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Customer Churn Prediction and Prevention System
  1. case
  2. Customer Churn Prediction and Prevention System

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Customer Churn Prediction and Prevention System

unosquare.com
Telecommunications

High Customer Churn Rate

Acme Telecommunications is experiencing a high customer churn rate, leading to significant revenue loss and increased customer acquisition costs. The current methods for identifying at-risk customers are reactive and not effective enough. There's a lack of visibility into the key drivers of churn, hindering proactive intervention strategies.

About the Client

Acme Telecommunications is a large provider of internet, phone, and television services to residential and small business customers. They are facing increasing customer churn and are looking for ways to proactively retain customers.

Project Goals

  • Reduce customer churn rate by 15% within the next year.
  • Improve customer retention by proactively identifying and addressing at-risk customers.
  • Gain deeper insights into the factors contributing to customer churn.
  • Develop and implement personalized retention strategies.

Functional Requirements

  • Customer churn prediction model (using machine learning).
  • Risk scoring system for individual customers.
  • Automated alerts for high-risk customers.
  • Dashboard visualizing churn trends and key drivers.
  • Reporting on the effectiveness of retention campaigns.

Preferred Technologies

Python
Scikit-learn
Cloud platform (AWS or Azure)
Data visualization tools (Tableau or Power BI)

Integrations Required

  • CRM system (Salesforce or similar) for customer data
  • Billing system for usage data
  • Customer support platform for interaction data

Key Non-Functional Requirements

  • Scalability to handle a large customer base.
  • High performance for real-time churn prediction.
  • Data security and privacy compliance (GDPR, CCPA).
  • Reliable and available system uptime.

Expected Business Impact

By reducing churn, Acme Telecommunications will see increased revenue, lower customer acquisition costs, and improved customer lifetime value. The insights gained from the churn prediction model will also allow for more effective marketing and customer service strategies, leading to higher customer satisfaction.

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