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• W2074071710 abstract "Drawing on the marketing, finance and retail research literature, this article uses a multi-stage targeting methodology to enhance the targeting of high-revenue customers. Such an investigation is important for both research and practice, as many retail-marketing departments use traditional response models to identify the optimal customers for marketing communications, despite the fact that maximum revenue is the financial objective for corporations rather than response. A methodology is deployed to build multi-stage models that target the most responsive, highest-spending and highest-profit customers using customer- and product-level profit data from a specialty retailer in the United States. Comparisons of the competing models show that multi-stage models identify customers with higher actual sales and profit than both traditional response models and other forms of multi-stage models in which any two combinations of customer response, spend or profit are predicted. Implications and limitations of this research are discussed in light of the results." @default.
• W2074071710 created "2016-06-24" @default.
• W2074071710 creator A5052340925 @default.
• W2074071710 date "2009-12-01" @default.
• W2074071710 modified "2023-09-25" @default.
• W2074071710 title "Enhanced customer targeting with multi-stage models: Predicting customer sales and profit in the retail industry" @default.
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• W2074071710 doi "https://doi.org/10.1057/jt.2009.22" @default.
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