Predictive modeling is a gateway drug to personalisation

By Ariane Bernard


New York, Paris


This question came up earlier this month in my chat with publishers at INMA’s Media Innovation Week: Once you have modeled a next best action (as I discussed in an earlier blog), what do you do? 

At this point, we no longer have an analytics problem and we no longer have a data science modeling problem. Instead, we’re in the delivery side of things: variating {something} for our user whose activity — or lack thereof — makes them a candidate for some intervention relative to the one-size-fits-all experience you already have in place.

Predicting the likelihood of churn is a smart use of predictive analytics and leads to personalisation.
Predicting the likelihood of churn is a smart use of predictive analytics and leads to personalisation.

If we’re talking about churn and your model is identifying a group of users who are not reading enough and now are in the “danger zone” of your model, the next best action may be to try and put more articles in front of them.

Doing this, of course, is the territory of personalisation. I won’t go too nuts in the details of this (I won’t go too nuts yet ... but just you wait, because I have a whole report on the topic coming out in a few weeks).

But I want to underline something here: Not all personalisation has to come from your Web frontend or your app.

In fact, one of the more technically friendly place to deliver some personalisation is e-mail, leaning into your CRM and ESP to deliver some tailored e-mails to a flagging user (with more to read, with custom offers, etc.). By its nature, a CRM is oriented at personalisation. So anything that can use the pipes of your CRM can be useful to handle the personalised delivery of the next best action.

So that’s the last mile of your predictive analytics journey: closing the prescription with an action loop.

In doing so, we actually head into prescriptive analytics (next best action), and automating the delivery of this prescription isn’t analytics at all. The border between predictive analytics and prescriptive analytics is often pretty fine because making predictions will naturally identify the variables that are significant in the model.

You could, of course, stop at making predictions (“Lee is likely to churn”). But in most cases, you’ll want to act on this. 

Which is why, on this journey to predictive analytics, you will, sooner or later, encounter personalisation. And on this topic, and to borrow the words of the Terminator: “I’ll be back.”

If you’d like to subscribe to my bi-weekly newsletter, INMA members can do so here.

About Ariane Bernard

By continuing to browse or by clicking “ACCEPT,” you agree to the storing of cookies on your device to enhance your site experience. To learn more about how we use cookies, please see our privacy policy.