Telecom Customer Churn Prediction

The economics of telecom churn make prediction high-value work

Acquiring a new mobile subscriber costs 3-5x more than retaining an existing one when you factor in handset subsidies, sales channel costs, and activation costs. A churn rate of 2% monthly compounds to losing nearly a quarter of your subscriber base annually. The challenge is that most retention programmes are primarily reactive: a subscriber who calls to cancel is already far along the decision path, and the conversion rate on inbound cancellation calls is lower than on proactive outreach to subscribers who are at risk but haven't yet decided. Churn prediction shifts retention from reactive to proactive by identifying at-risk subscribers while there is still time to act.

  • Churn propensity model trained on your subscriber data that identifies at-risk accounts 30-60 days before cancellation

  • Contributing factor analysis showing which signals drove each subscriber's risk score for targeted retention messaging

  • LTV-weighted risk prioritisation so retention investment focuses on high-value at-risk subscribers first

  • Retention intervention workflow integration with your CRM or contact centre platform

Recent outcomes

Voice AI · Research

6× deeper insights

Text-based interviews converted to automated phone calls

AI Automation · Ops

20k+ txns day one

Manual invoice OCR across 40+ gas stations

Loyalty · Retail

1,062 users in 4 weeks

SuperValu & Centra loyalty platform with receipt validation

SaaS · Logistics

2,000+ shipments yr 1

Multi-carrier shipping hub for Indonesian eCommerce

4.9
on Clutch
See our work

The problem

Sound familiar?

  • Is your retention team primarily handling inbound cancellation calls, reacting to subscribers who have already decided to leave?

  • Is your retention budget allocated by recency of complaint rather than by predicted churn risk and subscriber lifetime value?

Short answer

RaftLabs builds customer churn prediction systems for telecom operators: machine learning churn propensity models trained on subscriber behaviour and usage data, at-risk subscriber identification with contributing factor analysis, retention intervention workflow automation, and lifetime value scoring integrated with churn risk for prioritised retention investment. Telecom churn prediction systems typically achieve 80 to 90% recall on churners identified 30 to 60 days before cancellation.

Key takeaways

  • Per-subscriber SHAP values surface the top contributing factors behind each risk score, so retention agents address the actual cause, a usage drop, a complaint, a competitor offer, rather than a generic script.
  • LTV-weighted prioritisation routes high-value at-risk subscribers to agent outreach and low-value ones to automated channels, preserving agent capacity for where it matters most.
  • Measuring real impact requires a randomised holdout: at-risk subscribers split into treatment and control groups, since without it you can't distinguish retained-by-intervention from would-not-have-churned-anyway.
  • PSI-based monitoring on key input features triggers automatic retraining when subscriber behaviour drifts, so the model doesn't silently degrade as competitor offers and network conditions change.

Trusted by

Vodafone logo
Aldi logo
Nike logo
Microsoft logo
Heineken logo
Cisco logo
Calorgas logo
Energia Rewards logo

Churn prediction delivery, by the numbers

churner recall at 30-60 day prediction window (typical)
80-90%
products shipped
100+
industries served
24+
cost delivery
Fixed

The economics of telecom churn make prediction high-value work

Churn prediction shifts retention from reactive to proactive by identifying at-risk subscribers while there's still time to act on the underlying dissatisfaction, rather than waiting for the cancellation call.

Capabilities

What we build

  • 01
    Churn propensity model

    CDR-derived usage-trend and network-quality features feeding a calibrated gradient-boosted model, updated on a configured schedule.

    Built with
    XGBoost · LightGBM
  • 02
    Contributing factor analysis

    Per-subscriber factor breakdowns translated into plain language for the retention agent's view.

    Built with
    SHAP
  • 03
    LTV-weighted prioritisation

    Risk-LTV matrix segmenting subscribers into treatment tiers, with uplift modelling filtering out unresponsive churners.

  • 04
    Retention intervention automation

    Channel- and offer-personalised outreach driven by contributing factors, with acceptance data feeding back into the model.

  • 05
    Churn analytics dashboard

    Commercial and operations views tracking AUC-ROC, retention ROI, and intervention performance in one place.

  • 06
    Model monitoring and retraining

    PSI-based drift detection triggering an automated retrain-validate-promote pipeline as subscriber behaviour shifts.

How we work

From scope to live churn prediction system

  1. Week 1
    01

    Subscriber data and churn rate scoping

    We map your subscriber base size, current churn rate, and what data you have available. You leave week 1 with a written scope document and a fixed-price quote.

  2. Weeks 2-5
    02

    Feature engineering and holdout design

    CDR feature extraction, prediction window, and holdout measurement methodology designed against your retention programme.

  3. Weeks 6-13
    03

    Build and validate

    Model training, contributing factor analysis, and CRM integration built in parallel, validated on held-out data.

  4. Final 2-3 weeks
    04

    Launch and retention team training

    Retention agents and operations teams trained on the new workflow before full rollout.

Why us

Why telecom operators choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your subscriber data also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building telecom and ML platforms.

  • 04
    Measurement methodology built in from day one

    A randomised holdout is designed alongside the model, not added later when someone asks for proof.

  • 05
    Monitoring designed so the model doesn't silently decay

    Drift detection and retraining run without manual intervention as the market changes.

Have a churn prediction project?

Tell us your subscriber base size, current churn rate, and what data you have available. We will scope the model and give you a fixed cost.

Talk about your churn prediction project

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Frequently asked questions

Models work best with 12-24 months of historical data: CDR data (call frequency, duration, data volume, roaming), billing and payment history, CRM interaction logs, contract history, network quality exposure, and churn labels. We conduct a data readiness assessment before scoping.

There's a trade-off between lead time and accuracy. For most telecom retention programmes, a 30-60 day prediction window is the practical optimum, enough lead time to act, with accuracy sufficient to make outreach commercially viable.

A randomised holdout is the standard approach: at-risk subscribers split into a treatment group receiving intervention and a control group that doesn't, with the churn rate difference giving a clean causal estimate of prevented churn.

Yes. Integration options include an API endpoint for risk scores, scheduled at-risk list exports, webhook triggers on risk threshold crossing, and direct database write-back. We integrate with Salesforce, Siebel, Oracle CX, and custom BSS/CRM platforms.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope Customer Churn Prediction for Telecom in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.

Go deeper

Browse our case studies

Telecom software by area