or to assemble, learn, deploy and managea collection of commercial products that do the same things. This would almost always take more time, cost more money, and be harder to maintain than a CDP. It would also give marketers less control.
How much work is needed to build and maintain a CDP? Less than you’re used to. Newer technologies like schema-less data stores and artificial intelligence-driven data preparation reduce set-up effort. Prebuilt modules for complex processes such as identity management eliminate custom development and simplify tuning to make the data ready for use. Standard connectors for common source and execution systems avoid custom integration projects. Users still need to decide what data they need and IT has to provide access. But most CDP deployments are still finished in weeks, not months or years.
What skills will I need to run a CDP? If you’re a marketer or marketing analyst, none. If you’re in marketing operations, a marketing technologist, or an IT person supporting marketing, you’ll need to arrange data access and provide some insight about the data in your source systems. The CDP vendor or a service partner will do the real technical work unless you want them to train you to do it for yourself. Marketers need more skills if the CDP includes analytic or campaign management features, but those are probably skills they already have or need to add regardless.
Does the CDP need a full-time person, a team, or a shared resource? Operating the CDP should be a part time job at most. Day-to-day tasks should be fully automated, so the operator simply needs to monitor the processes to be sure they’re running smoothly. Adding new data sources or integrating new applications will take some additional time but shouldn’t be major projects in most situations. Remember that technologies inside the CDP reduce the amount of manual effort. Again, using the CDP applications may take more labor but that’s work you’d be doing in other systems – and, in fact, the CDP eliminates many data preparation tasks, so most application users should actually be able to get more work done with a CDP in place. Don’t confuse CDPs with systems like marketing automation, where every new campaign is often set up by an expert user in marketing operations.
How much does a CDP cost? It depends on the CDP, data sources, volumes, and other factors. Some systems start at $50,000 per year or less for a mid-size business. Others start at $250,000 for an enterprise. Nearly any mid-size or larger company should be able to find a CDP they can afford if they look around a bit. What’s more important than the price is that a CDP should almost always be cheaper than alternative approaches. Most important of all, the CDP should deliver value that vastly outweighs its cost.
How long does a CDP take to implement? This also depends but it’s usually a matter of weeks unless you get hung up by organizational issues. It’s important to remember that CDP delivers clean, organized, actionable data, not just a data lake whose contents need further processing before they’re ready for use.
Who does the implementation? The vendor or a service partner should handle most of the technical work. Marketers and marketing operations staff need to provide guidance and corporate IT needs to provide access to company systems. Company staff can do more of the work if they want to take over operations after deployment. Some CDPs provide interfaces that make this easy; others are really built for a skilled technical user.
What is the level of experience required? No technical experience. You do need experience with your business and your company’s data to know what sources to ingest and what applications to support. The CDP vendor should be able to provide some advice on those topics based on its own previous experience.
Who defines the data requirements? The marketing and marketing operations team would identify sources. Analysts and campaign designers would identify preparation needed to make the data usable. Your IT staff, compliance, and security teams may also have some say in what gets loaded and how it can be used. The CDP vendor will provide guidance and may have a services team that can manage the process if you want them to.
Who does the aggregation? Aggregation, such as finding lifetime purchases per customer across all channels, is usually done within the CDP as part of the standard data preparation process. This assumes the aggregation rules are known in advance. Ad hoc aggregation might be done in an analytical application that’s part of the CDP or be done outside the CDP. Even ad hoc aggregations are usually easier because data from different sources has been standardized and tagged with customer identities.
What does maintaining the CDP look like? The CDP should allow automated data ingestion through APIs, batch loads, and other methods. Data preparation processes such as cleaning, standardization, transformations, and identity resolution should also run automatically. Operations staff will need to monitor the processes to ensure they run smoothly, explore any problems, and make adjustments when new sources are added, preparation flows are adjusted, or new output formats are needed. Making such changes requires an understanding of how existing processes work so operators can predict the result of an alteration. Exactly how much technical skill is needed will depend on the CDP interface and how much work is done by the vendor.
How is the CRM integrated? Most CDPs have standard connectors for CRM systems such as Salesforce. The integration would always read data from CRM. A CDP is technically capable of pushing data back into the CRM, but not all users want to do this. The CDP may also let CRM users view data, such as detailed Web behavior histories, without actually copying it into the CRM files. CDPs with analytical or campaign management functions are more likely to push data into CRM for execution.
How does CDP feed into my marketing automation tools? Marketing automation would be another source system for customer data and for events such as email sends and responses. What goes back into marketing automation will depend on the system. Some marketing automation systems have built-in databases, so the CDP would only feed attributes such as aggregated lifetime value or predictive model scores. Some marketing automation systems might take a customer ID from the CDP so they can identify duplicates within the marketing automation database. Marketing automation systems that don’t have their own database might connect directly to the CDP or to data extracted from the CDP and placed in a format the marketing automation system can accept.
Do the content management applications need to integrate with the CDP? There’s no requirement that content management integrate, especially if content management doesn’t contain information about which customers saw which content. But some CDPs can ingest content objects and analyze them, for example using natural language processing to extract topics, tone, keywords, and other attributes. Such information could be used to analyze content effectiveness and to make individual-level content recommendations. This is an area where CDPs vary widely.
How seamless are predictive profiling and other tool integrations? In some cases, prebuilt or custom connectors will let predictive tools read the CDP data directly or read extracts that are generated automatically. In other cases, the predictive systems will be connected through APIs that let the model builder choose which data to extract on a case-by-case basis. Some systems may work with file extracts rather than API connections, although this is more likely to be due to limits of the predictive tool than the CDP. The CDP should always be able to present the data in a format the predictive tool can use. Adding externally generated scores to the CDP is usually simple, since it’s a straight data feed with a stable structure.
If marketing owns the CDP, what is the required IT Involvement? The only essential IT involvement is to arrange for data access by the CDP and for user access to the CDP. Optionally, IT may play a role in operating the CDP itself, especially if the CDP runs on-premise.
March 19, 2017
CDP Vendors Answer the Skeptics
By David Raab, CDP Institute
A recent post on this blog listed several common objections raised by Customer Data Platform skeptics. We asked CDP vendors to respond. Here’s a digest of what they said. See the full answers as comments on the previous post.
- Cory Munchbach, Blueconic: These objections reflect unfamiliarity with CDPs or general tech issues.
- George Corugedo, RedPoint: Important to start with clear definition of CDP, as data environment to create and update Golden Record, used to support customer engagement for digital transformation.
Objection 1. CDP is too good to be true. Response: See for yourself.
Objection 2. CDP is just a data lake. Response: CDP has specialized features that data lakes don’t.
- Kiyoto Tamura, Treasure Data: CDP uses data lake technology but provides additional capabilities including prebuilt connections to common marketing systems, marketer-friendly query interface, and automated marketing decisions via data-driven marketing. Even technical experts who could manage their own data lake find a CDP saves them vast amounts of effort: the marketing analyst team at our clients at Shiseido did just that.
- Anthony Botibol, BlueVenn: Data lake serves many departments while CDP is specific to marketing and serves marketing needs including single customer view and fast access without help from other departments.
- Julia Farina, Lytics: It can be hard to get data out of a data lake. CDPs have prebuilt integrations with marketing tools and some include native execution tools.
- George Corugedo, RedPoint: Not at all the same. We can use many different data engines, including SQL and non-SQL. Hadoop is one option.
- Cory Munchbach, BlueConic: CDP is designed to support specific marketing objectives. For example, BlueConic makes data available in real time. A data warehouse or data lake can take days, which is too slow for many marketing needs.
Objection 3. CDPs don’t help because organizational issues are the real bottleneck. Response: CDP can’t cure this but it’s only a problem in some companies.
- Laura Corbalis, AgilOne: Can be true. CDPs reduce the resources needed to access and use data, freeing up time for testing,strategy, and execution.
- Cory Munchbach, BlueConic: Can happen if company doesn’t recognize need to use data to drive the business.
- George Corugedo, RedPoint: CDPs are an enterprise-wide initiative and take senior leadership to break down barriers.
Objection 4. Identity management is what’s really tough. Response: CDP solves this tough problem.
- Pedro Rego, BlueVenn: CDPs can do advanced identity management, create a single customer view, build a Golden Record, combine identified and anonymous data, and stay in compliance with privacy regulations.
- George Corugedo, RedPoint: It’s tough and CDPs do it very well.
- Cory Munchbach, BlueConic: Solutions are available, with or without a CDP. Not a reason to avoid CDPs.
Objection 5. Users will always need supporting services. Response: CDP makes things easier but you do still need skills.
- Laura Hazlett, Ascent360: CDP makes marketers more productive and less reliant on support from IT or a CDP vendor.
- Cory Munchbach, BlueConic: Not a CDP problem although it’s true that companies need adequate skills to use what CDPs provide.
- George Corugedo, RedPoint: Whether people need support is separate from whether they use a CDP.
March 10, 2017
Interview: How Agora Financial Increased Monthly Average Revenue by 167% With A Customer Data Platform
By Julia Farina, Lytics
We know that personalized marketing delivers the right message to the right person at the right time, but what sort of results can companies expect after executing a website personalization strategy? Agora Financial, a leading digital media company, adopted a customer data platform to execute personalized web campaigns and saw a 167 percent bump in website-specific revenue.
We recently had a chance to catch up with Agora Financial’s web director, TJ Tate, to learn more about the the specific business challenges that the popular finance publisher was trying to solve, why they chose a customer data platform with a built-in web personalization tool and kinds of results they’ve seen so far.
Lytics: Was there a business pain or opportunity that initially pushed Agora Financial towards onsite web personalization as a marketing strategy?
TJ: Yes, there were several challenges that we were trying to solve:
- Our means of advertising on our own web properties was limited to traditional banner placements. The vendor we used was very rigid and we could not really leverage the data we had on our users to make the experience more personalized.
- We wanted to be able to market to our audience based on where they are in our subscription sales funnel.
- Our customers range from financial to health to lifestyle affinities and we wanted a way to determine how interested our users were in certain types of content (topics, blogs, etc.) and how engaged they were with certain authors.
- We wanted to be able to trigger personalized experiences based on certain user behavior as well as what subscriptions they may or may not have.
Lytics: Why did you choose a Customer Data product versus other tools?
TJ: Quite simply, Lytics Customer Data Platform lets us pull in various types of customer data and is very easy to use. There are many tools that promise web personalization but a majority of them come at the cost of either being too controlling in one aspect or another or not supporting every data source we have at our disposal. Lytics does a great job of collecting, unifying, structuring and audience segmenting data regardless of its source and provides a user interface that allows our marketing team to effortlessly create website personalization campaigns in minutes.
Lytics: What’s your campaign schedule like? Are you executing on any specific use cases?
TJ: We have a rapidly changing promotion/production schedule. Our promotions change with the news so being nimble and testing quickly is key — and Lytics fits perfectly into that schedule. We are able to segment groups of our audience based on affinity for our current working promotions and start testing them across all of our websites in a matter of minutes.
We also have key moments in the customer subscription lifecycle that warrant very specific messaging. When our customers’ subscriptions are about to lapse, we can prompt them with a special renewal offer when they log into our site. When certain customers have more than one subscription, we can promote bundle offers. We can also send subscription offers to customers based on which authors and topics that interest them.
We also have plans to begin testing Lytics’ built-in data science scoring. We’re interested in using the user propensity score — which is a score that predicts how likely someone is to return with subsequent activity — to re-engage customers that are showing signs of becoming disengaged.
Lytics: What sort of results have your seen from your web personalization campaigns so far?
