Exploring the geographical dimension in loyalty card data
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
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it