Customer Migration Patterns: Evidence from a North American Retailer
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
In this study, we assess how marketing activities influence the extent to which customers become more (or less) profitable to a company over time. Using data collected from an apparel and household goods multichannel retailer over a three-year period beginning in 2001, we apply a Hidden Markov Model to a longitudinal data set from a cohort of 4,165 customers over a three-year period. We find three segments: inactive, occasional, and loyal. We find 23 unique migration patterns among these segments. Price promotions have a positive effect on increasing migration patterns, but were not significant for stable patterns. Catalogs have a negative impact on stable migration patterns and were not significant for increasing migration patterns. Finally, we find coupons have a negative impact on both stable and increasing migration patterns.
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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 | 0.044 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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