The wallet of a customer is defined as the total amount this customer can spend in a specific product category. This is a vital piece of information for planning marketing and sales efforts. We discuss the important problem of customer wallet estimation, while emphasizing the use of predictive modeling technologies to generate useful estimates, and minimizing reliance on primary research. We suggest several customer wallet definitions and corresponding evaluation approaches. Our main contribution is in presenting several new predictive modeling approaches which allow us to estimate customer wallets, despite the fact that these are typically unobserved. We present empirical results on the success of these modeling approaches, using a dataset of IBM customers.
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