TJ: The results have been great! We are up 167 percent in website-specific monthly average revenue since launching onsite personalization. We have published 45 campaigns to date and some of them reach as high as 30% click through rates. Lytics has also provided us with insights into where our customers are located across our various marketing channels. Because of how usable and marketer friendly the product is, we’ve saved lot of time and reduced the level of effort to create and execute website campaigns.
About Agora Financial:
For over 25 years, Agora Financial, a subsidiary of Agora Inc., has been a leading innovator in the financial publishing industry. Agora Financial specializes in independent financial, health and lifestyle commentary through print and online publications, books, films and products.
March 7, 2017
Tough Questions from CDP Skeptics
By David Raab, CDP Institute
I’ve recently been talking to senior IT executives and consultants outside the Customer Data Platform filter bubble to understand their views on CDPs. Every conversation adds something new to the picture and I’m not done yet, so I’m sure my conclusions will evolve. But there’s already enough interesting insight to report on what I’ve heard. So here's an interim report:
1. CDP sounds too good to be true. This came from an executive with decades of experience working with customer data who was unfamiliar with the CDP story. His take was it’s just plain hard to build customer databases and he didn’t see how CDPs could suddenly make it simple. I mentioned new technology but he said his organization has plenty of big data experts so he didn’t think that alone would change the equation.
On reflection, the right answer would have been that CDPs save time because they contain packaged features that must otherwise be built from scratch. Those features include user interfaces that partly automate tasks which otherwise require much skilled manual work (for example, filling out templates that generate scripts, rather than writing scripts directly); prebuilt connectors to source and execution systems; and internal processes such as data transformations and identity management. This is the same advantage that any packaged software has over custom development, and should let IT people see the issue as the same build vs buy decision they’ve made in many other circumstances. Perhaps if it's presented in that familiar way, they would find it easier to assess CDPs as a plausible option rather than the latest brand of snake oil.
2. CDP is just a data lake. This came from another deeply experienced technician, who said he hasn’t seen a new data warehouse project in years, so there’s no point in comparing CDPs to those. Noted. It’s all data lakes and access tools, he said, and proceeded to label Tamr and similar access tools as CDPs. Again, the right answer probably would have been that true CDPs have many more built-in functions and time-saving features than generic data unification and analysis products. But this was a side issue because his main point was…
3. CDP can’t speed things up because organizational issues, not technology, is what slows them down in the first place. He cited delays waiting for permission from data owners and compliance officers. That seemed a much more important argument. The best I can come up with as a rejoinder is that CDP features let you move quickly once permission is granted. In some cases, the CDP also gives granular control over data use, which in theory makes it less risky for owners to grant access. Delays caused by CDP owners who can't decide what they want is a slightly different story. The CDP should reduce those because it can ingest and access data with minimal configuration. That means fewer decisions are needed in advance because there's little penalty for changing your mind later. Of course, data lakes are justified with exactly the same argument.
4. What’s really tough is identity management. This came from a veteran of the traditional customer data management world. I grew up in the same neighborhood and certainly agree that identity management (or customer data integration, as we called it back in the day) is a special skill. The actual context of this discussion was that few in-house IT groups understand identity management, which is why they shouldn’t try to build their own CDP from scratch. I’d say that’s true. I do recognize that IT teams can purchase specialized identity management software but would argue that if they lack the right background, learning those tools will be a long and painful process. CDP vendors have the advantage because they deal with identity management all the time. Indeed since a surprisingly large number of CDPs don’t have built-in identity management features, many CDP vendors themselves rely on those same external identity management products. The difference is they’re frequent, experienced users.
5. CDP isn’t enough because users will always need supporting services. This came from a system integrator whose firm regularly uses a real CDP and recognizes the productivity gains it gives them. When I casually suggested CDP could empower marketers in the same way that marketing automation has empowered them, he drew on his experience as a vendor of those systems and informed me that few users actually do much without calling the vendor for support. I’ll concede the point with some exceptions. His more important observation was that even when companies do have skilled people on staff, they’ll only have one or two – so if the company expert quits or even just goes on vacation, things grind to a halt unless the vendor picks up the load. That’s not really an argument against CDPs but it does mean that CDP vendors shouldn’t over-promise how much marketers will be able to do for themselves. It also reinforces what CDP vendors know but sometimes don’t like to admit even to themselves: professional services and on-going customer support are a very important part of their package.
None of these insights is especially startling. I’m sure CDP vendors who are out selling run into them all the time. But they’re still useful reminders that getting more companies to benefit from CDPs takes more than simply letting them know that CDPs exist. I certainly can see ways to make my own presentations on the topic more effective. And I have some new questions to ask the CDP vendors themselves about how to illustrate the benefits their systems provide.
February 22, 2017
Five Reasons to Leave Your Marketing Service Provider for a Customer Data Platform
By Karen Wood, AgilOne
Marketing Service Providers (MSPs) are the old guard of omni-channel customer data management, but they have lost ground in recent years to the dramatically more agile and accessible approach of Customer Data Platforms (CDPs) like AgilOne. For many brands, the question of switching from an MSP to a CDP is a matter of when, not if. The top five reasons for the switch are that brands want:
1. Direct access to their data
2. A platform based approach to managing customer data
3. Expertise in both digital and offline marketing
5. Lower TCO on their marketing investments
Let’s dive into how CDPs are different from MSPs in each of these five areas.
Access to data
CDPs give marketers direct access to first party data, which is open and accessible. Marketers can easily explore data on an ad hoc basis, and easily connect their data with third party systems without additional contracts. This open access lets marketers more freely leverage data for marketing, enabling more relevant customer engagement and increasing marketing ROI.
MSPs directly manage their clients’ first party data, which creates a “black box” for their clients. Data requests must go through the MSP, and these requests are typically handled on a case-by-case basis. This causes lengthy wait times for accessing data, creates additional costs, and hinders the ability for marketers to fully leverage customer data for personalized engagement.
CDPs are built on a rich SaaS platform with cutting edge big data technology. This platform-based approach allows for continuous updates with new features and capabilities always being added.
MSPs typically combine some technology and services. An MSP does not typically develop technology themselves, but instead relies on a partner community to provide different features. This can result in a random mix of different, sometimes antiquated technologies, and the MSP approach can cause an over-reliance on services—again, increasing costs and creating a barrier to data.
CDPs that focus on the enterprise offer a single platform for both online and offline data. This is essential for brands that rely on direct mail and catalog revenue streams. For these brands, a CDP should be able to ensure the accuracy and quality of the direct mail database, and let marketers send direct mail to intelligent customer segments that leverage a complete digital/offline customer profile.
MSPs typically do not include offline data sources as part of their customer profile, and they typically do not provide direct mail database services. This causes marketers to leverage separate services for direct mail, and there is no unity or consistency across channels.
CDPs are inherently future-proofed because CDPs are platform-centric, not services-centric. This means that as a brand’s marketing needs evolve, and as the marketing technology landscape evolves, the a CDP will always stay lock-step with any changes on the horizon. Also, an enterprise CDP can easily scale to meet new demand, and be easily configured to meet new requirements.
MSPs create custom solutions for specific use cases in specific moments in time. As needs change, MSPs respond to change by building new custom systems. This places limits on how a brand can re-envision its marketing strategy, reduces a brand’s ability to be nimble, and makes any changes a brand chooses to implement a heavy burden for the organization.
Lower total cost of ownership (TCO)
CDPs typically have a pricing structure that is predictable and that allows marketers infinite flexibility and configurability without additional costs. This keeps the TCO low no matter how a brand’s needs evolve.
MSPs typically become more expensive based on the customization of a solution. One-off requests such as new data pulls, new reports, or new integrations typically incur additional charges. This keeps the TCO high, especially as brands grow and evolve.
Once upon a time, enterprise B2C marketing and CRM teams engaged with MSPs to optimize analytics programs, stitch data together, and attempt to orchestrate personalization across channels. But today, enterprises are trading up for the ROI of CDPs, which provides a more agile and accessible alternative to the legacy, services-heavy, request-driven engagements that are the hallmarks of MSPs.
February 14, 2017
Using CDPs to Support Analytics
By David Raab, CDP Insitutute
It may seem self-evident that the reason marketers create a unified customer view is to give customers a unified experience (or, more precisely, an optimal experience driven by unified data). But when the CDP Institute recently asked marketers to rank uses for a single customer view, “consistent treatments” was far down the list. Generic “personalization” came first, followed closely by two analytical uses: “customer insights” and “measure across channels”. Other research gives similar answers about the importance of analytics as a reason for assembling customer data. So it’s worth looking more closely at this counter-intuitive result.
Let’s start with the most basic question: why do marketers want analytics at all? There are plenty of reasons, including understanding their customers, tracking customer behavior, identifying segments for promotions, and measuring promotion results. What these all share is they require a cross-channel view of the customer, which can’t be provided by systems that execute email, Web sites, mobile apps, ad placements, call centers, or retail point of sale. In addition, many execution systems have limited reporting features for their data. This is why marketers often purchase separate systems to do Web analytics, social analytics, customer satisfaction surveys, or interaction analysis. Marketers also want advanced analytic functions such as predictive models, recommendations, and optimization. These are beyond the capabilities of most channel systems and often require cross-channel data.
So we see that many of marketers’ analytics needs cannot be met by their execution systems. The first step to closing the gap is to pull data into a data store that’s designed for analytical projects. For projects that only need data from one channel, the next step is simply to hook up specialized analytical tools. For projects that need a cross-channel view, the next step is to link data related to the same customer. That unified view, in turn, is made available to the analytical tools.
Of course, what we’ve just described is the process of building a Customer Data Platform. So it begins to make sense that marketers would consider analytics a major application for the CDP database. This doesn’t mean the CDP must provide analytical tools, although some do. Rather, it means the CDP is preparing data for access by analytical tools, whether or not those tools are part of the CDP.
Let’s look more closely at CDP capabilities that can support analytical uses. Remember that not all of these are provided by every CDP – or needed by every CDP buyer.
- multiple data types: marketing data includes structured data, such as purchase transactions; semi-structured data such as Web logs; unstructured data such as social media posts; and other formats including images, video, and audio. These come from disparate systems that often use different technologies. It’s important for the CDP to ingest all those data types, tag them for access, and place them into a single data store. This makes it easier for analysts to find them and lets analysts use a single toolkit for further processing.
- easily add new sources: marketers are constantly adding new data sources, often as part of tests to measure the sources’ value. Being able to incorporate new data with minimal effort makes it much easier to evaluate new sources and new information from existing sources. This lets marketers try more things and quickly assess which are worth keeping.
- persistent, detailed data: analysis often requires examining historical data in fine-grained detail, sometimes down to individual mouse clicks or even hovers. Exactly which data will be needed in the future isn’t known when the data is captured. The CDP gives marketers an easily-accessible place to store raw data and later retrieve whatever portions are needed. Source systems often discard such data because they don’t need it for operational tasks.
- data cleaning: analysts spend the bulk of their time preparing data to make it useful – 80% by one estimate*. Typical tasks include removing anomalies, standardizing values, and harmonizing formats. A CDP that cleans data as it’s loaded makes the data available for use with minimal additional effort. This is especially helpful when different analysts might otherwise be performing the same preparation tasks or when the same analyst might be repeating the tasks at different times. A CDP can also provide a central place to document cleaning processes so everyone can understand what was done to the data and so processes can be vetted and improved over time.
- transformations: a CDP can also calculate summary statistics that are used in common analyses, such as cumulative purchases, average, and trends. Like cleaning processes, these are easier to do once when data is loaded into the CDP, than separately for each new project. Some CDPs can also check for behavior patterns or deviations from patterns, extract information from unstructured source data, and apply tags based on the extracted data.
