An approach to develop effective customer loyalty programs
Bibliographic record
Abstract
Purpose This paper sets out to present a practical approach to develop an effective customer loyalty program by incorporating competition and heterogeneity in customers' preferences, and by avoiding the pitfalls associated with different types of loyalty programs. Design/methodology/approach To illustrate the approach, the paper presents a case study of T&T Supermarkets in Canada to show how a retailer can develop a cost‐effective customer loyalty program to retain and reward loyal customers so as to increase shopping frequency and shopping expenditure. The approach consists of four major steps, which are explained in detail. Findings Most T&T shoppers split their shopping trips at T&T (for Asian groceries and other specialty items) and a major competitor (for Western items). This creates a unique opportunity for T&T to develop a loyalty program that is intended to entice its loyal shoppers to increase their shopping frequency and expenditure at T&T. A “hybrid” reward structure was recommended to address the fact that there are two major segments of customers who prefer different types of loyalty rewards. Originality/value In addition to avoiding some common pitfalls of various loyalty programs, this paper presents a practical approach to develop an effective customer loyalty program by incorporating competition and heterogeneity in customers' preferences.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".