(Source: Business Wire)In a recent study titled "Optimizing Customer Retention Programs (October 20, 2008), Forrester Research profiled next generation uplift analytics and its impact on optimizing customer retention. According to the report "Uplift modeling is an emerging technique that can help marketers improve the performance of their customer retention programs." The report found that one marketer used this new technique to achieve results 36% better than traditional analytics, with 40% less marketing costs.

According to next generation marketing industry expert Dr. Mark Smith, executive vice president of Portrait Software, a down economy is the driver behind companies striving to trim costs wherever possible and elevating customer retention to the top of priority lists. Said Smith, "Traditionally identifying those likely to churn and then directing attention to that group of customers has been at the center of customer retention initiatives. As the Forrester report highlights, "simply looking at likelihood of churn isn't sufficient to power retention programs." Suresh Vittal, Principal Analyst at Forrester Research writes, "Emerging techniques like uplift modeling can help marketers do more than identify churners. This helps marketers to narrow programs to only focus on customers who will react positively."

Uplift Modeling

Sometimes discussed using different names; differential response analysis, incremental impact modeling, net modeling, incremental response modeling and true response modeling, Uplift modeling separates "true" responses to campaigns from purchases that would have been likely to happen anyway. Uplift modeling determines the increase in the probability that each customer will buy (or stay) when they would otherwise not have done so. As a key revelation exposed by the approach, marketers can now see which prospects are likely to ignore the offer and which are likely to use it as a trigger to defect.

Cut unnecessary marketing spend; improve marketing ROI

Smith comments that the ability to discern those customers whose behavior is most likely to change as a result of marketing, increases campaign profitability by enabling marketers to target fewer people, ignoring those who are not affected by marketing. Greater results are achieved by eliminating negative effects by not targeting those who are prompted by the marketing campaign to look for competitive offerings. As a result, marketers can actually spend less and make more using Uplift, overcoming the marketing modeling myopia that is associated with traditional analytic approaches.