- anonymization: customer data often should, or must, have personal identifiers removed before it can be used for analysis. This is another process that the CDP could do once rather than relying on analysts to apply their own procedures.
- identity resolution: many analyses rely heavily on assembling cross-channel profiles. This is a core CDP function that relies heavily on historical data and complex, carefully designed and tested processes. Having it centralized and documented saves time and ensures consistency across analyses. (To be clear, not all CDPs do this. Those that don’t may be used in situations where it’s not needed or may integrate with external identity resolution services.)
- restructuring: CDP data often must be converted into new formats before analytical systems can use it. This might mean converting distributed files like Hadoop into relational tables like SQL or converting relational tables into flat files. When the information to be restructured is known in advance, the CDP can do this on a regular basis rather than requiring analysts to do it separately for each project. Restructuring can also include other changes such as creating indexes or summary tables.
- extraction: even when the specific data to be restructured isn’t known in advance, the CDP can provide standard tools to do custom extracts. These can save analysts from creating custom extraction processes, saving time and reducing the chance for error.
Once again: not every CDP will provide the functions just listed. CDPs that do provide these functions will differ substantially in exactly what they offer. So while it’s important to recognize analytics support as a key CDP benefit, marketers also need to ensure they purchase a CDP that actually meets their analytical needs. As with any application, this means they must start by defining how they’ll use the data, work backwards from that to define the functions needed to support those uses, and then carefully evaluate potential CDPs to understand how they perform those functions.
* Crowdflower, 2016 Data Science Report, http://visit.crowdflower.com/data-science-report.html
February 7, 2017
4 Ways Customer Data Platforms Will Change the Way You Look at Marketing
By Glenn Thomas Davis, Treasure Data
Imagine you’re a telemarketer working your way through a list of cold leads. Every time you reach a live human being, you go into your canned pitch:
“Good morning, Mrs. Smith! I’m calling because I’ve got a great opportunity for you! Did you know that four out of five people in your neighborhood are eligible–”
“–hello? Mrs. Smith? Mrs. Smith?”
This is what we internet marketers are like when we don’t understand our customers, which is why we collect data. But little bits of data, taken out of context, don’t really help us understand our customers very well.
How big a problem is this? Take a look at this chart from a recent Econsultancy survey, Customer Recognition: How Marketing is Failing at its Top Priority:
Apparently it’s a big problem. Marketers recognize the need for a single, unified customer view, but it’s out of their reach. Why? According to an analysis by David Raab, at least part of the reason is lack of technical expertise:
Enter the Customer Data Platform (CDP). Identified by Raab in 2013 and added to Gartner’s hype cycle in 2016, CDPs provide a central, unified customer view, ideally in a tool that is manageable by a marketing person without advanced technical skills.
How exactly are CDPs different from other kinds of data collection platforms, and why are the differences important? Read on to find out the four key factors:
- The CDP is the Cure for the Customer Data Gap
- The CDP is the Connective Tissue Between Customer Data Sources
- Customers Expect What a CDP Enables You to Deliver
- Personalization at Scale is the Key to Data Differentiation
1. The CDP is the Cure for the Customer Data Gap
Econsultancy’s survey concludes that digital recognition is central to growth and retention strategies:
Eighty-six percent of marketers prioritize providing an integrated experience across devices and media, albeit in a statistical tie with growing an addressable audience. Together, these two are at the core of modern marketing; they represent the ability to know who customers and prospects are, at scale, wherever they are, and the ability to use that knowledge to provide excellent products, marketing and services.
Why is this a challenge today? A large part of the reason has to be the fact that most customer data originates in systems that weren’t designed to share it with anything else. The difficulty of getting various systems to talk to each other leads to data silos. To achieve a unified customer view, marketers have a range of possible choices.
Let’s take a look at these options in turn.
- Data Hubs, like Boomi and Jitterbit, allow data to be moved between systems, which is helpful for achieving a unified customer view, but not adequate. Data hubs create redundancy and allow inconsistency. Furthermore, they do nothing to address identity resolution, and they don’t persist historical data. They are more useful for cross-system processes than a single customer view.
- Data Warehouses, which store all data in a central database, are a big step in the right direction. But EDWs are mostly for analysis and don’t support real-time updates or access. They are typically big corporate IT projects that take years if they ever get done.
- Marketing Clouds and Suites, like Adobe Marketing Cloud, combine customer-facing systems in a single system with a unified database. In theory, this sounds great, but the reality is that such systems are built from acquisitions and are very lightly unified, perhaps providing a table to link identifiers and some very skinny profiles, e.g. for personalization. They don’t allow easy analysis or real-time access across systems, and they don’t play well with external systems, which spells trouble for marketers who want to integrate data from multiple systems.
Marketers who are already using multiple systems to collect data, i.e. most marketers, need to unify the data they are already collecting into a single persistent and robust customer view. A system that is actually designed to do this is a CDP.
2. The CDP is the Connective Tissue Between Customer Data Sources
Of course, many systems that are not CDPs collect detailed tracking data on customer behavior. An example is the Data Management Platform, which tracks cookie data on the web in order to build up advertising profiles. The major difference between a DMP and a CDP is anonymous vs. identified profiles. Cookies are anonymous, and DMPs (unless they are a CDP by another name) store only anonymous data by design.
Without personal identifiers, there’s no way to link data from one source (e.g. the web) with data from other sources. Hence DMPs do not make it possible to obtain a single customer view.
More than this, customers expect you to be able to identify them.
3. Customers Expect What a CDP Enables You to Deliver
Consumers consistently say that they want more personalization in their online and offline shopping experiences, and 49% say they buy more from companies who provide personalized ads. This desire for personalized service is not confined to retail—it is a reflection of rising customer expectations for service across every industry. As Raab says, “today’s customers simply assume that your company knows – and remembers – who they are, what they’ve done, and what they want, at all times and across all channels.”
Of course there’s another side to this. Everyone nowadays has had the experience of “creepy retargeting”—the unwelcome sensation that either the ads popping up wherever you go are a little too perfect, or conversely, that an ad for a particular product keeps popping up everywhere long after you’ve already decided you’re not interested. But in can be argued that creepy retargeting is just dumb retargeting. A company that injects its ads intrusively into the customer experience is a company that doesn’t understand its customer well enough.
There’s no such thing as understanding your customers too well, or being too smart about personalization. And a company that employs a CDP is a company that can deliver smart personalization at scale. A recent Harvard Business Review article pegged CDPs as a key component of this long-anticipated goal:
Given the complexity of coaxing meaning from a wide range of data, companies tend to limit the data they use, generally focusing on the data that’s easiest to get. In addition, traditional CRM systems, built on more rigid, relational databases, often don’t have the flexibility or scalability to manage vast piles of structured and unstructured data. What companies need are systems that can run the advanced analytics to discover useful and practical insights, and then trigger the sending of appropriate messaging, e.g., if customer “A” does action “B,” send item “C.”
An emerging answer to this issue is the customer data platform (CDP), which is the modern version of a customer data warehouse—though one that is far more flexible and interconnected.
4. Personalization at Scale is the Key to Data Differentiation
Customers’ rising expectation of hyperpersonalization is driven by the customer experience they receive from the Data Giants that increasingly shape our world.
Mark Andreessen memorably said “software is eating the world,” but it might be more accurate to say that data is eating the world. As Stephen O’Grady pointed out and I discussed at more length in an earlier post, software is no longer a robust differentiator among technology companies, if it ever was. Software is cheap, readily available and easily duplicable, whereas data sets are hard to build, hard to get and virtually impossible to copy.
O’Grady’s example of data differentiation in action is Google Maps vs. Apple Maps on iOS. The thing that differentiates the experience of using Google Maps from the experience of using Apple Maps is not the software design per se. The software design is not what causes Google Maps to send you safely to your destination and Apple Maps to drive you into a lake. The difference is the gigantic, detailed body of data Google has to draw from in Google Maps, data they had been collecting from Maps users for years before Apple Maps ever came out. Since they will continue to collect data and refine their product based on user experience, forever, it will be very difficult for Apple Maps to ever catch up.
We call the Googles and AirBnBs Data Giants because of this differentiating factor, which O’Grady calls a Data Moat. But as the Maps example highlights, it’s not simply the sheer amount of data they own that differentiates the Data Giants, it’s what they do with it. The Data Giants use unified, rich data to provide a hyperpersonalized experience for their customers. And they do it at scale.
A CDP can help differentiate your business by giving shape and meaning to the data you collect. Another way to put it is that connected data you use to improve customer experience, to automate and personalize marketing processes, and to align your teams on these goals, is Live Data. Data that is disconnected, stale, and inaccessible to your teams, is useless.
Fortunately, more Customer Data Platforms are becoming available each day. They all have different approaches to managing and accessing your customer data, so as always it’s worth doing your homework. A good place to start is the Customer Data Platform Institute. If you’d like to explore a CDP specifically designed for Live Data Management, by all means take a look at our platform Treasure Data.
February 1, 2017
Top 10 Web Personalization Use Cases Powered By Customer Data Platforms
By Julia Farina, Lytics
According to eMarketer research, 67 percent of marketers surveyed said they personalize their websites by leveraging consumer behavior-based data. In other words, the marketers have advanced into the new frontier of tailoring engagement with consumers based on actions they take across a brand’s website, email marketing, commerce channels and more. And it’s no wonder: Web personalization not only boosts conversions and engagement levels but it an also improves customer experience. In fact, according to a recent Forrester Research report, 62 percent of U.S. online adults have chosen, recommended, or paid more for a brand that provides a personalized service or experience.*
There are many forms of personalization – email, mobile-app, advertising, etc. – but web personalization is one of the most common. Successful web personalization delivers the right content to the right person at the right time, and we know that a Customer Data Platform is a key ingredient. But where do you start as far as actual use cases? According to Lytics customers, here are the ten popular web personalization tactics to get you brainstorming:
1. Content Recommendations
Suggest specific content to your customers based on their individual content affinities (i.e., the topics that interest them based on their engagement behavior with your brand’s content).
2. Event Promotions
Keep your customers engaged online and offline by promoting location-specific events based on where your customers are and what interests them. If you’d like to see a live example of this, we’re currently promoting a Forrester Webinar on “The Secrets To Marketing Personalization” on the Lytics website with a campaign targeting new and returning web visitors (but excluding employees and people who have already converted on the campaign). And, if we wanted to get even more targeted, we could use machine learning to focus on only those visitors who are highly engaged across our communication channels.
3. Subscription Sign-ups
Target unknown users with a message prompting them to provide their name and contact information (e.g., email address) so that you can grow your marketing database and begin a dialogue with them.
4. Customer Support
Give special attention to your customers that have an open (and possibly negative) support ticket so that they feel taken care of. Or suppress promotions to those with negative tickets.
5. Special Promotions
Send targeted promotions based on your customer’s engagement trends. For example, free shipping to someone that has unpurchased items in their shopping cart, or specific incentives to customers that are showing momentum and are most likely to buy.
6. Account-Based Marketing
Nurture your targeted sales leads by sending personalized messages to customers or prospects from specific organizations.
7. Referral-Specific Engagement
Send specific welcome messages or incentives to customers that come to your website from a referral partner or affiliate website.
8. New Customer Orientation
Engage your new customers right away. Send them personalized messaging based on what set-up actions they need to take in order to fully take advantage of your products.
9. Service Announcements
Keep your customers informed by alerting them to time-sensitive notices (e.g., subscription renewals), new feature availability, or a temporary service interruption.
10. Win-Back Messages
Send specific messages to people who have unsubscribed from your email newsletter, or use machine-learning techniques to identify at-risk customers and target them with purchase incentives.
