An analytical investigation of the profitability of selected loyalty programs in a competitive environment
Bibliographic record
Abstract
This dissertation investigates the profitability of loyalty programs in a competitive environment. Loyalty programs are prevalent marketing tools that encourage repurchase intentions among customers, and increase long-term profitability of firms. However, there is no consensus among researchers regarding the effectiveness of these programs in a competitive environment. This thesis responds to this line of research by developing game theoretic models that incorporate customers’ valuations of reward and time, two factors that have not been considered simultaneously in previous studies on the profitability of loyalty programs. The results show that for firms offering undifferentiated products (e.g., coffee shops), offering loyalty programs is a dominant and profitable strategy for the competing firms only when customers highly value rewards, but not time. After assessing the profitability of loyalty programs, the thesis investigates LP design issues related to the effectiveness of restricting redemption. This aspect of loyalty program design has received minimal attention in the literature. Nine sub-games between two competing firms are solved in which each firm applies one specific restriction level on redemption (unrestricted, low restricted, or high restricted), and optimal decision variables are obtained for each scenario. Based on the Nash equilibria of the sub-games, the main game is solved in which the firms decide about the level of restriction on their loyalty programs, which maximizes their profit. The results show that firms should follow highly restrictive policies at equilibrium, but not when customers highly value time over reward. When the latter is the case, a prisoner dilemma occurs. Firms should react by applying redemption policies that are the least restrictive at equilibrium. Furthermore, when customers do not highly value neither time nor reward, a prisoner dilemma arises that suggests the firms to offer a low restricted redemption policy at equilibrium. In addition to these findings, this thesis contributes to the literature by developing comprehensive analytical models, that are stochastic and competitive, and that incorporate psychological theories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".