Predicting Subscriber Churn with >85% Accuracy: Implementation Experience

An operator spends millions on acquiring subscribers but loses up to 30% of the base within the first six months. The cost of acquisition is 5–7 times higher than retention — each lost unit is a direct loss. According to industry reports, operators with AI systems reduce churn by an average of 20%. On a project for an operator with 5 million subscribers, we cut churn by 18% in one quarter, retaining over 30,000 subscribers. We build AI systems that predict churn with >85% accuracy and suggest what to offer to whom. Request an analytical report on your data — we’ll show which segments are at risk.

Which Signs Influence Churn?

Key feature groups: service usage (voice, data, SMS), finances (ARPU, delays), support interactions, and competitive context. Trends are especially valuable — e.g., decreasing data consumption over the last 30 days compared to the previous period. Feature engineering from BSS/OSS systems includes:

features_usage = { 'voice_outgoing_min_30d': sum(voice_outgoing_last_30d), 'voice_incoming_min_30d': sum(voice_incoming_last_30d), 'unique_called_numbers': len(unique_called_30d), 'data_usage_gb_30d': sum(data_usage_last_30d), 'data_usage_trend': data_30d - data_60_30d, 'arpu': avg_monthly_revenue, 'arpu_trend': arpu_30d - arpu_90d, 'payment_delays': count(payment_delay > 5d), 'last_payment_days_ago': days_since_last_payment } 

Interaction with the operator:

features_interaction = { 'cs_contacts_30d': count(support_contacts_last_30d), 'complaints_90d': count(formal_complaints_90d), 'nps_score': last_nps_response, 'app_logins_30d': mobile_app_logins_count } 

Competitive context: numbers ported to a competitor (MNP at segment level), price difference with analog tariff — the larger the gap, the higher the churn risk.

Why Uplift Modeling Outperforms Churn Classification

The naive approach is to give a discount to everyone who might leave. Problem: some subscribers will stay anyway (sure things), some will leave even with a discount (lost causes). The discount is only needed for persuadables — those whom the offer can tilt to stay. An uplift model (see Uplift modelling) estimates the causal effect:

Uplift = P(retained | treated) - P(retained | not treated)

Target those with uplift > 0. A meta-learner (T-Learner) builds two classifiers — for treatment and control:

model_treatment = LightGBMClassifier().fit(X_treated, y_treated) model_control = LightGBMClassifier().fit(X_control, y_control) uplift = model_treatment.predict_proba(X)[:, 1] - model_control.predict_proba(X)[:, 1] 

This yields 20% more retained subscribers for the same budget than simply ranking by churn probability. Besides T-Learner, we use S-Learner (one classifier with a treatment feature) and X-Learner (cross-learning). In practice, T-Learner gives the best uplift with sufficient sample size, while X-Learner is more robust to imbalance. The choice depends on the volume of retention campaigns and the treatment share.

How to Personalize Retention Offers?

Predicting who will leave is not enough — you need to decide what to offer. Next Best Offer (NBO) — a multiclass model that selects the offer: discount, bonus traffic, tariff upgrade, or free roaming. It accounts for CLV, ARPU, consumption type, and offer history. Timing is crucial: 30–60 days before contract end, after a negative NPS (within 48 hours), after a complaint to CS (immediately).

Telecom Churn Specifics

Types of churn: voluntary churn (conscious departure), involuntary churn (disconnection for non-payment), early churn (first 90 days). For prepaid, churn is defined by inactivity: 30/60/90 days without top-up.

Contractual vs. prepaid: Postpaid has a clear termination date; the model predicts churn at renewal. Prepaid has no contract; churn is defined by inactivity.

Multi-Horizon Models

Steps to Build a Churn Model

1. Collect and aggregate data — export from BSS/OSS, CRM, CDR for 6–12 months.
2. Feature engineering — compute usage, financial, interaction, and trend features.
3. Train a baseline — LightGBM / XGBoost with time-based cross-validation.
4. Build an uplift model — T-Learner or S-Learner with CATE estimation.
5. Calibrate NBO — multiclass model considering CLV and budget.
6. A/B testing — compare campaigns with and without the model.

Model Evaluation Metrics

What the Work Includes

• Detailed analytical report on churn factors and segmentation
• ML model (churn + uplift + NBO) with API for integration
• Integration with CRM and campaign dispatch
• Documentation and team training for the operator
• Support for 3 months after launch

Timeline and Cost

Basic churn model on BSS data – 4–5 weeks. Full system with uplift, NBO, and CRM integration – 3–4 months. Cost is calculated individually, based on data volume and complexity. Payback comes from a 15–30% churn reduction: for an operator with a million-subscriber base, savings can be significant.

Why Work with Us

We are a team of senior ML engineers with 7+ years of telecom experience. Completed 15+ churn prediction projects for operators in Russia and the CIS. We guarantee quality: our models undergo A/B testing and deliver measurable business results. Certified in AWS SageMaker and PyTorch. Get a free pilot on your data within 2 weeks — contact us for a consultation.

Common Mistakes to Avoid

• Ignoring trend features: many rely only on point-in-time metrics like current ARPU, missing the early signs in decreasing usage.
• Using only a classification model without uplift: leads to wasting budget on sure things and lost causes.
• Not calibrating NBO to budget constraints: offering too much to high-CLV users without considering total campaign cost.
• Poor timing: sending offers too late or too early reduces effectiveness.

Request an analytical report on your data — we’ll show which segments are at risk. Contact us to discuss your specific case.