Understanding who your customers are, how they engage with your brand and what content interests them are the building blocks for personalized, one-to-one marketing campaigns.
If you’d like learn why 75 percent of e-business and channel strategy professionals say personalization is their top priority, check out our upcoming Forrester webinar, “The Secrets of Marketing Personalization.” It will cover the market trends driving the demand for personalization and show how leading brands such as retailers Dr. Martens and Wildfang are growing their businesses by as much as 73 percent with a one-to-one digital marketing strategy.
*Forrester Research, Inc. (2015). “Just For You: Use Personalization Technology To Help Associates In The Retail Store.”
January 26, 2017
Six Considerations Before Starting a Single Customer View Project
By Anthony Botibol, BlueVenn
The ambition to build an enterprise Single Customer View (SCV) database is typically instigated by the marketing team. However creating a unified, aggregated and cleansed record of every customer from your many data sources will require the input from many other departments, too.
As you can imagine, each of these departments are likely to have their own objectives with what they hope an SCV will achieve, meaning several things need to be considered before a project is undertaken.
- Getting cross-department commitment
While marketing is seen as having the most to gain from an SCV, it can also benefit other departments, such as sales, finance and HR.
While this will make it easier to build a business case for the project, it can risk other departments ‘muscling in’ with their own sets of requirements, making the project more complex, expensive and time consuming. It needs to be clear from the start who will take ownership on the SCV and what its primary objective is – marketing!
- Getting hold of source data
As data is owned by different teams (both internal and external), getting hold of source data is one of an SCV project’s biggest challenges. It requires company-wide coordination to ascertain different data sources, while IT are traditionally heavily involved to manage the creation of automated data feeds from source systems.
Customer Data Platforms are making this easier, but the creation of the SCV will also require input from the likes of sales, finance and operations in order to a holistic customer view for matching, enhancing and supressing data.
- Do you want fixed price or agile development?
As with any project, organizations are concerned about costs and keeping the project on course. Typically, an SCV project will use the following models:
- Fixed price – a fixed price and fixed timeline project offers reassurance, but will require in-depth knowledge of the data sources and rules in order to determine costs and time.
- Agile – a flexible timeline and budget model with the SCV developed in ‘sprints’ allows quicker deliverables, agile response to unexpected opportunities and moveable priorities but this option can be prohibitive for organisations with a set budget.
- Allocating the right resources
It may be that marketing ‘owns’ the SCV, but it is rare for marketing teams have the resources or experience to manage data and run an SCV project. Finding people within the business who understand the different data sources and business rules is essential.
- What data are you going to use?
Determining the values and benefits of different data sources up front is important and you need to be realistic about the volume of data feeding the SCV. Can marketers make sense of hundreds or thousands of fields of data?
That said, there are some essential data sources that all SCV should consider, including:
- Personal information – name, address, email, phone numbers
- Transactional data – what they have bought in the past and their buying patterns
- Communication history – response to emails, click through, open times to shape future communications
- Geodemographics – age bands, affluence and lifestyle information to improve segmentation
- Suppressions – opt ins and opt outs, telephone preferences and bereavement registers, etc.
These data sources provide great insight to deliver far more innovative and relevant campaigns. By ensuring that your data is trustworthy, it ensures that any segmentation or analysis provides true answers and accurate business intelligence.
- Identifying your customer
You want to build a Single Customer View – but who is your customer? The answer is straightforward for B2C organizations, but less so in the world of B2B. For example, is your ‘customer’ the business or an individual within the business? Are you dealing with head office, or multiple branch locations?
The process of unifying your customer data needs plenty of deliberation while you establish your requirements. Don’t forget, a Single Customer View on its own will achieve very little if it is not contained within a platform that makes it accessible to your existing marketing tools. However, as long as you have realistic expectations of what you want the system to deliver, you’ll have the foundations in place for more efficient and effective marketing.
January 23, 2017
The Right Message and Channel Matters
By Laura Hazlett, Ascent360
How Stevens Pass Used Omni-Channel Marketing to Talk to Customers Where They Are Listening.
Located on the crest of the Cascade Range in Washington, Stevens Pass receives abundant snowfall making it one of the top locations for Washington residents. Stevens Pass caters heavily to day travelers from the surrounding areas. With a variety of terrain, Stevens Pass is an ideal location for all ski levels. Night skiing allows the resort to be open longer hours enabling skiers to take full advantage of the beautiful resort.
With an ever-changing demographic, Stevens Pass is faced with the challenge of speaking to all customers differently. The message you use for a college student is much different than the one used for a family or a more mature skier. Along with the message, the marketing channel matters as well. While young customers may take more to a Facebook ad, a mature customer may do best with email. Stevens Pass was faced with the challenge of talking to each segment differently and speaking to them where they are listening. With the Season Pass push upon them, it was important that they take all channels into consideration to gain the most value from their marketing efforts. But how do you tailor the message and the channel to each different segment?
To segment their data, Stevens Pass turned to their Customer Data Platform software to create 12 different customer segments based on life stage and purchasing behavior. The first step was to separate their data into three different life stage ranges; young adult, family age, and mature. From there, they used purchasing behavior to create additional segmentation within the life stages; Season Pass Holder, Former Season Pass Holder (hasn’t purchased a season pass in 2+ years), Day Tripper, and Prospect (customer exists in the database but have never purchased).
Segmenting the data was the first step, importantly, Stevens Pass also personalize each message to each of the different segments. Highlighting specific events such as après ski for young adult and lessons for family, Stevens Pass personalized the message based on life stage and purchasing cycle by reiterating pass holder benefits to Season Pass holders and the benefits of Stevens Pass over competitors to prospects.
The last piece of the puzzle was to test which marketing channel would produce the best results for each segment. In the previous year, Stevens Pass had used similar segmentation to personalize the message, but primarily used the email channel. This year, they wanted to compare results with Facebook and Google to evaluate which channels would perform the best. With the ability to now try multiple channels, Stevens Pass decided to try a couple of novel campaigns. They sent unsubscribed emails into Facebook and Google to see if they could reconnect with customers.
They also decided to use a portion of the budget to push their season pass video content out to segments on Facebook and Google.
Smartly, an automated campaign was created to remove all Season Pass Holders from the campaigns across all channels. This allowed Stevens Pass to allocate their marketing budget on activities focused on converting prospects.
Over the next couple of weeks, Stevens Pass monitored the campaigns to see what was working and what was not. Email was proving to be the leader for Mature and Family segments but was greatly lacking in engagement for the Young Adult segment. On the other end, Young Adult was performing great in Facebook while Mature was doing hardly anything. Looking through the results, the decision was made to push harder into the Young Adult segments in Facebook and reallocate the Mature budget to creating a Look A Like Young Adult Facebook segment. In the following weeks, the results were reviewed and changes were made to extend some campaigns while turning others off.
The overall results from all channels created an overall ROI of over 1400%!
Other key takeaways:
- Increased segmentation from 12 customer segments in 2015 to 52 customer segments, totaling 970,000 emails sent.
- Advertised on Google and Facebook to 20 segments.
- The #1 segment on Facebook was the Young Adult Look A Like segment meaning customers that had similar attributes to their Young Adult segment. This segment accounted for an additional reach of almost 2 million people.
- The top 3 Google campaigns were recent passholders from all life stages.
- Young Adult segment accounted for 54% of total Facebook revenue
- The Stevens Pass Video created for Facebook accounted for 23% of overall Facebook Revenue
Through database segmentation and omni-channel marketing, Stevens Pass could deliver the right message to the right audience where they were listening. Their CDP software allowed them to gain valuable insight into their data and drive relevant marketing campaigns to increase ROI.
Interested in learning more about what CDP software could do for you? Contact us.
January 19, 2017
Reflections on the CDP Industry Profile Report
By David Raab, CDP Institute
CDP Institute yesterday released its Industry Profile report, presenting core statistics including revenue, number of customers, funding, and employee counts. It’s a quick read: at 1,300 words, not much more than a longish blog post. That’s on purpose, because we wanted to focus attention on the numbers themselves. Because this was the first time they’d ever been assembled, they were the news. (You can download the report here.)
But now that report itself is published, it’s time to step back and consider what it means. I’ll share several observations, and would love to hear what others think.
The biggest surprise was simply the size of the industry. The headline figure for the report was a projection of $1 billion CDP revenue by 2019. That’s an eyecatching figure but it’s just a prediction. What's more important is that we estimate actual revenues for 2016 at just over $300 million. To put things in perspective, that’s the size of the marketing automation industry in 2011, only one year before Oracle bought Eloqua and began to end the era of independent marketing automation vendors. I don’t see the CDP industry as anywhere near that type of consolidation, but its size suggests much broader acceptance than you’d expect for a band of plucky startups.
A related surprise is that most of that revenue doesn’t belong to vendors who evolved into CDPs from previous incarnations as tag managers. (See this Customer Experience Matrix blog post for a more detailed discussion.) Twothird of the revenue comes from vendors who were CDPs from the start, in the sense that they built unified, persistent customer databases. Again, this shows the core CDP concept is already better established than many had realized.
A third surprise is the diversity of the industry. As the Profile report mentions briefly, CDP vendors came from three different origins (tag managers, campaign and personalization systems, and customer data systems). All have converged on the CDP model but there is still great variety among systems from the different groups and even within the groups. This is one big difference compared with marketing automation systems, which very quickly adopted a standard set of functions. It’s one reason the CDP industry isn’t ready for consolidation. At this stage, many different types of CDP are competing and we’ll see if one configuration becomes dominant.
All these surprises illustrate another truth: that CDPs are still unfamiliar to most marketers and marketing technologists. That’s frustrating in some ways, but it's good news for the industry in the sense that many potential buyers have yet to enter the market. One reason we project continued strong growth for CDPs is that we expect many of those people to buy once they learn what CDPs can do for them. Educating those buyers is the mission of the CDP Institute and we look forward to the challenge. Still, it's nice to find a report that shows the wind is at our back.
January 16, 2017
Xanterra Parks & Resorts Boosts Campaign Performance up to 839%
By Carol Wolicki, RedPoint Global
Xanterra Parks & Resorts may be the travel and hospitality industry’s sweetest secret. The company owns or manages 34 hotels with 5,600 rooms, manages 8 million acres of land, and owns six luxury yachts, 89 food and beverage outlets, seven golf courses, 16 swimming pools, four marinas, and four stables. With all of these properties comes the incredible opportunity to cross-sell, heighten one-to-one customer engagement, and ultimately grow their business. With the help of RedPoint Global’s proprietary customer data platform (CDP), Xanterra boosted marketing campaign performance as high as 839% and enjoyed year-over-year revenue improvement of 91%. Their story is not unlike many of our customers, who come to us seeking a 360-degree view of their customers as a foundation to enhanced customer engagement and strategic business improvement.
Xanterra wanted to move towards one-to-one marketing that reflected the unique passions and preferences of each guest, creating valuable synergies within and across all the company’s properties. The entire project started with getting the data right, which turned out to be a major challenge because Xanterra’s diverse portfolio of properties relied on more than 100 sources of customer data – each with unique characteristics and complexities. Data from its multiple brands lived in silos and couldn’t be consolidated for marketing action.
The first step on the road to one-to-one marketing was for Xanterra, with RedPoint’s help, to unify its data into a common database. Building on this foundation, Xanterra next aimed to segment its customers, and map their journeys through the customer experience. With unified customer data, segmentation, and guest mapping in place, Xanterra could leverage many new opportunities to drive value, improve marketing performance and operating efficiency, and enhance the guest experience.
