Stop losing customers to competitors. Our AI identifies at-risk subscribers 60 days in advance, automates personalized retention campaigns, and reduces churn rates by 35% while maximizing customer lifetime value.
Telecom operators lose billions annually to customer churn. Traditional retention approaches react too late and waste budget on customers who weren't planning to leave.
Average telecom churn rates range from 15-25% annually, costing operators $65B globally in lost revenue.
By the time operators identify at-risk customers, 73% have already decided to switch providers.
45% of retention offers go to customers who weren't planning to leave, wasting millions in discounts.
One-size-fits-all retention campaigns have only 12% success rate because they ignore individual customer needs.
Boaweb AI analyzes hundreds of behavioral signals to predict which customers will churn, when they'll leave, why they're leaving, and what retention offer will be most effective.
Our AI analyzes 200+ customer signals in real-time—usage patterns, service quality metrics, billing interactions, customer service contacts, payment history, contract details, competitor activity, and demographic factors. Machine learning identifies subtle behavioral changes that indicate dissatisfaction up to 90 days before a customer churns, giving you time to intervene proactively.
Each customer receives a churn risk score (0-100) updated daily based on their behavior and profile. The AI segments customers into risk tiers—critical (90-100), high (70-89), medium (40-69), and low (0-39)—and predicts the most likely churn timeframe. This allows your retention team to prioritize high-value, high-risk customers and allocate resources efficiently.
The AI doesn't just predict who will churn—it explains why. Natural language processing analyzes customer service transcripts, social media posts, and survey responses to identify dissatisfaction drivers: network quality issues, billing disputes, competitor offers, service limitations, or poor support experiences. Understanding the "why" enables targeted retention strategies that address actual pain points.
For each at-risk customer, AI recommends the optimal retention strategy—price discounts, plan upgrades, service improvements, loyalty rewards, or targeted support. Machine learning models predict the success probability and ROI of each intervention based on similar customer profiles. This ensures retention budget is spent on offers that actually work, not generic discounts that erode margins.
The system automatically triggers personalized retention campaigns via email, SMS, app notifications, or customer service alerts based on churn risk and timing. AI continuously monitors campaign performance, conducting A/B tests on messaging, offers, and timing to optimize conversion rates. The system learns which strategies work for different customer segments and automatically improves over time.
Discover which customers are at risk of churning and what retention strategies will actually work. Get a free churn analysis of your customer base.
A Swedish mobile operator with 2.1 million subscribers was experiencing 21% annual churn rate, losing approximately 36,750 customers monthly. Their retention team relied on manual analysis and generic win-back campaigns, resulting in only 9% success rate. Annual revenue loss from churn exceeded €89M.
Boaweb AI Solution: We deployed a machine learning churn prediction system that analyzes behavioral data from billing, network usage, customer service, and payment systems. The AI scores all 2.1M customers daily, identifies churn drivers, and triggers personalized retention campaigns automatically through email, SMS, and customer service alerts.
Results after 12 months: Churn rate decreased from 21% to 13.2% (37% reduction), saving approximately €34M in lost annual revenue. Retention campaign success rate improved from 9% to 43%. Customer lifetime value increased by €187 per subscriber. The AI system identified that network quality issues were the primary churn driver for premium customers, leading to targeted infrastructure improvements that further reduced churn.
Our AI achieves 89% prediction accuracy 60 days before churn, compared to 45-60% accuracy for rule-based models. Machine learning analyzes hundreds of behavioral signals that humans can't process manually—subtle usage pattern changes, service quality trends, payment timing shifts—to identify at-risk customers much earlier and more reliably than traditional segmentation.
The system integrates with your existing data sources: billing systems (payment history, plan details), network telemetry (usage patterns, service quality), customer service platforms (support tickets, call transcripts), CRM (demographics, contract info), and marketing systems (campaign responses). More data improves accuracy, but the AI can start making predictions with just billing and usage data.
AI analyzes historical retention campaign results to understand which offers work for different customer segments and churn drivers. For example, customers leaving due to price sensitivity respond to discounts, while those frustrated by network quality need service improvements. The system runs propensity models to predict offer success rates and recommends the intervention with highest expected ROI.
The AI both predicts and prevents. It identifies at-risk customers early enough for effective intervention, explains why they're at risk, recommends personalized retention strategies, and automates campaign execution. Some clients integrate the AI with service systems to proactively improve network quality or resolve billing issues before customers even complain, preventing churn drivers entirely.
Most telecom operators see 30-40% churn reduction within 12 months, translating to millions in retained revenue. Additionally, retention budget efficiency improves 3-5x as offers target genuinely at-risk customers with appropriate interventions. For a mid-sized operator with 20% churn and €500M annual revenue, a 35% churn reduction retains €35M annually while reducing retention costs by 40%.
Stop losing customers to competitors. Get an AI-powered churn prediction system that identifies at-risk customers early and automates personalized retention strategies that actually work.
Includes customer risk analysis and retention strategy roadmap.