Predictive modelling is a term with many applications in statistics but in database marketing it is a technique used to identify customers or prospects who, given their demographic characteristics or past purchase behaviour, are highly likely to purchase a given product. In this context, ‘predictive’ does not simply mean predicting the future; it means identifying the quantitative factors that can be used to predict buyer behaviour. Predictive modelling is a powerful data analysis technique that can be used to target email and direct mail activity, and to some degree behavioural targeting in online media.
Here’s an example: Let say you sell 10 products. It may be the case that all purchasers of product 8 are: 1) in a certain geodemographic group, 2) married with more than one child and 3) own more than one car. All these factors can be analysed and combined to predict the likelihood of any consumer in your database buying product 8. Usually this combined measure is referred to as a ’score’ i.e. a figure which represents the presence or combination of certain variables in the consumer record. Once you have developed your scoring model you can rank all customers by their score. When you’ve stripped out those who have already bought product 8, you are left with a set of high potential prospects.Â
Predictive modelling can also be undertaken based on transactional information about past purchases. Going back to the 10 products, it may be the case that 80% of people who buy product 7 have previously bought products 2, 5 and 6 and in that order. So we can say that people who have bought products 2, 5 and 6 (in that order) but who have not yet purchased product 7, are much more likely to buy product 7 than everyone in your database. Again a score is attached to these behaviours and that score can be used to rank your prospects in terms of untapped sales potential.
Of course as well as predicting purchase behaviour, these techniques can be used to predict risk. In credit assessment for example, it may be the case that those customers who have certain demographic characteristics combined with a certain type of past purchase behaviour are highly likely to default on a credit agreement. This is sometimes referred to as credit scoring. If you are rejected for credit at a bank or in a shop it will be because your data has been analysed and your credit risk score is deemed too high or low to meet the criteria of the lender.
These predictions can help you target your communications very efficiently and also help you control commercial risk in customer behaviour. What’s interesting about these techniques is that they help both the marketing department and the finance department. Marketing delivers customers who are both highly likely to convert to sales or high lifetime value whilst at the same time, producing customers who are less likely to cause problems for the finance department. Overall, this means that the resources of the business are being better utilised.
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Rory Sutherland as new IPA President
I think it’s great news that Rory Sutherland has been made the flag-flyer at the IPA. This is for two reasons. Firstly he’s a direct marketer through and through. And whilst the traditional mass-analogue advertising model still has a big role to play in marketing communications, times have changed with the advent of the internet, mobile and ipods. One-to-one communciation is becoming an increasingly important, if not dominant model. As a direct marketer with a broad mind, Rory is a great choice to be the standard bearer of this change in the advertising community. His appointment marks a coming of age for direct marketing.
Secondly, anyone in advertising, and particularly anyone with a creative pedigree who is broad-minded enough to say, “I eccentrically believe data analysis and really good statistical modelling can be immensely creative - because, just like a good creative team, well-worked data can reveal wonderfully unexpected, unasked for truths” automatically gets a vote from me. As David Ogilvy once said, in advertising it pays to be unorthodox.
However, it’s a shame that Rory has to feel ‘eccentric’ when it comes to promoting the benefits of using hard data. Advertising does seem fixated on basing its decisions on what people say they might do in some given situation rather than looking at what people have actually done in a known situation.
Data doubters in advertising argue that data analysis can only provide ‘rear view’ insight and therefore has only limited application in the creative process. But the past is a repository of great learning and there should be no doubting how this can help us better manage the future. The reality is that all intellectual development and economic progress is a product of the past. If you don’t believe me, ask Barack Obama what he’s learnt from FDR or look at what the Beatles learnt from skiffle. So, if looking backwards can help us solve the current global economic crisis, or create some of the greatest popular music of all time, then surely it can certainly help us sell cars, cereals and holidays.