Xanterra recognized RedPoint Global’s unique ability to deliver a complete end-to-end solution – from data to insight to action – through a unified platform. Thanks to RedPoint’s platform, Xanterra could connect virtually any data source, channel, or media through ready-to-use adapters and built-in messaging orchestration.
According to Andrew Heltzel, director for Marketing and CRM at Xanterra, “RedPoint engineered a data intake solution that allowed us to keep basically all our existing IT infrastructure 100% intact. We didn’t have to change a single reservation platform to get all our data into our common database. We didn’t have to standardize systems or data entry processes across all of our businesses, or address inconsistencies, or overcome a lack of connections across our enterprise. Instead, by leveraging RedPoint’s data management strengths, we quickly got a 360-degree view of our customers.”
RedPoint’s CDP technology handles all cleaning, standardization, and enrichment with more than 300 third-party appended attributes. “That took us from ‘unknown’ or ‘partially known’ profiles to very strong profiles, known both demographically and psychographically,” said Heltzel. “Plus, for the first time, we have visibility across all of our brands’ data. Now, we know if you’ve cruised with us on Windstar, if you’ve also stayed with us at Glacier National Park, and if you’re also a prospect for us now at Austin Adventures. This immediately helped us create more meaningful, targeted, and timely messaging.”
Xanterra can now tell which brands organically share common types of guests and successfully market across them. This practice is aided with the seven core segments that the RedPoint platform “bubbled up,” which allow Xanterra to create robust personas for and market across their customer base. RedPoint also made it easy to track lifestyle changes that shift customers from one persona to another – potentially a key driver of campaign lift.
Putting comprehensive data and personas in place enabled Xanterra to map out every digital and analog guest touch for most of its brands, including emails, call centers, and front desk interactions, and to align those touches with the consumer’s emotional state at the moment. With this data, Xanterra centrally manages post-book, pre-arrival communications and can lessen the burden on local marketing teams. Xanterra’s marketers also use RedPoint’s visual tools to construct automated behavioral-based campaigns that encourage prospects to share the information.
With the help of the RedPoint Convergent Marketing Platform™, which includes our proprietary CDP technology, Xanterra was able to develop far more relevant offers, featuring unique and targeted imagery, content, and subject lines through a blend of segmentation, A/B testing, and its fresh 360-degree customer view. These new campaigns earned 73 cents per email, on average, versus 8 cents for previous average campaigns. This was an improvement of 839% once the RedPoint system was fully deployed, and included growth in every segment.
Xanterra is now focused on building deeper two-way links with their call centers and leveraging their data to help customer service representatives have more personalized, relevant, and profitable conversations with prospects. This is a natural outgrowth of their digital personalization efforts, which resulted in 91% year-over-year revenue improvement and 103% year-over-year increase in transactions.
With the help of RedPoint, Xanterra conquered its customer data challenges and built more personalized and relevant marketing campaigns. They now intend to move deeper into omnichannel marketing with the aid of RedPoint’s powerful social media toolset and pursue new B2B opportunities, extending their business from a historical B2C emphasis.
January 9, 2017
Forget Skilling Up – Marketing Technology Needs to Come Back Down to Earth
By Anthony Botibol, BlueVenn
Over the last few years, many marketers have noticed a transitional change in their roles. Rather than focusing on the more creative aspects that have traditionally defined marketing, they are instead transforming into data scientists; analyzing reports, cleansing data sets and building in-depth profiles for each of their customers.
But are these new data wrangling responsibilities what marketers signed up for when they started their career? According to a new report from BlueVenn, not only do over 40% of marketers have to digest 21 or more sources of data, many are now spending as much as 80% of their day working with it.
Having to adapt to this new data-led approach to their job – while maintaining the more creative components that drew them to marketing in the first place – is a challenge that many need to understand better. Not least the MarTech industry.
To learn more, we surveyed over 200 senior B2C marketers to determine how they are handling the ever-increasing deluge of customer data, and then identify their biggest struggles.
Bringing multiple data sources together in order to form a coherent Single Customer View (SCV) has long been regarded one of marketing’s ‘holy grails’. Yet unified customer data is still a long way off for many – as many as 82% of marketers have yet to achieve an SCV, with 62% saying they have to deal with customer data from numerous disparate silos.
High volumes of data and disjointed data cause other problems, too. For example, over half (54%) of marketers claim that poor quality data has damaged their ability to provide more targeted campaigns, while 27% claim that they lack the skills to analyze it anyway.
So, given that we found 72% of marketers believe data analysis is the most important skillset for them to acquire over the next two years, what can be done to improve the situation? Do marketers, as the marketing press so frequently suggest, need to re-skill into data science? Or is this expectation misplaced?
If we’re being honest, the majority of marketers aren’t clamoring to retrain in data science in order to continue doing their day-to-day jobs. That said, they are equally reluctant to hand precious marketing data over to others. The IT department may have the analytic skills, but do they have the marketing savvy to put the data to best use? An external data agency may have them - but they also come with a considerable cost.
If customer data needs to stay with the marketing department then there needs to be a middle ground. Out conclusion was, rather than turning marketers into data scientists, MarTech vendors need to step up. After all, who are these marketing tools really for?
This is where the case for a Customer Data Platform can be made. By both removing the need to analyze data by hand and creating a single interface from which multiple data sources can be managed, marketers can do much of the work of data scientist using automated data management processes.
True, we might not yet be at a stage where marketers can press a single red button and the platform does all the data crunching for them. Nevertheless, as far as resolving many of the biggest issues they face – without extensive retraining – a CDP sounds like the compromise that our report suggests marketers are crying out for.
January 5, 2017
Do Marketers Really Need a Single Customer View?
Author: David Raab, CDP Institute
The Customer Data Platform industry is doing very well, thank you: nearly every vendor I speak with doubled or tripled their business last year. But I still find myself asking why so few firms have a CDP in place. After all, nearly every marketer says unified customer data is important while only a few have it available. Why haven’t they all bought a CDP to fill the gap?
Maybe marketers aren’t as interested as they claim. Either they’re simply giving the "right" answer on surveys or they really want a unified view but think other projects are more important. This may be a factor but it’s hard to believe that so many marketers would speak one way and act another. And surveys that ask explicitly about obstacles don’t find that priorities are the main issue. The more common answers cite budgets, existing systems, and organizational roadblocks. (See, for example, the CDP Institute’s recent survey on the topic.)
Of course, CDPs are specifically designed to overcome these obstacles. They are cheaper and faster to build than conventional data warehouses, can easily ingest data from and send data to nearly any system, and let marketers work with little support from in-house IT. So one conclusion I draw is that too few marketers are actually aware of CDPs as an alternative that solves those problems. Building that awareness is one of the main reasons the CDP Institute exists.
But I don’t think awareness is the entire problem. CDPs aren’t the only way to share customer data. Enterprise data warehouses are one alternative, although adequate ones are rare. It's more common to find companies where a single system supports all channels: 19% of respondents in the Institute survey and similar numbers in other surveys. That figure is surprisingly high but it includes small businesses and online-only businesses where a single system comes naturally.
Still more common are firms that move data directly between systems (almost 40% in the Institute survey). These probably use a combination of custom-coded integrations and integration platforms like Jitterbit, Mulesoft and Zapier. I’d argue this means the integration platforms are the main real competitors to CDPs.
The big appeal of the integration platforms is they can be deployed incrementally. Marketers can build data flows to support specific purposes, thereby tying each investment to a defined benefit. They can work around limits in existing system capabilities by only building flows that support deployable actions. They can only connect systems that need to be connected, avoiding the need to plan for more general solutions. They avoid creating another new database, something many are reluctant to do. Cost and dependence on IT are limited.
In short, integration platforms offer a way to share data in a package that is less intimidating than a CDP. This is probably why so many marketers haven’t felt an urgent need to go further.
But integration platforms eventually fall short. As more systems are connected, the number of connections increases sharply: linking three systems directly requires just three point-to-point connections, while linking six systems requires fifteen connections. (To be fair, if all systems connect to a common hub, each new system adds just one connection, same as with a CDP.) If data from each system is stored in every other system, redundancy also increases with each new system. This is a problem that even a hub won’t solve. With redundancy comes danger of inconsistency, made almost inevitable by the fact that different systems will update at different intervals and many will not be able to post data in real time.
Replication is even more challenging for processes that require keeping historical data, which many operational systems are not designed to retain. Such processes include trends, aggregation, and the all-important management of persistent, cross-channel customer identities. Attempting to duplicate the processing logic for each these in every system is almost laughably difficult. Replication is simply impossible for data types that a particular system may not support.
These problems all become more critical as companies move beyond simple uses for shared data, such as personalization, to more advanced uses such as predictive modeing and cross-channel coordinated treatments. At some point, the integration platform approach simply collapses under the pressure. Then marketers must either avoid adding the stress of new applications or move to a shared central database – that is, a CDP.
One conclusion from this could be that farsighted companies should use a CDP from the start. This is the most efficient approach since it avoids investment in an integration platform they’ll eventually discard. This will make sense in some cases – especially in enterprise environments where even an initial integration deployment would be complicated.
But many firms that are just dipping their toes in customer data sharing will find that an integration platform is good place to start. For those companies, the pertinent lesson is they should recognize they’ll eventually need a proper CDP and look for opportunities to move in that direction as quickly as possible. This minimizes investment in the temporary solution and lets them build the CDP itself incrementally. If a company’s environment is in fact simple enough that it never outgrows the integration platform – that’s okay too. What’s important is to have the solution that best for each situation, not to push everyone into the same box, even one as pretty as a CDP.
December 27, 2016
CDP Differentiators: Identified vs. Anonymous Data
Author: David Raab, CDP Institute
Many systems assemble customer data. This is confusing for marketers who often have a hard time understanding how the systems differ. Here’s a look at one important distinction: whether a system works with identified or anonymous individuals.
Identified individuals are known by name or an identifier that can be linked to a name, such as phone number, email address, credit card, or bank account. Anonymous individuals can’t be linked to a name but may still have an identifier that can be tracked over time, such as a browser cookie, device ID or account log-in. This means that even anonymous individuals can have a customer profile with detailed information. But the anonymous profile is typically limited to one source: without a personal identifier, there’s no way to link it to data in different systems that's about same person.
In recent years, the most important anonymous identifiers have been Web browser cookies. These are deposited on a computer during a Web or email interaction and can be read during subsequent interactions. If the visitor identifies herself by filling out a form or logging into an existing account, the cookie can be linked to her identity. But most site visitors don’t identify themselves, so their cookies remain anonymous.
The primary use of anonymous cookies has been to create advertising audiences. These are built by linking each cookie to attributes derived from Web behaviors such as content consumption. Audiences are built by selecting cookies with specified combinations of attributes. Technical details differ, but you can safely visualize this data as a spreadsheet where the first column holds the cookie ID and other columns contain values for attributes. Key design challenges in building these systems include handling millions (sometimes billions) of cookies, allowing thousands of attributes, easily adding new attributes, and selecting records very quickly.
Identified data is a different story. Because the data can be linked to a specific individual, the system often includes data from different sources. Each source may have its own identifier: a cookie ID from a Web site, an email address from CRM, a device ID from a mobile app, and so on. To accommodate this, the system needs a central “spreadsheet” that lists all the identifiers associated with a particular individual. Other “spreadsheets” hold the actual data from the source systems: that is, Web page views, purchases, phone calls, emails sent, etc. Each row on the central spreadsheet represents one individual and the columns are the different identifiers. On the other spreadsheets, each row represents a particular item (page view, order, phone call, etc.) and the columns are the details about those items (date, page name, product purchased, price, etc.). The columns are different in each spreadsheet, reflecting attributes of the items they represent. But every spreadsheet needs a column with a customer identifier. This is what the system matches to the identifiers in the central spreadsheet when it needs to create a unified customer view by assembling all data associated with an individual.
