An approach to develop effective customer loyalty programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it