Fighting churn with rate plan right-sizing: a customer retention strategy for the wireless telecommunications industry
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
Prior literature suggests that wireless customers have difficulty in predicting their usage requirements and they often subscribe to rate plans that are not financially optimized. However, the benefits of having wireless customers on optimal rate plans are relatively unknown due to limited research in this area. This paper aims to address this knowledge gap and presents a customer retention strategy for the wireless telecommunications industry. The usage and payment records of 1403 Canadian post-paid wireless customers are examined over a 3.7-year study period. Pearson's χ 2 test and hazard function graph are used to reveal how customer churn rate is influenced by rate plan suitability. The statistical analysis demonstrates that customers who are using optimal rate plans have lower churn rate than those with non-optimal ones.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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