The actual technologies involved with anonymous and identified data involve more than simple spreadsheets. But you can still safely assume that systems for identified data are more complex than systems for anonymous data. Challenges facing systems for identified individuals are different as well. Key identified data issues include storing and accessing different data types, making it easy to add new sources, and combining similar data that comes from separate systems.
The different requirements for anonymous and identified data mean it’s hard for one system to do both well. Some vendors don’t even try. Others use a single technology they feel works well enough. But most who try to do both run what are essentially two different systems, each optimized for one application. They then call on the appropriate system as needed. These vendors differ in the underlying technologies, how much data is actually shared by the two systems, how data in one system can be accessed through the other (if at all), and how the data is presented to administrators, users, and other systems. Many vendors also improve performance using supplemental technologies such as indexes and summary tables.
This practical complexity is what muddles the distinction between Customer Data Platforms (CDPs) and Data Management Platforms (DMPs). Most DMPS were originally designed to handle anonymous cookie pools for advertising, using some variant of the “single spreadsheet” model. Most CDPs were designed to manage identified data from multiple sources, using the “multi-spreadsheet” approach. But many DMPs have been extended to handle identified data and many CDPs have added support for advertising audiences. The technical details of how they do this vary widely, but it’s those technical details that determine how well they succeed. So there’s no value to generalizing about which solution is theoretically better.
What does have value is being a smart buyer. This means you should:
- recognize that managing anonymous and identified data are fundamentally different applications
- look closely at how any system that does both actually works, keeping an eye out for different internal mechanisms
- test handling of each data type separately. A system that manages one data type well doesn’t necessary do an equally good job with the other.
December 20, 2016
Customer Data Platform Use Case: How to Turn Post-Purchase Data into Insights and Income
By Laura Hazlett, Ascent360
Data is one of the most valuable assets to a company. It provides insight into what people are buying, how much they are spending, and most importantly, who they are. But getting a clear view into this data is often a challenge. Only so much can be pulled out of each island of data you have. Your Point of Sale system will show you what is being purchased and how much you are earning while your email sign up will tell you who is interested in your company. But how do you know if those email subscribers ever turn out to be customers? A Customer Data Platform (CDP) can help you gain valuable knowledge from your data. By combining all data sources into the CDP, analytics can then be run to provide key statistics such as Lifetime Customer Value, Customer vs Prospect ratio and RFM Score. From there, segments can be built to execute more effective marketing campaigns.
Thinking through how exactly to use this data and how best to gain value from it can be difficult. The best way to look at how valuable this type of information can be, is to look more closely into a use case. Below we will walk through a customer journey to better understand the benefits of a Customer Data Platform.
CDP Use Case: Post-Purchase Customer Stream
For this use case example, you are the owner of a jewelry chain who has recently purchased software to better track and understand your customers and transactions, also known as a Customer Data Platform. Yesterday, at one of your stores, a customer purchased a necklace. After looking through many options, he decided on a $200 necklace. At that amount, it isn’t a transaction you would typically look further into. However, because his purchase comes through your Point of Sale system and into your CDP software, you are alerted that while this purchase was only $200, his lifetime value at this store location is $12,150. He is immediately tagged as a high value customer and goes into a high value customer marketing stream you have easily developed.
This automated email is sent to high value customers the following day, thanking them for their recent purchase. The email is also dynamically populated with the store managers signature. It is not a sales email, simply a loyalty message thanking the customer for their purchase.
Looking through the customer journey, you decide you want to see how many days between the initial purchase it takes before a repeat purchase is made. In the case of your jewelry chain, you see that the typical repeat purchase is about 90 days after the initial purchase. You present this information to your marketing team who then decides to implement a post-purchase and cross-sell marketing campaign to go out 85 days after a purchase. The customer bought a necklace? Great, promote the matching earring set in your post-purchase email!
Since you have set up the automated email it will check the data each day to see who is a high value customer that purchased the prior day, send out the personalized thank you email, and then the 85-day post purchase email. After implementing this campaign, you look through your CDP analytics and realize that while mainly very successful, there are still some customers that are not opening or clicking the email you are sending them. You realize that some customers need a different journey outside of email. Recognizing these people are important, you decide to create a segment of people that will receive a direct mail piece if they have not opened the email within a week of being sent. Your CDP software will check this segment to see they have come back to your store after receiving this direct mail piece.
Importantly, your CDP software can attribute revenue to the marketing campaign from the email and direct mail campaigns because the system is pulling in all of the point of sale data. Overall, you see a high marketing ROI from this post-purchase stream as well as high customer retention.
CDP: The Benefits of Customer Data
Without CDP software, this type of analysis and execution would likely not be possible. Combining your data into a single customer database can allow you to gain valuable insight into your customer data and execute highly targeted marketing strategies to customers and prospects leading to increased revenue. All companies have data, but it what you do with it that makes the difference and can set you apart from your competition. Because after all, data is just data until you do something with it.
December 15, 2016
Retailer Wildfang used a Customer Data Platform to Boost Revenue by 73%
Author: Julia Farina, Lytics
Wildfang, a quickly growing clothing and accessories retailer, is a brand that is all about understanding its customers. They serve an extremely diverse audience, ranging from sports enthusiasts, fashionistas and career women to A-list actors and entertainers. Wildfang understands their various audiences and, in-store, their associates can immediately assess which products will resonate with individual customers as soon as they walk in.
However, the challenge was how to extend that personalized experience to their e-commerce website. Wildfang decided to use a customer data platform (CDP) to help them identify their diverse audiences online, personalize their messaging to them and generate more revenue.
Wildfang chose Lytics because its customer data platform allows companies to personalize their marketing (e.g, website, online ads, email, etc.) by better managing data about customers and taking action from both integrations (e.g., Facebook Ads, email, etc.) and within the product itself (the Lytics website personalization product).
How they did it:
STEP 1: Resolve identities and access a holistic view of each customer by feeding cross-channel customer data (location, email, subscription, social, offline and online purchasing, web, and mobile) into one centralized hub.
STEP 2: Take advantage of behavioral and predictive insights and create dynamic, cross-channel audience segments.
STEP 3: Create custom web campaigns that integrate with the look and feel of their website.
USE CASES AND RESULTS
Personalized messaging and recommendations
Instead of one-size-fits-all messaging, Wildfang used a customer data platform to deliver distinct language and recommendations on their website that would resonate with customers who came from select partner and affiliate websites. They saw a 10% click-through rate as a result.
Wildfang used a customer data platform to leverage in-store and website data to identify customers who qualified for lifetime free shipping. These customers were greeted with website personalization to remind them of the offer. Conversion rates have skyrocketed — up 80% year on year, with targeted messaging a key factor.
Better User Experience
While unknown visitors received a website prompt for one retailer’s newsletter signup, Wildfang used a CDP to suppress prompts for known customers (who had already provided an email address). This simple fix resulted in a better user experience for known customers and reduced bounce rates.
By using a Customer Data Platform, Wildfang eliminated unnecessary advertising spend by not retargeting customers who were already engaging with them over email and reserving online win-back ads for individuals who unsubscribe from emails.
The overall result of using a Customer Data Platform has been a better customer experience and a considerable increase in revenue for the popular retailer. Emma McIlroy, CEO of Wildfang explains: “Previously, we didn’t have a way to target members from our most important cohorts on our website. With Lytics, identifying and messaging this group on site is easy. This opportunity equals immediate and significant revenue for us and helped us grow our business by 73% in one year.”
December 12, 2016
Research Roundup: New Surveys Shed Light on Customer Data Platforms
Author: David Raab, CDP Institute
Several recent surveys touch on Customer Data Platform issues. Each has its own focus but all address the relationship of unified customer data to unified customer experience – a topic that isn't as simple as it might seem.
Myths of Marketing Survey from BlueVenn (download here).
BlueVenn polled 202 B2C marketers about their use of customer data. Marketers said they were in charge of data at 60% of the companies, compared with 27% who said it was managed by IT. They also reported that customer data comes from many systems (40% have more than 20 data sources) and takes a lot of time (nearly 30% spend more than half their time on analyzing data). That answer helps to explain why time was the most commonly cited obstacle to creating more targeted campaigns (41%), followed by data cost (34%), data access (28%), data skills/knowledge (27%), and other data-related issues.
BlueVenn’s major conclusion was that marketers are spending too much time on data-related work and that better systems are the cure. In their words: “We here at BlueVenn believe that the perceived skills shortage as a barrier to achieving a Single Customer View, real-time omnichannel marketing and customer journey analytics, is in fact the BIGGEST myth of marketing. It is not the fault of the marketer that they cannot achieve their strategies –the blame should in fact lie at the feet of marketing technology providers.”
Regarding unified data vs unified experience: 28% said they had connected all channels to create an omnichannel experience while just 18% reported having unified all data sources into a single customer view. This suggests they either have direct connections between systems or share identities without a complete customer profile. So, if you’re thinking the single customer view is a prerequisite to omnichannel experience, think again.
The State of MarTech and AdTech: Customer Journey Investments in 2017: The Agency Perspective from Kitewheel (download here).
Kitewheel asked 134 agency marketers about their use of marketing technology. Key findings were that agencies see great demand for customer journey projects and 75% are investing in technology to do them. These investments are despite the fact that respondents already have more tools than they use (just 6% use all their existing tools weekly while 72% use fewer than 40% of their tools weekly.) By far the largest barriers to tool use were lack of expertise and training (66% combined). The gap is preventing nearly two-thirds of agencies from delivering projects their clients want.
In Kitewheel’s words: “Agencies are cautious in their ability to build the capabilities to deliver a customer journey in 2017. 64% don’t expect to be able to deliver real journeys until 2018 or later. Primary reason for this caution is the lack of skills and tools (54%)”
On unified data vs. experience: 46% said they can currently do omnichannel personalization while just 12% said they could do adtech/martech unification. So, even more than before, we see cross-channel treatments without unified data. Respondents did rate adtech/martech unification as the top capability needed for journey programs, while omni-channel personalization ranked fifth.
The Impact of CRM on Customer Experience from Usermind (download here).
Usermind surveyed over 500 people who either had personal super-admin access to a CRM system or supervised an individual with super-admin access. Perhaps not surprisingly, this group concluded that CRM had the greatest impact on customer experience than other systems, including data warehouses/other customer data platforms, marketing automation, social and Web traffic data, or third party data. Less easily explained away are conclusions that companies with more satisfied customers (as reported by the respondents) were more likely to use CRM as their primary customer system of record, to have a dedicated customer experience team, and to have fully digitized business operations or a digital transformation strategy in place. This group also uses many systems: 33% reported having more than 20 systems impacting customer experience.
The survey also found that companies implementing customer journeys with workflow tools inside each application were much more likely to report very satisfied customers than companies using internally developed solutions or “integration platforms” (which Usermind did not define). On the other hand, the capabilities needed to improve customer experience covered a wide range of cross-system functions: data mapping to identify customers across all applications; adaptive system and data integrations; automated workflows; defined customer journeys that span applications and teams; and, a unified view of customer data”. So the respondents feel that a siloed CRM isn’t enough.
Usermind’s advice seems to be that companies should use existing tools to build better experiences instead of waiting to build a unified view. This is why resources like dedicated customer experience teams have more impact than a data warehouse or CDP. In Usermind’s words: “Traditional integration approaches create challenges for your team, and roadblocks to delighting your customers. Point-to-point integrations don’t pass valuable customer context along with your data. And whenever your source systems or schema change, your integrations will break. Data alone won’t deliver a better customer experience — your analysis needs to be translated into action. If you use a customer engagement hub or journey orchestration to deliver a one-to-one, real-time customer experience, you can avoid the pitfalls of traditional, labor-intensive approaches.”
