Bibliographic record
Abstract
Evaluating the capacity of consumer loyalty programs to generate additional sales is essential for marketers who run such programs. However, customers' self-selection into the loyalty programs makes this evaluation difficult. This is the case especially in set-ups where the reward is not granted automatically upon achieving a certain number of points. In the case of automatic rewards, marketing theory predicts that points accumulation accelerates as consumers approach the threshold of necessary points for the reward, and is also boosted after the redemption, in what is called `the rewarded behavior effect'. In this thesis I use these insights to develop two models for evaluating loyalty programs where the rewards are not granted automatically. The first model applies to programs where consumers use the accumulated points like cash, for day-to-day expenses, while the second applies to programs where consumers use the points for non-ordinary treats, which on average are much larger. I estimate the parameters of both models using data provided by AIR MILES, Canada's largest coalition loyalty program. I show how sample heterogeneity and the non-random timing of the reward cash-in can be confounded with true loyalty program effects and I tease apart these effects to obtain non-biased estimates of program profitability. I use the model insights to suggest ways in which AIR MILES can change the program to further boost its profitability, contingent on retailers' contribution margins. The dissertation advances the literature by developing structural models for set-ups where retailers do not impose automatic redemptions upon consumers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".