Research Note—Using Basket Composition Data for Intelligent Supermarket Pricing

Nanda Kumar () and Ram Rao ()
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Nanda Kumar: SM 32, School of Management, The University of Texas at Dallas, P.O. Box 830688, Richardson, Texas 75083-0688
Ram Rao: SM 32, School of Management, The University of Texas at Dallas, P.O. Box 830688, Richardson, Texas 75083-0688

Marketing Science, 2006, vol. 25, issue 2, 188-199

Abstract: How can supermarkets use the vast data they have to design strategies to compete for large-basket shoppers, potentially their most profitable customers? We say, analyze the data to glean basket composition of heterogeneous consumers. Of theoretical and practical interest is the question, will our suggestion improve supermarket profits, and if so, by what pricing strategies? By answering this we can also answer the question, do results of extant research on single-product marketing that using such information intensifies competition and lowers profits, generalize to multiproduct marketing by supermarkets that carry several products, and whose patrons purchase multiple items? We derive equilibrium data-based intelligent pricing strategies that also produce increased supermarket profits. What is more interesting is our result that data analysis is profitable whether or not a competitor undertakes data analysis. If not too costly, implementing data analysis is a dominant strategy. Moreover, with comprehensive data analysis connecting basket information and household data, stores can increase profits further by targeting rewards at individual households, thereby segmenting the market more completely. In contrast to past research findings, we show that for supermarkets, even under competition, use of information on past purchases enables intelligent pricing, better segmentation, and higher profits.

Keywords: basket size; competition; consumer data-analytics; game theory; supermarkets; price discrimination; intelligent pricing; retailing; reward cards; reward programs (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:25:y:2006:i:2:p:188-199

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