Customer Churn Prediction in Telecom Using Delta Lake

In today’s hyper-competitive telecom market, customer expectations are rising, prices are converging, and switching providers has never been easier. As a result, customer churn has quietly become one of the most costly threats to profitability.

What makes churn particularly challenging is its subtlety. Early warning signs like reduced data usage, fewer top-ups, or less app engagement often slip through the cracks. By the time a customer leaves, it’s already too late.

But telecoms sit on a goldmine of behavioral data. The real opportunity lies in transforming that data into proactive retention. This article explores how 0to60.ai, in collaboration with Delta Lake, helps telecom operators predict churn with precision and deploy timely, personalized interventions that drive loyalty and protect revenue.

Despite years of investment in CRM tools, segmentation strategies, and loyalty programs, many telecoms still can’t confidently answer two critical questions:

Who is likely to churn and when?

The common pitfalls include:

• Static customer segments that ignore real-time behavior shifts
  
• Fragmented data across billing, service, and network systems
  
• Broad, generic outreach that misses the real at-risk users

The result? Retention campaigns are either misdirected or too late, wasting budgets and leaving valuable subscribers behind.

To break this cycle, churn prediction must become a living, behavioral intelligence system not a one-time analytics project.

At 0to60.ai, we bring a low-code, AI-powered platform designed for telecom-grade scale and complexity. Our solution makes predictive churn modeling accessible without deep data science teams or custom infrastructure.

Here’s how it works:

1. Unified Data Foundation with Delta Lake

Delta Lake acts as the data engine—consolidating batch and streaming data across all customer touchpoints: call logs, complaints, recharge behavior, app usage, and more.

Its schema evolution, ACID compliance, and time travel features ensure that every behavioral signal is tracked accurately, securely, and in real time.

2. Behavioral Modeling Beyond Demographics

We move beyond age, region, and ARPU. Our models analyze signals like:

• Drops in data usage
  
• Increased complaints or call drops
  
• Fewer recharges or plan downgrades
  
• Reduced interactions with the mobile app
  

These features are fed into machine learning models that evolve over time—capturing behavioral drift and external factors.

3. Time-to-Churn Forecasting with Survival Analysis

Instead of simply classifying churn risk, we use survival analysis to estimate when a customer is likely to leave. This enables operators to reach out with the right offer at the right moment—before loyalty is lost.

4. Seamless Experimentation and Deployment

Thanks to MLflow, all models are tracked, versioned, and monitored. New models can be deployed or rolled back with a few clicks, ensuring governance and agility across teams.

1. Data Overload:
Telecoms generate massive volumes of structured and real-time data. Delta Lake consolidates it into a single, reliable layer—ready for modeling.

2. Behavior Drift:
Customer usage patterns change rapidly. 0to60.ai’s models adapt continuously, retraining on live data to stay accurate over time.

3. Business-Data Disconnect:
Insights often get stuck with data teams. Our low-code tools and intuitive dashboards let CX teams act on churn signals without delay

With 0to60.ai, both data scientists and business teams can act on insights without depending on weeks-long dev cycles.