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The trajectory of customer loyalty: an empirical test of Dick and Basu’s loyalty framework

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  • Paul Valentin Ngobo

    (Paris Dauphine University)

Abstract

The classic model of loyalty proposed by Dick and Basu (1994) assumes that customers can be naturally classified in four loyalty conditions. This model has been tested with cross-sectional data and measures of recalled or retrospective consumer loyalty. Therefore, these studies could not examine whether and why customers move across different loyalty conditions over time, and offer guidance to managers on how to shift customers to the more desirable loyalty conditions. In this paper, we conduct an empirical test of this model and examine the key drivers of shifts in consumers' loyalty conditions over six annual time periods. Based on data from 6,109 households and 23 stores, we find customers can be classified in three loyalty conditions only: (1) the no loyalty, (2) the latent loyalty, and (3) the true loyalty conditions. The spurious loyalty condition is not supported, probably because switching costs are negligible in the grocery retailing industry. However, we find that marketing actions, i.e., private label policy, feature advertising, end-of-aisle product display, and store pricing policy, influence customer transition across loyalty conditions.

Suggested Citation

  • Paul Valentin Ngobo, 2017. "The trajectory of customer loyalty: an empirical test of Dick and Basu’s loyalty framework," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 229-250, March.
    • Handle: RePEc:spr:joamsc:v:45:y:2017:i:2:d:10.1007_s11747-016-0493-6
    DOI: 10.1007/s11747-016-0493-6
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    1. Gilles Laurent & Raphaelle Lambert-Pandraud & Eric Lapersonne, 2005. "Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and Interpretations," Post-Print hal-00458431, HAL.
    2. 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.
    3. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    4. Alba, Joseph W, et al, 1994. "The Influence of Prior Beliefs, Frequency Cues, and Magnitude Cues on Consumers' Perceptions of Comparative Price Data," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(2), pages 219-235, September.
    5. Tülin Erdem & Michael Keane & Baohong Sun, 2008. "The impact of advertising on consumer price sensitivity in experience goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 6(2), pages 139-176, June.
    6. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    7. Sridhar N. Ramaswami & S. Arunachalam, 2016. "Divided attitudinal loyalty and customer value: role of dealers in an indirect channel," Journal of the Academy of Marketing Science, Springer, vol. 44(6), pages 770-790, November.
    8. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    9. Paul S. Albert & Dean A. Follmann, 2000. "Modeling Repeated Count Data Subject to Informative Dropout," Biometrics, The International Biometric Society, vol. 56(3), pages 667-677, September.
    10. V. Kumar & S. Sriram & Anita Luo & Pradeep K. Chintagunta, 2011. "Assessing the Effect of Marketing Investments in a Business Marketing Context," Marketing Science, INFORMS, vol. 30(5), pages 924-940, September.
    11. Mark, Tanya & Lemon, Katherine N. & Vandenbosch, Mark & Bulla, Jan & Maruotti, Antonello, 2013. "Capturing the Evolution of Customer–Firm Relationships: How Customers Become More (or Less) Valuable Over Time," Journal of Retailing, Elsevier, vol. 89(3), pages 231-245.
    12. Mazodier, Marc & Quester, Pascale, 2014. "The role of sponsorship fit for changing brand affect: A latent growth modeling approach," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 16-29.
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