The telecom industry is highly competitive, and customer retention plays a vital role in ensuring continued business success. Retaining customers is crucial because they are hard-earned, and their departure can directly affect the revenue stream. Currently, the Retention department of the telecom company operates reactively: engaging customers only after they have terminated their contracts. This approach leaves room for improvement in identifying at-risk customers before they make the decision to leave.
The primary goal of this project is to shift from a reactive to a proactive approach in retaining customers. By analyzing and understanding customer behavior more effectively, we aim to develop a predictive model that can identify customers at risk of leaving and offer solutions to retain them before termination.
The team will collaborate with the engagement partner at PwC, combining internal insights with expert guidance to enhance the effectiveness of the customer retention strategy.
In the current model, the telecom company’s Retention department deals with customer churn after it happens, which is a reactionary approach. By the time the customer terminates the contract, it is often too late to intervene effectively. The existing customer analysis, primarily done through Excel, has failed to provide the actionable insights required to predict churn and take preventive measures. The analysis ends in a dead-end due to the lack of sophisticated predictive tools and a clear visualization of data that could inform decision-making.
Therefore, the challenge is twofold:
Identification of At-Risk Customers: Currently, the company lacks the tools or methodology to predict which customers are likely to terminate their contracts in advance. Data Visualization and Clarity: The data gathered on customer behavior has not been visualized in a way that’s intuitive and easily understandable for management. This hampers the decision-making process at higher levels. The project’s objective is to identify ways to predict customer churn before it happens and visualize the customer data effectively, enabling better strategic decision-making.
- Data cleaning, transformation, and modeling
- Visualization using Power BI
- Business insights generation
Data Collection & Understanding:
❖ Customer Profile Data: Gather comprehensive data, such as customer demographics, usage patterns, service issues, and billing history, to understand customer behavior and preferences. Historical Churn Data: Look into past customer churn patterns to detect commonalities among customers who left. This could include factors like contract length, service usage, payment history, or previous complaints.
❖ Customer Interaction Data: Investigate customer interactions with the company, including call center inquiries, complaints, and service requests, to identify potential signals of dissatisfaction. Predictive Modeling:
❖ Churn Prediction Models: Use machine learning algorithms like logistic regression, decision trees, or random forests to predict customer churn. These models can identify key factors that lead to churn and generate risk scores for customers.
❖ Risk Scoring System: Develop a system that assigns a churn probability score to each customer, highlighting those who are most at risk. Early Warning System: Build an alert system that notifies retention managers when a customer’s risk score exceeds a certain threshold, enabling proactive intervention.
❖ Data Visualization & Reporting: Dashboard Creation: Develop a dashboard to visualize churn risk factors, customer segmentation, and predictive scores. Use data visualization tools such as Power BI or Tableau for easy-to-understand reporting.
❖ Self-Explanatory Reports: Ensure the reports and dashboards are clear and self-explanatory, providing management with actionable insights without requiring deep data expertise.
❖ Customer Segmentation: Visualize the data in segments (e.g., high-risk vs. low-risk customers) to enable targeted retention strategies. Actionable Insights:
❖ Personalized Retention Plans: Based on the predictive model, design tailored retention strategies for at-risk customers. This could include personalized offers, service adjustments, or proactive customer service outreach.
❖ Feedback Loop: Establish a feedback loop where the Retention department can fine-tune predictive models based on new data and outcomes, creating a continuously improving retention strategy.