Churn Prevention Using Survival Analysis and Next-Best-Action Modeling

Synopsis

Most churn prediction systems only tell you which customers might leave, but not when they will leave or what to do about it. This product solves both problems by predicting not just who will churn but when each customer is likely to leave, then automatically recommending the best action to keep them. The system treats customer churn as a timing problem rather than a simple yes or no question. It analyzes when customers typically leave and what factors influence that timing, then uses this understanding to generate specific recommendations for retention teams. The system includes both automated daily processing for analyzing your entire customer base and instant predictions for individual customers through an API, so retention teams can take action immediately.

Survival analysis Kaplan Meier CoxPH Gradient Boosting C index and Brier Lift@K Next Best Action Batch + FastAPI

1. Problem the product solves

Classification answers if a customer will churn within a window. Survival analysis answers when churn becomes likely for each individual. Timing unlocks proactive outreach, smarter incentive timing, and better budget allocation.

North star: improve lift in the top risk bucket while keeping survival probabilities calibrated at short and medium horizons.

2. Solution overview

The product converts a telecom churn dataset into a survival format, explores segment level retention with Kaplan Meier curves, trains two models, and then operationalizes the best model for both batch campaigns and real time calls. The system also includes a practical decision matrix that maps risk and value to the next best action so teams know exactly what to do and when to do it.

Time to event framing Dual model track Prescriptive layer

3. Data and survival transformation

Source: IBM Telco Customer Churn. Tenure in months is the time variable. Event flag is 1 for churned customers and 0 for right censored customers who were still active at last observation. Categorical fields are one hot encoded. A compact autopay flag is derived from payment method. Senior citizen, contract type, internet service type, support add ons, and billing fields are retained.

This structure preserves information from active customers and avoids bias that a plain classifier would create by discarding censored rows.

4. Exploratory survival analysis

Kaplan Meier curves show how retention changes with time. The full cohort curve steps down as churn events occur. Segment curves reveal where risk concentrates.

Contract: month to month customers drop fast. One year and two year stay longer.
Internet service: fiber has higher churn than DSL, and both are higher than no internet.
Autopay: non autopay customers churn more than autopay users.
Senior status: seniors churn faster than non seniors.

Log rank tests confirm that these curves differ in a statistically meaningful way. These insights shape features and the action plan.

5. Modeling approach

5.1 Cox Proportional Hazards

The Cox model estimates how each feature scales the hazard of churn. Hazard ratios give clear levers. Month to month raises risk relative to two year. Non autopay raises risk relative to autopay. Fiber raises risk relative to DSL. We check proportional hazards with standard diagnostics and proceed when reasonable.

5.2 Gradient Boosting Survival

A gradient boosted survival model captures non linearities and interactions while optimizing a Cox style objective. Tuning targets estimators, depth, learning rate, and subsampling. This model usually ranks risk better while Cox remains easier to explain.

Interpretability vs accuracy Use both for insight and power

6. Evaluation protocol

6.1 Concordance index

C index measures how well the model orders customers by time to churn. Values well above 0.5 show useful discrimination. The boosted model typically edges out Cox on this metric.

6.2 Time dependent Brier score

Brier score evaluates the accuracy of predicted survival probabilities at chosen horizons with proper censoring weights. Lower is better. Curves across 6 to 24 months help judge calibration.

6.3 Lift at top K percent

Lift answers a business question. If we target the top decile by risk, how many more churners do we capture compared to random targeting. Strong lift in the top decile means efficient spend.

7. Results at a glance

Kaplan Meier confirmed contract length, autopay, internet type, and senior status as strong drivers.
Cox delivered clear hazard ratios that match the survival curves. It works as a story engine for stakeholders.
Gradient boosting improved rank order and reduced Brier at common horizons. It is the production scorer.
Lift improved in the top decile which makes campaigns cheaper for the same save rate.

Aspect	CoxPH	Boosted survival
Interpretability	High with hazard ratios	Lower, explain with feature importance
Discrimination	Good	Very good
Calibration	Good	Good to very good
When to use	Explain drivers and set policy	Score customers for campaigns

8. Next Best Action strategy

Prediction has value only when it leads to action. The system segments by churn risk and by customer value, then maps each cell to a concrete play. High value and high risk gets a high touch offer and an early renewal path. Medium risk gets automated education or usage nudges. Low risk is monitored so spend is not wasted.

Timing matters. Survival curves tell us when risk spikes. Outreach is scheduled before that spike, not after it.

A future iteration can add true uplift modeling once A B tests produce treatment response data.

9. Deployment paths

9.1 Batch scoring

A scheduled job loads the latest snapshot, applies the preprocessing pipeline and the trained model, computes survival probabilities at chosen horizons, and exports a scored file with a recommended next action for each customer. This feeds marketing tools and CRM lists.

9.2 Real time API

A FastAPI service loads the serialized pipeline and model on startup and exposes a POST endpoint. The endpoint validates payloads with Pydantic, runs the pipeline, returns a risk score and survival probabilities, and can be queried by an agent desktop or a web app.

10. Engineering details

Preprocessing: ColumnTransformer for numeric scaling and one hot encoding for categoricals. Same transformer is reused in training and inference.
Feature hints: contract, autopay flag, internet type, senior citizen, add on count, support features, monthly charges and simple ratios.
Validation: train test split with stratified churn rate, optional cross validation for tuning the boosted model.
Serialization: joblib for the full pipeline plus model bundle to keep inference consistent.
Monitoring: track C index drift and Brier at 6 and 12 months, track campaign lift by decile, and audit calibration with reliability plots.

11. Limits and future work

Static features only. Add time varying covariates like monthly usage and ticket counts.
No direct uplift modeling yet. Introduce controlled experiments and train meta learners.
Single event focus. Explore competing risks to separate price churn from service churn.
Localization. Tune actions and thresholds by region and plan type.

FAQ

Why survival analysis instead of a classifier

It uses censored information from active customers and gives time based probabilities. That supports proactive timing which a plain classifier cannot do well.

Which model should I trust day to day

Use the boosted survival model for scoring and the Cox model for explanations. They complement each other.

How do I act on the scores

Use the decision matrix. Pick actions that match both risk and value. Schedule contact before the customer’s risk spike.

What does a good result look like

Higher lift in the top decile and well calibrated probabilities at the horizons that matter to your business. That translates to fewer lost customers for the same budget.