Strategic Consumers, Revenue Management, and the Design of Loyalty Programs
Bibliographic record
Abstract
We study the interaction between the design of a premium-status loyalty program, revenue management, and strategic consumer behavior. Specifically, we consider a contemporaneous change where firms across several industries switch their loyalty programs from quantity-based toward spending-based designs. This change has been met with fierce opposition from the media and consumers. Building on the microfoundations of strategic, forward-looking, and status-seeking consumer behavior, we endogenize strategic consumer response to firms’ pricing and loyalty program design decisions, and we characterize conditions under which, by coordinating these decisions, firms can benefit from strategic consumer behavior. We further show that by switching to a spending-based design, firms can benefit from strategic behavior even more, under broader conditions, and in a Pareto-improving way. Finally, we also analyze combined designs, which utilize a combination of quantity and/or spending requirements, and show how they can be used to better manage the transition toward spending-based designs, possibly minimizing negative consumer reactions. This paper was accepted by Serguei Netessine, operations management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".