JOURNAL ARTICLE

On the Profitability of Loyalty.

  • Published In: Marketing Science (INFORMS), 2025, v. 44, n. 2. P. 306 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Lei, Ying; Shen, Ji; Yang, Ei; Zhai, Xin 3 of 3

Abstract

This article develops an analytical framework to characterize repeat-purchase customer loyalty and its profitability in a dynamic duopoly competition with two horizontally differentiated firms. It distinguishes between loyalty driven by switching costs ("behavioral loyalty") and loyalty driven by positive brand preferences ("attitudinal loyalty"), analyzing their effects under scenarios of global and local consumer preference changes. The study finds that while switching costs increase repeat-purchase loyalty, this loyalty is not profitable for firms because it intensifies price competition and lowers equilibrium prices. Conversely, loyalty driven by strong and stable brand preferences can be profitable, especially when consumer preferences evolve locally and remain relatively stable over time; however, under global preference changes, stronger brand preference may reduce loyalty despite increasing profits. The paper's results highlight the nuanced relationship between customer loyalty and firm profitability, emphasizing the importance for firms to understand the underlying drivers of loyalty rather than relying solely on repeat-purchase behavior as a profitability indicator.

Additional Information

  • Source:Marketing Science (INFORMS). 2025/03, Vol. 44, Issue 2, p306
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:0732-2399
  • DOI:10.1287/mksc.2022.0109
  • Accession Number:183504473
  • Copyright Statement:Copyright of Marketing Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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