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The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆

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  • Park, Chang Hee
  • Yoon, Tae Jung

Abstract

This research investigates how up- and down-selling promotions affect customers’ cross-brand purchasing and churn behavior at a multi-brand retailer. We employ a hidden Markov model that accounts for the dynamics of customers’ latent brand preferences and attrition and captures the resulting purchase behavior in response to promotional offers. Using data on coupon promotions and purchase transactions from an online retailer, we find that coupons for a higher-end brand increase customers’ likelihood of purchasing the brand. While this suggests that the retailer can increase its short-term revenues by sending coupons for the higher-end brand to customers of the lower-end brand, we find that customers up-sold via coupons are more likely to switch back to the lower-end brand, in comparison to other customers of the higher-end brand, limiting the positive effect of up-selling promotions in the long term. Moreover, lower-end brand customers’ promotion-induced brand switching leads to their increased attrition from the retailer, which negatively affects long-term purchase behavior and revenues. In contrast, when triggered by down-selling coupons, customers’ brand switching does not impact their attrition. Based on these findings, we demonstrate how our model-based approach can assist marketers’ multi-brand couponing decisions.

Suggested Citation

  • Park, Chang Hee & Yoon, Tae Jung, 2022. "The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆," Journal of Retailing, Elsevier, vol. 98(4), pages 647-666.
  • Handle: RePEc:eee:jouret:v:98:y:2022:i:4:p:647-666
    DOI: 10.1016/j.jretai.2022.03.005
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    as
    1. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    2. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
    3. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    4. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    5. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    6. Mark, Tanya & Bulla, Jan & Niraj, Rakesh & Bulla, Ingo & Schwarzwäller, Wolfgang, 2019. "Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 528-541.
    7. Greg M. Allenby & Peter E. Rossi, 1991. "Quality Perceptions and Asymmetric Switching Between Brands," Marketing Science, INFORMS, vol. 10(3), pages 185-204.
    8. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
    9. Amos Tversky & Daniel Kahneman, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1039-1061.
    10. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    11. Michael Braun & David A. Schweidel, 2011. "Modeling Customer Lifetimes with Multiple Causes of Churn," Marketing Science, INFORMS, vol. 30(5), pages 881-902, September.
    12. Övünç Yılmaz & Pelin Pekgün & Mark Ferguson, 2017. "Would You Like to Upgrade to a Premium Room? Evaluating the Benefit of Offering Standby Upgrades," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 1-18, February.
    13. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    14. J. Jeffrey Inman & James S. Dyer & Jianmin Jia, 1997. "A Generalized Utility Model of Disappointment and Regret Effects on Post-Choice Valuation," Marketing Science, INFORMS, vol. 16(2), pages 97-111.
    15. Bruce G. S. Hardie & Eric J. Johnson & Peter S. Fader, 1993. "Modeling Loss Aversion and Reference Dependence Effects on Brand Choice," Marketing Science, INFORMS, vol. 12(4), pages 378-394.
    16. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    17. Taylor Randall & Karl Ulrich & David Reibstein, 1998. "Brand Equity and Vertical Product Line Extent," Marketing Science, INFORMS, vol. 17(4), pages 356-379.
    18. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    19. Övünç Yılmaz & Pelin Pekgün & Mark Ferguson, 2017. "Would You Like to Upgrade to a Premium Room? Evaluating the Benefit of Offering Standby Upgrades," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 1-18, February.
    20. Davis, Harry L & Hoch, Stephen J & Ragsdale, E K Easton, 1986. "An Anchoring and Adjustment Model of Spousal Predictions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(1), pages 25-37, June.
    21. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    22. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    23. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    24. Kim, Wonjoon & Kim, Minki, 2015. "Reference quality-based competitive market structure for innovation driven markets," International Journal of Research in Marketing, Elsevier, vol. 32(3), pages 284-296.
    25. Chang Hee Park & Young-Hoon Park, 2016. "Investigating Purchase Conversion by Uncovering Online Visit Patterns," Marketing Science, INFORMS, vol. 35(6), pages 894-914, November.
    26. Sharad Borle & Siddharth S. Singh & Dipak C. Jain, 2008. "Customer Lifetime Value Measurement," Management Science, INFORMS, vol. 54(1), pages 100-112, January.
    27. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
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