IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v98y2022i4p647-666.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435922000252
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jretai.2022.03.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Michael Braun & David A. Schweidel, 2011. "Modeling Customer Lifetimes with Multiple Causes of Churn," Marketing Science, INFORMS, vol. 30(5), pages 881-902, September.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    15. 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.
    16. 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.
    17. 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.
    18. Taylor Randall & Karl Ulrich & David Reibstein, 1998. "Brand Equity and Vertical Product Line Extent," Marketing Science, INFORMS, vol. 17(4), pages 356-379.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Greg M. Allenby & Peter E. Rossi, 1991. "Quality Perceptions and Asymmetric Switching Between Brands," Marketing Science, INFORMS, vol. 10(3), pages 185-204.
    24. Sharad Borle & Siddharth S. Singh & Dipak C. Jain, 2008. "Customer Lifetime Value Measurement," Management Science, INFORMS, vol. 54(1), pages 100-112, January.
    25. 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.
    26. 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.
    27. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
    2. 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.
    3. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    4. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    5. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    6. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    7. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    8. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    9. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    10. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    11. 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.
    12. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    13. Jaiswal, Anand K. & Niraj, Rakesh & Park, Chang Hee & Agarwal, Manoj K., 2018. "The effect of relationship and transactional characteristics on customer retention in emerging online markets," Journal of Business Research, Elsevier, vol. 92(C), pages 25-35.
    14. Holtrop, Niels & Wieringa, Jaap E., 2023. "Timing customer reactivation initiatives," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 570-589.
    15. Arun Gopalakrishnan & Eric T. Bradlow & Peter S. Fader, 2017. "A Cross-Cohort Changepoint Model for Customer-Base Analysis," Marketing Science, INFORMS, vol. 36(2), pages 195-213, March.
    16. Arun Gopalakrishnan & Zhenling Jiang & Yulia Nevskaya & Raphael Thomadsen, 2021. "Can Non-tiered Customer Loyalty Programs Be Profitable?," Marketing Science, INFORMS, vol. 40(3), pages 508-526, May.
    17. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
    18. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    19. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    20. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jouret:v:98:y:2022:i:4:p:647-666. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.