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Leveraging Loyalty Programs Using Competitor Based Targeting

Author

Listed:
  • Hollenbeck, Brett
  • Taylor, Wayne
Abstract
Loyalty programs are widely used by firms but their effectiveness is subject to debate. These programs provide discounts and perks to loyal customers and are costly to administer, and with uncertain effectiveness at increasing spending or stealing business from rivals. We use a large new dataset on retail purchases before and after joining a loyalty program (LP) at the customer level to evaluate what determines LP effectiveness. We exploit detailed spatial data on customer and store locations, including locations of competing firms. A simple analysis shows that location relative to competitors is the strongest predictor of LP effectiveness, suggesting that LPs work primarily through business stealing and not through other demand expansion. We next estimate what variables best predict LP effectiveness using high-dimensional data on spatial relationships between customers, the focal firm’s stores, and competing stores as well as customers’ historical spending patterns. We use LASSO regularization to show that spatial relationships are more predictive of LP effects than are past sales data. Finally, we show how firms can use this type of predictive analytics model to leverage customer and competitor location data to substantially increase the performance of their LP through spatially driven targeting rules.

Suggested Citation

  • Hollenbeck, Brett & Taylor, Wayne, 2019. "Leveraging Loyalty Programs Using Competitor Based Targeting," MPRA Paper 92900, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:92900
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    References listed on IDEAS

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    Cited by:

    1. Stourm, Valeria & Bradlow, Eric T., 2023. "Cross-reward effects in a coalition loyalty program: The impact of a point currency devaluation," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 276-293.
    2. Brett Hollenbeck & Renato Zaterka Giroldo, 2022. "Winning Big: Scale and Success in Retail Entrepreneurship," Marketing Science, INFORMS, vol. 41(2), pages 271-293, March.

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    More about this item

    Keywords

    Loyalty programs; predictive analytics; spatial models; retail competition; machine learning;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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