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Choosing the Right Analytics: Uplift Models

Last week we discussed time-to-event models; this week our series on choosing the right analytics for the job moves to uplift models. If a propensity model predicts a customer is likely to buy a given product, why should the business go to the expense of sending a promotional offer? Uplift models help businesses determine if an investment is likely to be worth the result. Often used in conjunction with time-to-event models, they predict the amount of change likely to occur in a customer behavior as a direct result of a particular retailer action.

Uplift models can save businesses millions by enabling them to avoid offering discounts to customers who will purchase without them. For example, such a model might predict whether or not sending a 20 percent-off offer is likely to increase a particular customer’s propensity to buy a pair of designer jeans within the next two weeks. The business can then send the coupon only to customers whose behavior it’s likely to change.

On the other hand, when uplift modeling indicates a customer’s behavior is likely to be affected by a promotion, it can also help businesses determine which promotion will have the most impact. Will 20 percent off be much more effective than 10 percent off? Than free shipping? Is offering 12 months of interest-free credit necessary, or will 6 months be nearly as enticing? Uplift models provide the analytical insights businesses need to make precise decisions about where to put marketing spend for higher ROI.

Uplift models are based on cutting-edge analytic techniques that can predict individual customer sensitivities to price incentives, redemption terms and even promotional package design. For instance, one FICO client that spends heavily on promotion used uplift models to increase its return on this investment. The analytics provide insights that enabled the client to accelerate the purchasing behavior of so-called “laggards”—customers who historically haven’t been among the first to purchase. By targeting these customers with offers that are likely to change their historical behavior, the client increased the concentration of sales in the first two months of the product lifecycle—its “critical period” before competitors can draft off of their momentum.

Next time, our series concludes by looking at maximizing and measuring these analytics. If you can’t wait for the next post on this topic, check out our Insights white paper: "Which Retail Analytics Do You Need?" (registration required).

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