Conversational intelligence helps telecom giant prevent churn and retain 12K customers monthly

Retain telecom customers through AI churn prevention

In the telecom industry, retaining customers is a survival strategy. Rising competition, customer dissatisfaction, and delayed churn detection often leave providers reacting too late. A global telecom giant partnered with Grid Dynamics to modernize its AI churn prevention system, transforming millions of customer interactions into real-time insights that drive proactive retention.

Why legacy churn models aren’t enough

Traditional churn models provided a static view of risk, estimating monthly churn probabilities without explaining how or why customers were leaving, resulting in:

• High false positives and late detection of at-risk users
• No visibility into how churn risk evolves over time
• Lack of insights from millions of call transcripts
• Inability to personalize retention campaigns

The company needed a smarter, faster, and more explainable AI system that could reveal churn drivers, predict risk evolution, and guide precision interventions before customers switched providers.

AI-driven conversation intelligence for proactive retention

Grid Dynamics built a modern churn modeling solution for extracting insights from call transcripts and other textual data sources, offering a holistic framework for churn driver analysis, profiling, and actionable recommendations. Core capabilities included:

• Churn driver analysis: Extracts churn topics such as pricing, service quality, and contract issues from millions of recorded conversations to identify root causes and sentiment drivers.
• Sequential survival modeling: Uses a Long Short-Term Memory (LSTM) architecture enhanced with attention layers to better understand churn drivers and predict churn. The model dynamically tracks churn probability across time, improving predictive accuracy by up to 10% (Area Under the Curve, AUC).
• Temporal risk profiling: Quantifies how churn probabilities change over time and links these patterns with Lifetime Value (LTV) to optimize retention offer timing and treatment selection.

Measurable business impact

The solution enhanced churn prediction and risk identification, delivering substantial, quantifiable results:

• +15% more churners identified in the top-risk segment
• +5–8% higher monthly churn prediction accuracy
• 7K–12K customers retained per month
• 33% of churners identified through call transcript analysis
• 28K–33K customers reached monthly with personalized retention offers

With real-time churn visibility and AI-driven insights, marketing teams could act faster, deploying hyper-targeted, data-backed campaigns that matched treatments to customer intent and sentiment. This shift turned churn prevention from a reactive process into a predictive, continuous optimization loop. 

Learn how AI turns customer churn into retention strategies

Explore how AI-driven analytics, conversational AI, call transcript intelligence, and sequential survival modeling empower high-attrition enterprises to reduce churn, personalize engagement, and optimize retention strategies across millions of customers.