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Measuring the effects of customized targeted promotions on retailer profits: prescriptive analytics using basket-level econometric analysis

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Abstract

This study empirically estimates the expected basket-level demand effects, as well as the expected store profit effects, of three different customization levels of retailer promotions. Using data from a national grocery retailer in the U.S., we estimate a household’s contemporaneous purchase incidence outcomes in 28 frequently shopped categories. Estimating the cross-category dependencies in purchase incidence as a function of exposure to levels of customized promotions, allows us to measure the effect of each campaign on expected retailer profit and implement prescriptive analytics to identify the appropriate multi-level coupon mix for maximizing profits. We find all three levels of coupon customization result in per-customer returns, but that medium customization leads to the highest incremental expected profit, while high customization generates the highest expected profit. The results provide insights to retailers about investing in more customized promotional efforts, with a detailed cross-category perspective into where such value is gained.

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Notes

  1. “Active” indicates the weeks in which the coupon was still good (i.e., had not expired) after it was received.

  2. This is because only a subset of the 800 households purchase in these categories over the study period, and even these households purchase only once over the 102 weeks.

  3. In the estimation, we find that the 800 households in our dataset fall under 750 unique multi-category heterogeneous support points, which reflects a very high degree of heterogeneity across households.

  4. Category shelf price is not used as an explanatory variable since time-varying product prices were not made available to us by the retailer.

  5. For 17 categories, the 2-support distribution is found to be the most appropriate, while for the remaining 11 categories, the homogeneous solution is found to be the most appropriate, based on BIC.

  6. Another interpretation is that customers with higher purchase frequencies joined the loyalty program earlier.

  7. In Web Appendix (Figs. 1 to 6), we plot the expected store profits for all 6 different types of retailer targeted coupons campaigns, including the 3 sub-Type A coupons (i.e., favorite product, favorite brand and category).

  8. Store trip probability is calculated similar to the store choice model of Bell and Lattin (1998), where the store attractiveness variable has the spirit of the inclusive value variable used in nested logit models (Ben-Akiva and Lerman 1985).

  9. The only noticeable negative cross-category spillover occurs for nuts, which could be due to the fact that nuts are a protein substitute.

  10. The only estimated negative cross-category effect is for in-store photo finishing products.

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Authors and Affiliations

  1. Leavitt School of Business, Southern Utah University, 351 W. University Blvd., Cedar City, UT, 84720, USA

    Alexander Chaudhry

  2. McIntire School of Commerce, University of Virginia, 125 Ruppel Drive, Charlottesville, VA, 22903, USA

    Carrie Heilman

  3. Olin Business School, Washington University in St. Louis, One Brookings Drive St, Louis, MO, 63130, USA

    P. B. Seetharaman

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  1. Alexander Chaudhry
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  2. Carrie Heilman
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  3. P. B. Seetharaman
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Correspondence to Alexander Chaudhry.

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Chaudhry, A., Heilman, C. & Seetharaman, P.B. Measuring the effects of customized targeted promotions on retailer profits: prescriptive analytics using basket-level econometric analysis. J Market Anal (2023). https://doi.org/10.1057/s41270-023-00253-3

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