Customer Attrition

Requirements Clarification & Assessment

1. Understanding Customer Attrition:

   • Define customer attrition, also known as customer churn, as the phenomenon where customers stop doing business with a company, such as canceling subscriptions or not renewing services.
   • Identify the importance of predicting churn to enhance customer retention strategies and improve business profitability.

2. Clarifying Business Goals:

   • Ascertain the primary objectives of forecasting churn, such as reducing churn rate, increasing customer lifetime value, or understanding customer dissatisfaction.
   • Determine the key performance indicators (KPIs) that will measure the success of churn prediction efforts.

3. Data Requirements:

   • Identify the types of data needed, including demographic information, transactional data, customer engagement metrics, and historical churn data.
   • Assess data quality and availability, ensuring that sufficient data is accessible for building robust predictive models.

4. Technical Considerations:

   • Determine the computational resources and tools required, such as machine learning platforms, data storage solutions, and model deployment environments.
   • Establish the timeline for model development, validation, and deployment.

5. Stakeholder Engagement:

   • Engage with stakeholders, including marketing, customer service, and IT teams, to gather insights and align expectations.
   • Communicate the potential impact of churn prediction on business operations and customer relationships.