Uplift modeling is an approach to predicting the incremental impact a marketing campaign has on a customer through controlled experimentation. It measures the variation in the difference between a treated group and a control group segmenting customers into the following groups:
1. Those that buy only when treated
2. Those that would buy or not buy regardless of whether or not they were treated
3. Those that do not buy when treated, but do buy when not treated
I've have yet to use Uplift Modeling, but it sounds rather interesting.
Would you like to read more?
DM Review article
Using Control Groups to Target on Predicted Lift
3 comments:
Portrait Software provides analytic tools for uplift modeling.
http://www.portraitsoftware.com/Products/portrait_uplift_optimizer
It really works.
It's a conceptually simple (though slippery) idea, but quite hard to make work in practice. But when you get it right, the results are phenomenal.
At the risk of shameless self-promotion (apologies), I have a blog dedicated mostly to this topic at http://scientificmarketer.com. And lots of information and some white papers available at http://stochasticsolutions.com
If you want to know more about Uplift Modeling, there is a lot of good information here - What is Uplift Modeling?
The resource also includes information about how uplift modeling works, case studies of uplift modeling users, and an uplift modeling FAQ
If you are interested in using uplift modeling techniques for customer retention programs, Forrester have published a report that is certainly worth a read - "Optimizing Customer Retention Programs" by Suresh Vittal, Principal Analyst - Forrester Research.
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