Telecom — Customer Churn Analysis and Retention Strategy Pipeline

Overview

The purpose of this DAG is to analyze customer churn behaviors and develop effective retention strategies within the telecom industry. Triggered by updates in customer data, it ingests various data sources including CRM data and customer interaction logs. The ingestion pipeline collects this data and prepares it for in-depth analysis. The processing steps include data cleansing, exploratory data analysis, and the development of predictive models that identify at-risk customers. These models util

The purpose of this DAG is to analyze customer churn behaviors and develop effective retention strategies within the telecom industry. Triggered by updates in customer data, it ingests various data sources including CRM data and customer interaction logs. The ingestion pipeline collects this data and prepares it for in-depth analysis. The processing steps include data cleansing, exploratory data analysis, and the development of predictive models that identify at-risk customers. These models utilize historical churn data and customer engagement metrics to generate actionable insights. The final outputs consist of tailored recommendations for marketing teams, which are stored in a centralized data warehouse for future analysis and strategy refinement. Key performance indicators (KPIs) such as churn rate reduction, customer lifetime value, and engagement metrics are monitored to assess the effectiveness of implemented strategies. The business value of this DAG lies in its ability to proactively address customer churn, ultimately enhancing customer retention and increasing revenue for telecom providers.

Part of the Fraud & Anomaly Analytics solution for the Telecom industry.

Use cases

• Reduces customer attrition through targeted interventions
• Enhances customer lifetime value with effective retention strategies
• Improves marketing efficiency with data-driven recommendations
• Increases revenue by retaining high-value customers
• Facilitates proactive decision-making in customer relationship management