Exploring the geographical dimension in loyalty card data
Bibliographic record
Abstract
Considers the potential that retail loyalty card schemes offer for a more informed understanding of consumer behaviour. With the widespread introduction of loyalty card schemes across the UK, Europe and North America, retailers now have the opportunity to link detailed shopping pattern information to the individual consumer. Data gathered from loyalty card transactions can be referenced to the address of the individual, and as such, can be considered to be a particular type of potential geographic information. Based on detailed semi‐structured interviews within five UK retail organisations that have implemented loyalty card schemes, the article shows the nature of data analysis and applications at present, with data being mostly utilised in direct marketing. It is argued that recognition of the geographic nature of loyalty card data is currently lacking amongst scheme operators, yet is vital if higher order functions are to be realised. To that end, the paper presents visual frameworks that position loyalty card data within the organisational hierarchy and highlight potential techniques and applications that can be achieved via loyalty card data analysis.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".