A Churn-Strategy Alignment Model for Managers in Mobile Telecom
Why this work is in the frame
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Bibliographic record
Abstract
Customer churn is a vexing problem in the telecom industry. Data mining techniques play an important role in churn prediction. However, most of these techniques can only provide a result that customers may churn or not, but seldom tell why they churn. Therefore even an accurate prediction result is of minimal use to telecom management, especially to the strategies of customer retention. In this paper, we propose a new model for strategic alignment of churn predictors to an adaptation of the Delta strategic model for firm competitiveness. This model is substantiated using a dataset from Duke University's Teradata Center for CRM. Research results contribute to analyzing churn predictors from a new perspective - that of organizational competitiveness strategy. Using factor analysis, the model links high-level churn predictors with competitiveness strategy.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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