Regarding data vs experience: 64% of respondents said that data mapping to identify customers across all applications would improve customer experience, compared with 51% who cited customer journeys that span applications and teams and 38% who cited a unified customer view. Again, respondents are saying they can deliver coordinated experiences without assembling a central database.
From Theory to Practice: A Roadmap to “Omnichannel” Activation from Winterberry Group (download here).
Winterberry spoke with more than 100 executives at advertising, marketing, media and technology companies. The topic was audience (i.e., customer and prospect) recognition in particular and omnichannel strategies in general. Key findings were that 73% saw recognition as a moderate or higher priority but only 9% were able to recognize customers across all channels. Fewer than 7% were satisfied with their ability to leverage customer data across channels. The survey distinguished cross-channel recognition from omnichannel marketing programs, but found for both that technical improvements such as integration were more important than organizationsl issues such as collaboration, priorities, or staff skills.
In Winterberry’s words: “What’s the next frontier of omnichannel marketing? Panelists said the next great leap forward would be driven from the inside, with the potential alignment of internal business processes and technology infrastructure likely to do more to advance their omnichannel efforts in the years ahead than any other initiative.”
On data vs experience: the survey found that 40% felt they did cross-channel orchestration extremely or fairly well while just 32% said they do audience recognition across all or most channels. Once more, we see that orchestration is apparently possible without the data sharing that recognition makes possible.
Each survey offers useful insights related to its primary topic. But, for me, the most important message is the one they all share: omnichannel programs can be delivered without building a central database. That may seem an unlikely conclusion for the Customer Data Platform Institute blog, but let's be clear. It doesn't mean that central databases are unnecessary. It only means you can do some omnichannel work without them. One intermediary step is cross-channel customer recognition, which requires building cross-channel identities (presumably in a central system) and sharing them with experience-delivery systems. The next step is to expand the central system by adding more data and sharing it. This can be an incremental process as the central system gains access to more sources and as marketers find uses for additional pieces of information. A complete customer view is still the long-term goal because it enables the richest marketing programs and deepest analysis of customer behaviors and program results.
Centralized orchestration may be another intermediate step. An orchestration engine needs a unified customer view, which it might create for itself or read from a separate customer database. Either way, the orchestration engine's role is to provide execution systems with consistent customer treatments. This replaces relying on each execution system to make its own decisions. Although orchestration is not part of CDP definition, it's important to recognize that many CDPs do include such functions because they add value for marketers. And value to marketers, not conformance with a definition, is what really counts.
December 8, 2016
Best Practices for Customer Data Management in the Multi-Screen Era
Authors: Michael Katz and David Spitz, mParticle
When it comes to customer data, we’re undergoing a period of massive change. eMarketer reports there are 2x the number of Internet connected devices today as there are people; by 2020, that number will be almost 5x. Meanwhile, according to the Winterberry Group, organizations use on average more than a dozen distinct SaaS tools, with some using as many as 30 tools. That’s a lot of data sources and outlets.
The classic 3 V’s of big data, volume, velocity, and variety, still apply today, but in different ways, shapes, and forms than in the CRM and web eras. With mobile, there is much more data being created passively via the radio, and thus generating and transmitting all the time. The variety is much greater than even on web because of all of the device telemetry data, the geospatial element, as well as the native data points. The velocity is also unlike anything we’ve ever seen since data is being generated with every single movement and swipe.
But what makes the current moment so challenging isn’t just the data itself. The software deployment model, how data gets onto these remote devices, and, most important, the end use cases are all different, too.
The dominant interaction types on most of these new devices is apps, and those apps themselves are fundamentally different from browsers. They are compiled code and shipped software that live locally on your phone, TV, Kindle, etc. They are decentralized from the browser, with no agile development. On the other hand, because native apps can access and leverage device functionality, they can more readily access features like new cameras, accelerometers, barometers, glass to support 3D touch. All of this innovation in hardware has the potential to create a much richer experiences for end users.
New data types such as push tokens and exceptions never existed in web environments yet are now paramount for success. Conversely, all this incremental data collection, when not managed properly, can add significant overhead, risk, and complexity into an app experience.
Taken together, these changes have serious implications for marketers. For example, in a world where the mobile device is now the hub for every step in a customer’s journey, marketers need converged, multi-purpose data platforms, not just ad platforms masquerading as customer data platforms, to take advantage of “through-the-line” opportunities that exist on mobile. At the same time, people are multi-tasking, engaging with customer support reps, and, yes, still buying in retail stores, and all of that needs to be taken into account, too.
Data convergence in the multi-screen era doesn’t mean necessarily that we’ll have an “all in one” monolith for ad serving, email, social, web, and so on, but it does mean that these efforts will be joined at a business and data level like never before.
Re-Engineering Data Management for the Multi-Screen Era
In the face of these challenges, companies need to change how they handle data at every step along the way, yet they can’t just hit the pause button on business as usual and overhaul everything in order to do that.
Here are four steps companies need to take in order to not just survive, but thrive, in this new era:
1) Define Your Data Strategy
Defining a strategy is typically the first step in any endeavor, and data management is no exception. To define your data strategy, you must:
● Identify your goals: Do you want to improve growth, retention, audience insight? Whatever it is, be clear.
● Map your data: Map your data to your goals by considering factors like KPIs, how you’ll think about segmentation, and what your engagement triggers will be.
● Create naming conventions: Your naming conventions should be clear and simple to understand. Far too often, organizations use inconsistent and/or difficult to understand naming conventions, and that can wreak havoc down the line.
● Build a hierarchy of user IDs: As we move away from anonymous web tracking, take advantage of the data that’s available -- including identities -- to develop an omnichannel understanding of customers.
● Outline your use cases: Determine how you will use the available data to achieve your goals by clearly outlining your use cases.
● Align use cases with technology: Ensure a clear alignment between your use cases and your technology stack and consider what your stack needs to look like in the future to help solve for your core business challenges.
● Remember privacy: Privacy is a trade-off of personalization, and you must have the right privacy controls in place to respect your customers’ requirements.
2) Optimize Data Collection
Your data collection process can impact both your users’ experiences and your ability to take action. With that in mind, your data collection process needs to:
● Do no harm: Collect data once and do so in the right way in order to avoid bloating your app with unnecessary code that can degrade the user experience.
● Be consistent: Accept no compromises in capturing data consistently across all screens, but remember to account for the native data types on each. Failing to account for these native data types can lead to an 80/20 scenario where you have 80% of the data but are missing the 20% that is native to each screen, and it’s that 20% that typically drives the overwhelming majority of value.
● Make data capture use case agnostic: Step back and think about the bigger picture. While execution should be highly use case specific, having a single source of truth that can support diverse use cases will stand the test of time.
3) Use Data Controls to Create Value
The value of a data platform should go beyond the sum of its parts. The best way to inject additional value into the stack is to enable greater control of data through filters, enrichment, and segmentation. To do so:
● Be diligent about converting signal to noise: Don’t pollute downstream systems with an abundance of data. Limiting what you send makes your analysis easier and your costs lower.
● Merge identities around a single customer view: Augment direct matches with data science, but only after maximizing user identity matching.
● Enrich data to get a more complete view: Bring data feeds in from all of your SaaS tools as well as from relevant third party tools.
4) Simplify Data Connections
Finally, you need to simplify the process of operationalizing data to all the different endpoints across the entire lifecycle of your business. This simplification requires you to:
● Empower end users to move quickly
● Sync continuously to avoid wastage
● Bring data back “in” from executional tools to learn and optimize
When thinking about data connections, you also need to keep in mind the fast pace of change in today’s environment. The vendors leading the pack can change at any time. One of the great benefits of having a data platform detached from execution is the ability to add execution and analytics tools as needed onto a central data hub, as well as remove those wants that are no longer needed without losing historic data.
Thriving in the Multi-Screen Era and Beyond
In the multi-screen era, a reactive, tool-centric approach is simply not an option. It adds significant cost, complexity, and risk to your business. Ultimately, you end up with a tangled web of client and server side integrations that leads to unnecessary overhead and creates user experience, privacy, and security challenges.
That’s why you need to spend time thinking about all of your data use cases across marketing, analytics, data science, attribution, CRM, help desk, you name it, and build a data strategy to support them holistically. Following the above-mentioned steps will enable a truly 360-degree view of the customer that’s not only insightful but also meaningful to the business.
Michael Katz and David Spitz are, respectively, CEO/Co-Founder and CMO of mParticle. This blog post was adapted from their October 2016 webinar of the same title.
December 6, 2016
Subaru UK Distributors IM Group Reap the Benefits of a Customer Data Platform
Author: Anthony Botibol, BlueVenn
As the sole importer and distributor of Subaru cars in the UK, IM Group coordinates the activities of over 200 dealerships around the country.
In 2011, IM Group set out to centralize the marketing for all its dealerships, providing effective, direct messages to existing and potential customers via a unified Customer Database Platform.
Traditionally, the dealerships had the main responsibility for customer records, which created a significant problem. They often lost track as cars were sold to new owners, or serviced at locations other than the original dealership. Moreover, there was no framework, or will, to share data with each other, or with central franchising.
It therefore made it impossible to maintain a reliable database of customer contacts. This contributed to the issue of multiple and inaccurate records for individual customers, as well as confusion over which dealers ‘owned’ the data, where customers had used more than one dealer. All of this undermined the potential for high quality direct marketing to existing and prospective customers.
The data had to be cleaned and streamlined, as well as integrated with information from potential customers who had visited the Subaru website. The company wanted to send email newsletters with content tailored to segmented groups to prompt enquiries, encourage test drives and ultimately convert them to sales – while also monitoring the conversion rates at different levels and running analytics on dealerships’ performance.
IM Group addressed these issues by turning to the BlueVenn Customer Data Platform (CDP), for analytics and a Single Customer View.
The BlueVenn solution is a purpose-built database containing a single record, which can be linked to all other information, for every individual the company wishes to contact. For example, to match vehicle tracking ownership third party data with first party data, to identify owners who had sold their cars to stop service reminder communications and irrelevant offers. It helps to create a customer view and perform tasks such as segmentation, profiling and campaign performance management and acts as the platform for all the marketing intelligence.
The solution has enabled IM Group to handle the online marketing for all of its dealerships, ensuring a more coherent national strategy, and make it possible to monitor the performance of each franchise in the market.
“Combining data from the dealerships and website users greatly increased the number of sales opportunities we could identify. Compared with the hundreds of enquiries per month generated by our newsletter, the company can now identify thousands of website users and it is possible to see exactly which parts of the site they have visited, and what interests them most. This helps us to better target prospective customers with content tailored specifically to the interest they have shown,” says Howard Ormesher, CRM Director at IM Group.
IM Group is now able to filter content to determine which contacts should receive offers, dealership news or national newsletters, depending on contact preference. Additionally, dealers can opt to include their own offers such as reduced pricing on servicing or discounted accessories for example.
Equally important is that data on customer responses is fed back to BlueVenn. This gives IM Group a much more reliable picture of the market, supported by evidence, rather than the anecdotal reports from dealerships. It also provides an early indication of whether or not a marketing campaign is working, and IM Group can measure factors such as opens, clicks, resulting web sessions, test drives and sales for every email it sends.
The overall result has been a considerable increase in conversion rates: the number of enquiries leading to test drives has risen by a factor of 3.2 and test drives to sales is up by 1.6.
