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A telecom provider is now able to see high churn risk customers 3 months prior
A major telecom provider was losing 15% of its subscribers annually. Their retention team only reached out after a customer filed a cancellation request. Using predictive analytics, they now identify at-risk customers 3 months in advance.
Service:
Predictive analytics
Timeline:
January 2025 - June 2025
Country:
Ukraine
Industry:
Saas
Company:
NDA, National Telecom Operator of Ukraine
Summary:
Winning back telecom customers is nearly impossible once they have signed with a competitor. We implemented a predictive model that identifies at-risk subscribers and their specific frustrations three months before they leave. This allows the team to resolve issues and rescue the relationship before the customer considers switching.
Business
The company is an established provider with over 70,000 subscribers. With a 15% annual churn rate, they needed a way to protect revenue by keeping existing customers rather than just focusing on new sign-ups.
Problem
The main challenge was timing. Reaching out during a cancellation call is usually too late because the customer has often already signed a contract with a competitor. At this stage, trust is lost and even deep discounts rarely work. The company needed to identify unhappy customers while they were still in a position where they could be saved.
Solution
Step 1: Consolidated data from separate systems
The project began by gathering all customer information into one place. This included payment history and technical logs, support calls etc. We connected data that was previously stored in different departments. This provided a unified view of how each subscriber experienced the service over time.
A unified view that combines historical records with AI predictions for every customer (anonymized)
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Step 2: Identified quiet signals of frustration
We used the organized data to find specific behaviors that happened before a customer decided to quit. These signals acted as quiet clues. Examples included a slight drop in internet speed or a sudden change in how a customer paid their bills. These small signals were often too subtle for people to notice manually.
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Step 3: Trained the predictive model
We trained a predictive model using thousands of examples of past customers who had already left. The model learned to recognize the patterns that led to those departures. The system now assigns a risk score to every subscriber based on these patterns. This allowed the provider to predict an exit before a formal complaint was ever made.
Step 4: Shifted to proactive outreach
The retention team began receiving a weekly list of these at-risk customers. Instead of waiting for a complaint, they called the customer with a direct solution. They fixed technical glitches or offered better price plans while the customer was still happy to talk. This approach turned a potential cancellation into a positive service experience.
Results
- 80% accuracy in identifying at-risk customers up to three months before they reached out to cancel.
- 8,400 subscribers were identified for rescue every year before they could switch to a competitor.
- Reduced churn because the team is addressing hidden frustration points before they escalated.
- Communication became hyper-personalized because the team knew exactly who would churn and why.
