Relationship between product based loyalty and clustering based on supermarket visit and spending patterns
Bibliographic record
Abstract
Loyalty of customers to a supermarket can be measured in a variety of ways. If a customer \ntends to buy from certain categories of products, it is likely that the customer is loyal to the \nsupermarket. Another indication of loyalty is based on the tendency of customers to visit the \nsupermarket over a number of weeks. Regular visitors and spenders are more likely to be loyal \nto the supermarket. Neither one of these two criteria can provide a complete picture of \ncustomers’ loyalty. The decision regarding the loyalty of a customer will have to take into \naccount the visiting pattern as well as the categories of products purchased. This paper \ndescribes results of experiments that attempted to identify customer loyalty using thes e two \nsets of criteria separately. The experiments were based on transactional data obtained from a \nsupermarket data collection program. Comparisons of results from these parallel sets of \nexperiments were useful in fine tuning both the schemes of estimating the degree of loyalty of \na customer. The project also provides useful insights for the development of more sophisticated \nmeasures for studying customer loyalty. It is hoped that the understanding of loyal customers \nwill be helpful in identifying better marketing strategies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".