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May 20, 2019

Behavioural and Engagement Data Key to Success for Predictive Analytics

By Matthew Kelleher, RedEye

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At a recent event I presented six case studies about the successful (i.e. they made money) application of Predictive Analytics. All the case studies were based on the application of Predictive Analytics to help target customers or prospects at various points along the customer journey, for instance, identifying the single buyers most likely to make a second purchase or which VIP customers were at greatest risk of lapsing.

At the end of the presentation I was asked, ‘What is your definition of Behavioural Data?’ I had repeatedly talked about the importance of accurate and complete data to drive Predictive Analytics and described 1st party customer data as falling in 3 types: transactional, engagement and behavioural. But I had fallen into the trap of failing to explain what I meant by each of the definitions I was using. So, very briefly:

  1. Transactional Data. Or RFM. Or RFV. What a customer has bought, how much, when, where… Data that is used to build RFM models (i.e. identifying prospects, single or multi buyers, VIPs etc.)
  2. Engagement. Points of interaction with the brand, from opening an email to receiving a catalogue to visiting the website.
  3. Behavioural Data. Usually related to website activity, what the prospect or customer has browsed, their recency frequency across devices, what they’ve clicked, if they visit the website direct from social, if they click on a discount offer, etc.

Behavioural data has always been critical. It is the core of data-driven personalisation. By building up, and allowing marketers to react to ‘behaviour’, from marketing the products someone is interested in through to identifying if the consumer is an offer junky or full price buyer, this is what underpins RedEye’s approach to Predictive and AI. Using this information to work in combination with engagement and transactional data identifies prospects and customers in terms of what