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Customer behavior across competitive loyalty programs

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Abstract

Customers can belong to multiple competing loyalty programs each with multiple reward levels. We extend loyalty program theories by proposing five mechanisms that capture the competitive effects in multi-firm, multi-level loyalty programs. We empirically test our hypotheses using data from a loyalty program management app where customers manage points independently across competing firms. We utilize goal shielding theory to show how a customer’s purchase at the focal firm is affected by the customer’s purchases and redemptions across competing firms. Specifically, we find that a customer’s purchase probability at the focal firm decreases after they qualify for a reward independent of redemption at a competing firm (competitive mere reward qualification) and after they redeem a reward at a competing firm (competitive rewarded behavior). Further, we find that the customer’s purchase probability at the focal firm increases if the customer is far from both the qualified and higher-level rewards at the competing firm (competitive stuck-in-the-middle), and if the customer accelerated their purchase frequency to qualify for or redeem a reward at the competing firm (competitive effort balancing post qualification and redemption). Four lab experiments supplement our empirical findings with causal evidence. Our research shows that customer progress toward a goal in a loyalty program is influenced by competing loyalty programs.

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Notes

  1. https://www.bellycard.com/

  2. https://www.fivestars.com/

  3. https://smile.io/about-us

  4. To provide some examples, here are the reward options that are offered by one of the restaurants in our dataset: free bag of chips or 1/4 pound of salad (10 points); free truffle (20 points); free bread ends & house dressing (30 points); free sandwich (65 points). For another restaurant, the rewards are as follows: free cookie, fountain drink or bag of chips (15 points); free entrée (80 points).

  5. Figure 1 illustrates purchase probability, but we expect a similar impact on the redemption probability.

  6. A similar effect was demonstrated by Stourm et al. (2015) in the context of linear loyalty program.

  7. It is worthwhile to mention that the effort balancing is different from a temporal displacement in purchase incidence across firms after qualification or redemption at one firm. The temporal displacement effect argues if a customer exhausts their spending budget by accelerating the purchase in one firm, the customer will purchase less across all firms post qualification or redemption. Whereas effort balancing argues accelerating purchases in one firm to achieve a reward qualification or redemption leads to a decrease in engagement with that firm and an increase in engagement across other firms after the qualification or redemption. This represents a rebalancing of efforts to pursue goals/rewards and purchase across firms (Goswami and Urminsky, 2017).

  8. Restaurant 6 does have a higher average spend than the other 5 restaurants. This is likely because at restaurant 6, splitting checks is not an option. The entrée prices per person are similar with the other 5 restaurants.

  9. We acknowledge that customers can make more than one purchase at the same restaurant in a given month; however, this rarely happens in our dataset. In 96.7% of observations in our dataset, customers make either 0 or 1 purchase at a firm in a given month. Thus, monthly aggregation of data does not seem to pose a major concern.

  10. We used the critical values from Nahm et al. (2022) (Table W7, Web Appendix H) since this table reports critical values for N = 4, which is the observation window we use for our dataset.

  11. We decided not to model spending per visit, since in the restaurant industry spending per visit is mainly based on customer needs (e.g., lunch vs. dinner, group size, dining occasion, etc.), and is less affected by loyalty programs. Customers may dine more frequently at a restaurant to benefit from its loyalty program, but customers are less likely to eat more at each dining occasion just to get more loyalty points.

  12. We discuss \({\widehat{R}}_{\text{ijt }}^{\text{P}}\) and \({\widehat{\varepsilon }}_{\text{ijt }}^{\text{mrq}}\) in detail in the following section on Identification Strategy.

  13. We note that we do not include mere reward qualification in the Redemption model as it is a criterion for sample selection and we do not include effort balancing after redemption in the Redemption model because it is a post redemption effect, not a driver of the decision to redeem a reward.

  14. The firms in our data set do not run any advertising or direct marketing that is targeted toward specific customers, so there is no endogeneity concern with the marketing mix.

  15. This is an approach similar to one used in Hausman and Leibtag (2007) who used sales of other food products as an instrument for food sales of the focal product across firms.

  16. We also run the same model with customer fixed effects (see Web Appendix B). We find similar results for all the variables in both the customer random and fixed effects models. However, due to the large number of customers with the relatively small number of observations per customer (large n, small t), our fixed effects models lose many observations from the estimation. We also estimated several other model specifications which we detail in Web Appendix D.

  17. We also conducted an experiment where the participants where “not” informed that they did not have punch cards. The results are similar and available from the author(s).

  18. The treatment condition participants were informed that they enjoyed the experience of a free coffee. A pre-test indicated that this manipulation induced a sense of gratitude among the treatment condition participants. Web Appendix E provides details about the scenario and the manipulation.

  19. We find similar effects when the control condition is a new loyalty program in Mike’s instead of qualifying for a reward at Mike’s. Details are available from author(s) upon request.

  20. Instead of Mike’s and Joe’s for the name of coffee shop, we referred to the coffee shops as Store A and B in this and the next experiment since participants do not have to choose a firm. The firm name hence does not affect the consumer decision when we are testing redemption as the dependent variable.

  21. We find a similar effect when the control condition is a new loyalty program in Store A instead of the customer qualifying for a reward with 10 punches in Store A. Details are available from author(s) upon request.

  22. Based on average value of rewards in the dataset (54 points), we set small (14 points), medium (54 points), and large (94 points) rewards. Low and high reward levels are calculated as one standard deviation from the average value.

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Acknowledgements

The authors thank a mobile loyalty program application for providing the data and participants at the 2015 INFORMS Marketing Science Conference and the 2017 AMA Winter Educators’ Conference as well as seminar participants at Temple University, Western University, American University, University of North Carolina at Chapel Hill, Grenoble Ecole de Management, Michigan State University, and Penn State University for providing useful feedback on a previous version of this manuscript. This paper is part of the first author’s dissertation.

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  1. Fowler College of Business, San Diego State University, San Diego, CA, 92182, USA

    Farnoosh Khodakarami

  2. Smeal College of Business, The Pennsylvania State University, University Park, State College, PA, 16802, USA

    J. Andrew Petersen

  3. Darden School of Business, University of Virginia, Charlottesville, VA, 22903, USA

    Rajkumar Venkatesan

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  1. Farnoosh Khodakarami
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  3. Rajkumar Venkatesan
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Correspondence to J. Andrew Petersen.

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Khodakarami, F., Andrew Petersen, J. & Venkatesan, R. Customer behavior across competitive loyalty programs. J. of the Acad. Mark. Sci. 52, 892–913 (2024). https://doi.org/10.1007/s11747-023-00965-z

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