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New Tool to Predict Consumer Spending
Method allows more accurate prediction of wallet size and share when competitive data are unavailable.
Understanding share and size of wallet are important for marketing managers to understand both the relative performance of their brands and the size of the market. Sungha Jang, Ashutosh Prasad, and Brian Ratchford develop a Bayesian econometric method for estimating consumer spending patterns when data on category expenditures from competing brands are unavailable.
Using actual customer transaction level data, the method allows more accurate prediction of size of wallet and share of wallet. By identifying distinct segments based upon expenditures, their model enables better targeting of customers to increase revenue.
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Consumer Spending Patterns across Firms and Categories: Application to the Size- and Share-of-Wallet by Sungha Jang, Ashutosh Prasad, and Brian T. Ratchford, International Journal of Research in Marketing (March 2016)
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