Case Study: Churn Prediction in Telecommunications

Client Overview

Our client is a large, established telecommunications provider offering mobile, internet, and television services to millions of subscribers. In a highly competitive market characterized by low switching costs and aggressive competitor promotions, customer churn represented a significant threat to their revenue and market share.

Problem Statement

The client was experiencing a consistently high customer churn rate, leading to substantial revenue loss and increased customer acquisition costs. Their existing methods for identifying at-risk customers were largely reactive and based on simple rules, failing to capture the complex interplay of factors that drive churn. They lacked a proactive mechanism to identify customers likely to churn before they actually left, and therefore, could not implement timely and effective retention strategies. This resulted in a reactive approach to customer management, often too late to prevent attrition.

Solution Implemented: AI-Powered Churn Prediction Model

DSIGHTS developed and implemented an advanced machine learning-driven churn prediction model. This solution analyzes a wide array of customer data to predict which customers are most likely to churn in the near future. Beyond just prediction, the model also identifies the key factors contributing to an individual customer's churn risk, enabling the client to develop highly targeted and personalized retention campaigns.

Step-by-Step Process

1. Data Consolidation and Preprocessing:

   We began by consolidating diverse customer data from various internal systems, including customer relationship management (CRM) databases, billing systems, network usage logs (call duration, data consumption, SMS activity), customer support interaction records (complaints, service requests), and demographic information. Data cleaning, normalization, and handling of missing values were critical steps to ensure data quality for model training.

2. Feature Engineering for Churn Prediction:

   Our data scientists engineered a comprehensive set of features from the raw data that are known to influence churn. These included usage patterns (e.g., average monthly data usage, call frequency, international calls), billing history (e.g., payment delays, plan changes), contract details (e.g., contract length, remaining contract duration), customer service interactions (e.g., number of complaints, resolution time), and demographic information. We also created aggregated features to capture customer behavior over time.

3. Machine Learning Model Development:

   We experimented with and selected the most effective machine learning algorithms for churn prediction, including Logistic Regression, Random Forest, Gradient Boosting Machines (e.g., XGBoost, LightGBM), and Support Vector Machines. The models were trained on historical data where customer churn status was known. Techniques like cross-validation and hyperparameter tuning were used to optimize model performance and prevent overfitting.

4. Churn Factor Analysis and Interpretability:

   Beyond just predicting churn, we focused on model interpretability. Techniques like SHAP (SHapley Additive exPlanations) values and feature importance analysis were used to identify the primary drivers of churn for different customer segments. This provided actionable insights into *why* customers were leaving, allowing the client to address underlying issues rather than just reacting to symptoms.

5. Model Deployment and Real-time Scoring:

   The validated churn prediction model was deployed into a production environment, enabling daily or weekly scoring of the entire customer base. Each customer received a churn probability score. This scoring was integrated with the client's CRM system, allowing customer service representatives and marketing teams to view the churn risk for individual customers in real-time.

6. Retention Strategy Implementation and Monitoring:

   Based on the churn scores and identified churn factors, the client implemented targeted retention strategies. This included personalized offers (e.g., discounted plans, loyalty rewards), proactive outreach from customer service, and tailored communication campaigns. We established a continuous monitoring framework to track the effectiveness of these retention campaigns and the overall impact on churn rates, allowing for ongoing model refinement and strategy optimization.

Value Added & Results

The implementation of DSIGHTS' AI-powered churn prediction solution delivered significant, measurable benefits to the telecommunications client:

• **Reduced Churn Rate:** A 12% reduction in the overall customer churn rate within six months of deployment, directly impacting revenue stability.
• **Increased Customer Lifetime Value (CLTV):** By retaining more customers and improving their satisfaction, the average CLTV saw a noticeable increase.
• **Optimized Retention Spend:** The ability to target high-risk customers with personalized offers led to a more efficient allocation of retention marketing budget, improving ROI by 25%.
• **Proactive Customer Management:** The shift from reactive to proactive customer engagement allowed the client to address issues before they escalated, improving customer satisfaction and loyalty.
• **Actionable Insights:** The model provided deep insights into the root causes of churn, enabling the client to make data-driven decisions about product development, service improvements, and pricing strategies.
• **Competitive Advantage:** The enhanced ability to retain customers provided a significant competitive advantage in a saturated market.

This project transformed the client's approach to customer retention, turning a significant business challenge into an opportunity for growth and improved customer relationships.