ISyE Seminar Series: Chamsi Hssaine
"On The Limited Cost of Consistency In Points-Based Rewards Programs"
Chamsi Hssaine
Assistant professor of Data Sciences and Operations at the University of Southern California's Marshall School of Business
About the Seminar:
Points-based rewards programs are a prevalent way to incentivize customer loyalty. In theory, the effective design of these programs is shaped by two important considerations. Customer heterogeneity suggests that tailoring thresholds to different customer types may yield revenue gains, while demand uncertainty creates incentives to update thresholds over time as the seller learns how customers respond to the program. In practice, however, there may be nontrivial operational and customer-facing benefits associated with maintaining {\it consistency} along both of these dimensions. Setting a single threshold across customer types can facilitate transparent communication and reduce the legal, reputational, and operational risks associated with personalization, while avoiding threshold increases prevents the devaluation of customers' previously earned points. Motivated by these considerations, this paper studies the cost of consistency in points-based rewards programs. We study this question within the context of the popular ``Buy $N$, Get One Free'' (BNGO) rewards programs. We first bound the {\it value of personalization} of these programs, proving that the revenue of the optimal {single-threshold} program is within a factor of $\frac{\pi^2}{6}$ of that of the optimal personalized strategy. We then tackle the problem of designing {\it devaluation-free} learning algorithms --- i.e., algorithms that never increase the redemption threshold --- in the presence of demand uncertainty. Toward this goal, we first design a greedy learning algorithm that incurs $\widetilde{O}(\sqrt{T})$ regret in expectation over a horizon of length $T$; this guarantee is optimal, up to polylogarithmic factors. We then provide a devaluation-free modification of this algorithm, and show that this results in only a constant factor loss in regret. Finally, we conduct extensive numerical experiments to show the limited value of personalization in average-case settings, in addition to demonstrating the strong practical performance of our proposed learning algorithms. Joint work with Yichun Hu (Cornell Johnson) and Ciara Pike-Burke (Imperial).
The manuscript can be found on arXiv.
About the Speaker:
Chamsi Hssaine is an assistant professor of Data Sciences and Operations at the University of Southern California's Marshall School of Business. Her research interests lie broadly in data-driven decision-making, with applications to revenue and inventory management. Most recently, her research has focused on two themes: (i) fairness in decision-making, and (ii) the development of new models and algorithms for modern e-commerce platforms.
Prior to joining USC, Chamsi was a postdoctoral scientist in the Supply Chain Optimization Technologies group at Amazon. She received her Ph.D. in Operations Research and Information Engineering from Cornell University, and earned her B.S.E. in Operations Research and Financial Engineering from Princeton University. Her work has received a number of recognitions, including the 2026 SIGMETRICS Best Paper Award, and has been selected as a finalist for the 2026 INFORMS Service Science Best Cluster Paper Award, the 2024 INFORMS Service Science Best DEIJ Paper Award, and the 2021 INFORMS Minority Issues Forum Paper Competition.
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