Data driven marketing decisions are meant to maximize impact – right? Well, the only way to optimize marketing influence is to predict it. The analytical method to do this is called uplift modeling. This is a completely different animal from what most models predict: customer behavior. Instead, uplift models predict the influence on customer behavior gained by choosing one marketing action over another. The good news is case studies show ROI going where it has never gone before. The bad news? You need a control set… But you should have been using one anyway! The crazy part is that "marketing influence" can never be observed for any one customer, since it literally involves the inner workings of the customer's central nervous system. If influence can't be observed, how can we possibly model and predict it?

The true effectiveness of a marketing campaign isn't response rate! It's the incremental impact – that is, additional revenue directly attributable to the campaign that would not otherwise have been generated. Yet traditional targeting criteria are often designed to find clients that are interested in the product, but would have bought it whether or not they received a promotion. In such cases, the incremental impact is insignificant and the marketing dollars could have been spent elsewhere.

Net Lift Models are designed to maximize incremental impact by targeting the undecided clients that can be motivated by marketing. These "swing customers" are akin to the swing states of a presidential election; data miners could learn a lot from presidential campaigns.

Beyond targeted marketing, Net Lift methodology delivers tremendous performance improvements for deployed churn models – retaining "savables" while avoiding the adverse "reverse" affects retention outreach triggers for some customers – as well as other innovative business applications of this advanced analytical method.

This session will demonstrate how to build Net Lift Models (also referred to as Uplift or Incremental Lift) that optimize the incremental impact of marketing campaigns, discussing the pros and cons of multiple core analytical approaches.

Response modeling is the wrong modeling! Whatever your response rate, what about those customers who would have purchased anyway without expending the cost of contact? If retention offers targeted by a churn model save some customers, what about the "casualties," i.e., the customers who respond adversely to this contact but who would have stayed if left alone? Net lift modeling, a.k.a. uplift, incremental lift or true lift modeling, addresses these very issues.

This convention-altering white paper reveals the why and how of Uplift Modeling, and delivers case study results that multiply the ROI of predictive analytics by factors up to 11.