Churn Analysis

Key Metrics in Churn Analysis:

• Churn Rate: This is the percentage of customers lost over a specific period, calculated by dividing the number of customers lost by the total number of customers at the beginning of that period.
• Customer Lifetime Value (CLV): CLV helps measure the total revenue a business can expect from a single customer. It is crucial for identifying high-value customers at risk of churning.
• Average Revenue Per User (ARPU): This metric evaluates the revenue generated per customer, helping businesses identify high-risk customers who contribute significantly to revenue.

Factors Influencing Customer Churn:

• Product/Service Quality: Poor experiences with a product or service can lead customers to discontinue usage.
• Customer Engagement: Lack of interaction or engagement with customers may result in them seeking alternatives.
• Competitive Landscape: The presence of better options from competitors can influence customer decisions to leave.
• Pricing Structure: Uncompetitive pricing or unclear billing can drive customers to churn.

Steps in Conducting Churn Analysis:

• Data Collection: Gather data on customer behavior, transactions, and demographics.
• Segmentation: Group customers based on similar characteristics or behaviors to identify patterns.
• Predictive Modeling: Use statistical methods or machine learning algorithms to predict future churn based on historical data.
• Actionable Insights: Develop retention strategies, such as personalized offers, customer feedback mechanisms, and proactive engagement.

Other Terms: