The Impact of Contract Type and Monthly Charges on Customer Churn: Comparative Analysis Across Age Groups
Bibliographic record
Abstract
The study wishes to explore the specific impact of contract type and monthly expenditure on telecommunication customer churn. The study uses regression analysis to quantify the impact of these two independent variables on overall customer churn and analyzes the impact of contract type and monthly spending amount on customer churn for different customer groups by dividing customers into younger and older groups, respectively. The focus of the study is to explore which group of customers is more susceptible to the impact of contract type and monthly spending amount and to provide corresponding insights for the company and the industry in retaining customer churn. The results of the study show that both contract type and monthly spending can have a significant impact on customer churn. In the younger customer group, monthly consumption has a greater impact on churn and a negative relationship. Whereas in the older customer group, the impact of contract type is greater, and long-term contracts have a dampening effect on customer churn.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".