Predictive Analytics-Example Business Case

Today’s topic is Predictive Analytics and this is a continuation in a series of audio podcast interviews on this topic. Dr. Eric Siegel is a former computer science professor at Columbia University and the President of Prediction Impact.

Q: Dr. Siegel Could you provide an example case of deploying predictive analytics?

A: The two most established places predictive analytics is applied are 1) increasing the response rate in direct marketing and 2) improving customer retention by predicting churn. Let’s discuss a case of the first of these two applications, improving direct marketing response — which is also known as response modeling, since the thing predicted for each customer by the model is whether or not they will respond to the offer.

Predictive analytics supplies the predictive score for each customer, and it’s really simple how these scores can be used used to improve response rate and campaign profit.  First, you sort your list by the scores, with customers that received a higher predictive score up top. This means the first customers in your list are now the most likely to respond.  Then you target your campaign only towards customers higher up on the list.

So, the next question is, where do you draw the line going down the list – how many do you contact?  Well, suppose you are contacting a million prospects, and it costs $2 per contact (this example could be for either a mailing or a phone campaign). The average profit per response, let’s say, is $20, and a little over 7% of those contacted respond.  Under these conditions, a campaign will not be profitable — with a little arithmetic, you can see that it actually loses $566,000, because the response rate’s just not high enough relative to the cost that you’re spending and how much you make on those that do respond.

But with a predictive model, this can be turned around and you can take a loss and make it a profit.  If the cost is expended only to contact the top, let’s say, 25% of the customers, which contains the lion’s share of responses anyway, the campaign will turn out to be profitable, and could land a profit approaching half a million dollars. So knowing which customers to contact can make all the difference in the world.

Let me make one last note about this.  The predictive model that you use to do this can continuously be improved, as long as the responses to each campaign, even a campaign that’s targeted with the model in the first place, are tracked.  Each customer’s response or non-response, if you’ve tracked it, then becomes a learning case with which better predictive models can be developed.

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