“We now have a CRM infrastructure that is constantly fed; every day we take new feeds of data, the system is rebuilt and campaigns are triggered automatically, which drive in new content. And the culture of the organization is changing; there’s an acceptance that this is beginning to set the agenda and help the business to improve,” said Ormesher.
December 5, 2016
Persistence of Data in Customer Data Platforms
Author: David Raab, CDP Institute
The CDP Institute’s definition of a Customer Data Platform describes it as a “persistent, unified customer database”. Most of the CDP discussion focuses on the “unified” bit, since collecting data from different sources and linking it to cross-channel identities is a huge challenge. But “persistent” is worth some thought as well.
“Persistent” is in the definition to distinguish CDPs from solutions that read data from external source systems without storing it internally. The two main classes of these real time interaction managers, which assemble data to guide Web and call center interactions*, and integration platforms like Jitterbit, Mulesoft, Zapier, and Boomi, which act as switchboards to shuttle data between different systems without storing the data themselves.
The value of persistence is obvious: storing data lets CDP users look back over time to find patterns, calculate trends, build aggregates, and access details that might be lost or inaccessible in source systems. Persistence is especially important for identity resolution, which needs historical data such as the same device accessing different accounts or different devices being used simultaneously. On a practical level, it’s often easier to work with data stored inside the CDP than to read that data from an external system. Indeed, the owners of external systems are often unwilling to allow external access to their data because they fear it will interfere with operational performance. And they’re often right.
But it’s not enough to say that persistence is important. Persistence also has its costs, most obviously in extracting, moving and storing the persisted data. There are also performance penalties from having more data to sort through. It’s true that storage is cheap and big data technology scales almost indefinitely. But if you copy enough data these costs still become significant. At the extremes, there are certain kinds of data it doesn’t make sense to persist (in most cases), such as minute-by-minute changes in customer location, local weather, or stock portfolio values. Most CDP applications only need to know those values while an interaction is happening, so all that’s needed is to look up the current value at the start of an interaction. Storing a continuous history would be overkill, although it often does make sense to save a snapshot of those values at the time of the transaction. As the mention of location may suggest, persistence can also raise privacy issues.
In other words, the question isn’t whether persistence is needed but which data to persist. Choices must be made.
The first step is to distinguish three categories: data which must be persisted, data which might be persisted, and data which should never be persisted. You can then start asking which data falls into each category. The real answers will depend on your situation but here are some thoughts.
- Required data. At a bare minimum, this includes data for identity resolution. That information is the key to linking all other data, whether stored inside or outside the CDP. Historical information is critical to the process so relying purely on external data isn’t an option. Required data also includes information that is often lost in source systems, such as past addresses or contact phone numbers. It extends to derived values that don’t exist in the source systems, such as trends and aggregates.
- Optional data. This is most of the customer profile details and behavior histories loaded into a typical CDP. These could theoretically be read from source systems but are often not available in practice. Reasons could include slow access (especially to support real-time interactions), need for preparation or processing (too slow or inefficient to do on demand), or refused permission from the system owner. Reformatting and indexing data for easy access are other good reasons to load it into the CDP. So is looking for patterns in data streams – sometimes called complex event processing – which needs a readily-accessible history of previous information.
The call for whether or not to persist can be close where there are truly massive amounts of detail – think Web logs – which are used only occasionally. Having them available is extremely convenient, especially when summaries are not sufficient substitutes. For example, customer segmentation projects may need the underlying details to reclassify customers using different segment definitions. Simply storing the customer’s current segment with each interaction won’t work. This type of after-the-fact reclassification is a common requirement and one of the big advantages of having the details in the CDP. But it might not be worth loading the data if you’ll do the analysis just once every three years – although having the data easily available might result in doing the analysis more frequently. Chicken, meet egg.
- Excluded data. The clear cases are information that shouldn’t be stored for privacy, security or regulatory reasons. Beyond that, you’re mostly in the realm of cost-benefit analysis. One reasonable rule of thumb is you don’t want to load data that changes frequently, must be current when used, and is only used rarely. Mobile device location used for customer service is a good example. The difficulty and timeliness of accessing the data in source systems is also a factor: the easier it is to read the data externally, the less value you get from loading it into the CDP. But terms like "frequently", "rarely", "difficulty", and "timeliness" are all relative, so there are no fixed rules here. Sorry.
If you’re thinking that the boundaries between these categories are pretty vague, here’s some bad news: it gets worse. You might want to store the recent portion of a data stream that’s too large to keep in its entirety, in the way that surveillance tapes are kept for a period and then erased if nothing important happened. Or, you might read time-sensitive information directly from operational systems to support real-time interactions, but then upload the same information in overnight batches for historical analysis. And let’s not even get started on the fact that your CDP itself can have different types of storage with different levels of detail and access speed. Or that answers will change over time as you find and discard uses for particular pieces of data. Or that CDP technology itself will evolve.
Given these ambiguities, how should you think about persistence in planning your CDP? As ever, the foundation is specific business uses: what data do you need, in which formats and how quickly, to support your intended applications? Can you meet those needs by reading directly from the source systems or do you need to load it into the CDP? If it must be in the CDP, is the cost to load and store it acceptable? Beyond this relatively static analysis, remember that there may be future uses for the data and that you have choices in how you manage it in your CDP.
Bottom line: persistence in a CDP isn’t a simple topic. But if you only remember that you’ll likely need a combination of internal storage and external access, you’re already started in the right direction.
* This is a large and complicated category, which could easily occupy several blog posts by itself. See this Forrester Wave for a good overview of the classic real time interaction managers, nearly all of which are now baked into enterprise marketing suites. See this Gartner report for an overview of digital personalization engines, a newer group with overlapping functions.
November 28, 2016
Survey: Structured Management is Critical to Database Success
Author: David Raab, CDP Institute
Structured technology management tools such as long term planning and technology standards all contribute to success in building a unified customer database, according to a survey released today by the Customer Data Platform Institute.
We asked marketers and martech experts about their current and planned customer databases and several key management tactics, including long term planning, agile development, technology standards, and value measures. The results confirmed the obvious -- it pays to be organized -- but also added some nuance to understanding what's important.
Some of the key findings are summarized below. You can download the full report for free from the CDP Institute Library at www.cdpinstitute.org
- Long term planning. This is the foundation of traditional IT management and a strong indicator of a structured approach. It is the most common technique over-all, used by 31% of companies. It was also the most powerful predictor of having a database, used by nearly half of companies with a database in place, compared with just 25% of companies planning a database and 19% of those with no current or planned database. Other analysis found that long term planning was equally common whether martech responsibility was shared, held by IT or held central marketing, but less common among marketing departments or companies with no clear martech leader. It was also more common among people who rated single customer view as extremely important for marketing success. Taken together, these all indicate that long term planning is very important for success, both as an indicator of commitment and a sign of a systematic process.
- Value metrics for martech. Formal metrics to measure the value of marketing technology were less common than long-term plans, used by just 20% of all companies. They were substantially more common than average at firms with a database in place, less common at firms with plans to build a database, and almost never used at firms with no plans. They were used slightly more than average at firms where marketing and IT share responsibility for martech and were less common when marketing departments ran their own martech or responsibility was unknown. They were substantially more common among people who considered the single customer view extremely important. These results suggest that value metrics, as another part of structured process, contribute significantly to successful database deployment. Although the value of a central database can be difficult to measure because the database does not generate revenue directly, this does not appear to have hindered deployment at firms in the sample.
- Agile selection process. Agile processes are slightly less common than value metrics (18% of companies) but they show a proportionately similar pattern of being more common among companies with a database than those planning one. They are particularly common when martech responsibility is shared or owned by departments, but uncommon when IT or marketing run martech alone. (These figures may not be statistically significant.) They are more common among people who consider the single customer view extremely important. This pattern suggests that agile is a useful tool but does not speed database development more than other techniques. Companies using both agile and long term planning are much more likely to have a database than companies using either technique without the other.
- Center of Excellence (COE) for martech. Centers of Excellence help to spread skills in using martech tools throughout an organization. As such, they have less to do with building the central marketing database than with using it once deployed. Perhaps for this reason, there is little correlation between having a COE and having a database in place. In fact, firms planning a database are more likely to have a COE than firms with a database already built. This may indicate that those firms are moving ahead with martech tools without waiting to develop a central database. Like all other tools, COEs were more common where the single customer view was considered extremely important. There was little difference in use of COE based on who managed martech.
- Technology standards for martech. Technical standards to guide selection of marketing technology were slightly more common than average at firms with a database in place and slightly less common where a database was planned. This suggests they may slow deployment slightly but any effect is minor. Standards are exceptionally common at companies with shared responsibility for martech and where departments are in charge of their own martech. This is similar to the pattern for agile. It may be that technology standards provide a framework needed for agile to function.
November 22, 2016
Welcome to the CDP Institute Blog
Author: David Raab, Founder, CDP Institute
This is the first official post on the Customer Data Platform Institute blog, even though we launched the Institute three weeks ago. There’s no deep reason for the delay, just that we’ve been busy getting other parts of the Institute functioning. The most prominent of these is the on-line Library of papers from the Institute and sponsoring vendors. We see the Library as the heart of the Institute because it holds such a wide range and depth of information on Customer Data Platforms and related topics. Reading the Library contents is an advanced education in all things CDP and education is what the CDP is all about.
The other activity that’s taken much energy is the daily newsletter, which you’ve been receiving if you’re an Institute member (and if you’re not, sign up here). Even though we limit the newsletter to three articles a day and those articles are just links to news items published elsewhere, it’s still a substantial effort to scan for appropriate items, do enough to understand what each item really represents, and write a several sentence comment. I’ll admit those comments are my favorite part since I get to have a bit of fun while writing them. But the real reason we bother with the comments, when it would be so much easier just to reprint the first few lines of the original articles, is to explain why a particular item is included. This is usually because it illustrates some larger trend or point that’s worth tracking. I’ve always thought of everything I write as a tile filling in one tiny piece of a larger mosaic. Every piece enriches the picture but you can only make sense of it by pulling back and looking at the whole. The newsletter items are more pieces in the mosaic and comments are a ways of showing where each piece fits in the grand scheme of things.
Of course, I do include an occasional item simply because it amuses me.
This blog is another part of the same project. It provides larger tiles than the newsletter but still contributes to the same big picture. I’m especially excited about the blog because we plan to have many expert voices. I'll be one of them but the vendors sponsoring the Institute have agreed to contribute regularly. We’ll bring in outside voices as well. If things go as expected we’ll have almost one post per day, delivering a rich chorus of experts. They won’t always agree (in fact, I hope to occasionally start some productive arguements). But they should certainly cover the broad range of topics relevant to marketers at different stages in their customer data management journey. Truth be told, we are being uncharacteristically systematic (for me) in planning the mix – simply because I feel it’s so important to the members that we do it right.
Of course, even as I write this I’m scanning newsletter articles telling me that blogging is overworked, if not completely dead. For example, this one argues for more infographics while this one asks how your blog can stand out from 65 million others (it's not such a great article, actually, but a killer headline). So part of me does worry that the institute will pump out too much content on the blog and elsewhere. But most of me – and the vendors who have been enthusiastic supporters of the Institute – thinks there’s a ton that hasn’t yet been said on the topic of Customer Data Platforms and customer data management in general and that we’ll all benefit from having the Institute broadcast as much of that information as possible.
And, if you’re worried, we will indeed get around to infographics, videos, slide-shares, podcasts, Webinars, discussion groups, calendars, and eventually real-world meetings and conferences. But it took three weeks to just get to this blog post, so be a little patient, okay?
In the meantime, enjoy what we have and let us know if you have thoughts on what we should add or would like to contribute something of your own.