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Stats Modeling Tutorial: Net Lift Model (1)

NetLift Model Tutorial part-2: SAS Logistic Modeling and Video from Dr. Larsen

Net Lift Model Part-3: the whole Process flow
This tutorial describes basic concepts, benefits and challenges of implementation of Net Lift Models in direct marketing campaigns. Net lift models predict which customer segments are likely to make a purchase ONLY if prompted by a marketing undertaking. It's summarized based on a few SAS/Stats papers:
The True Lift Model - A Novel Data Mining Approach to Response Modeling" in Database Marketing, by Dr. Lo
Net Lift Model for Effective Direct Marketing Campaigns, by Dr. Kubiak.


Majority of direct marketing campaigns are based on purchase propensity models, selecting customer email, paper mail or other marketing contact lists based on customers' probability to make a purchase.

This purchase propensity model had a 'nice' lift (rank's response rate over total response rate) for the top 4 ranks on the validation data set. Consequently, we would contact customers included in top 4 ranks. After the catalog campaign had been completed, we conducted post analysis of mailing list performance vs. control group. The control group consisted of customers who were not contacted, grouped by the same purchase probability scoring ranks.

net lift model tutorial, direct marketing campaign

As shown in the above table , the top four customer ranks selected by propensity model perform beyond the control group. However, even though mailing/test group response rate was at decent incremental response rate (mailing group net of control group) for combined top 4 ranks was low incremental response rate, our undertaking would be likely generating a negative ROI.

We should be predicting incremental impact - additional purchases generated by a campaign, not purchases that would be made without the contact. Our marketing mailing can be substantially more cost efficient if we don't mail customers who are going to buy anyway.

Since customers very rarely use promo codes from catalogs or click on web display ads, it is difficult to identify undecided, swing customer based on the promotion codes or web display clickthroughs.

Net lift models predict which customer segments are likely to make a purchase ONLY if prompted by a marketing undertaking.

Purchasers from mailing group include customers that needed a nudge, however, all purchasers in the holdout/control group did not need our catalog to made their purchasing decision. All purchasers in the control group can be classified as 'need no contact'. Since we need a model that would separate 'need contact' purchasers from 'no contact' purchasers, the net lift models look at differences in purchasers in mailing (contact) group versus purchasers from control group.

In order to classify our customers into these groups we need mailing group and control group purchases results from similar prior campaigns. If there are no comparable historic undertakings, we have to create a small scale trial before the main rollout.

All models described in this project used stepwise logistic regression on data partitioned into test and validation sets.

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