Consumer Response Model Case Study

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The traditional response models are insufficient to target the highest-spending or most profitable customers.
In fact, response models can potentially target the most responsive customers who actually spend the least, especially when promotional offers involve free items or when there is no purchase requirement.
To evade the unnecessary marketing costs associated with targeting lower-spending and less profitable customers, statisticians in the financial service industries have enhanced response models by extending the models to predict customer spend as well as customer response. It is important to briefly mention that within retail businesses, this development has been considerably slower to emerge.
These models predict combinations of customer response, sales and profit at the individual customer level. There are two distinct analyses. The first analysis uses campaign data to build a traditional (one-stage) response and a two-stage (response-spend) model. The second analysis uses a random sample of customers to replicate the methodology of the first analysis and extend the two-stage models to a three-stage model involving response, spend
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The total customer sample utilized for model-building and final model selection was 67385. There were an additional 67645 customers randomly selected from a campaign that was not used to build the model (that is, a ‘hold-out’ or validation campaign). One benefit of using campaign data is that we also have available a random sample of the holdout campaign that did not receive any offer (a control group), which allowed us to assess the impact of marketing on customers more generally (that is, to measure incremental marketing impact). All customers were ‘active’ purchasers of a particular specialty product line distinct from the product line purchased by customers in the second data set described

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