Feedback Based Telecom Churn Prediction Using Machine Learning
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 an industry with stiff competition among individual organizations, Churn is an important factor to be considered by the company itself as well as by prospective customers. In the telecommunication industry, churn can be affected by multiple factors: a customer's preference, location, job, and so on. Thus, customer churn in the telecom industry is a widely studied subject. However, churn based on previous rates alone is not enough to predict future churn and there must be additional factors considered. We propose a method to overcome this inadequacy. By using the predictions generated by past data, a rough estimate of the churn rate for a certain time period can be generated, such as a quarter or a year. A Machine Learning algorithm can be used for the same to get the value of the prediction. This value can be further tweaked by incorporating customer feedback which can affect the churn rate. Thus, the value generated by the predictions and the feedback will be more accurate.
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 | 0.000 | 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 | 0.001 | 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) | 0.007 | 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