Customer Churn Prediction

Get insights into customer lifetime value and address churn risk among high-value customers and design retention strategies to improve loyalty

This turnkey enterprise AI solution enables the marketing and sales teams to get deep insights into customer lifetime value, monitor triggers for churn, strategize and effectively execute retention strategies suitable to different clusters of customers. Get in touch with us if you like to explore how this solution can suit your requirements.

Key Metrics

Churn Prediction Accuracy with other AI Models


Churn Prediction Accuracy with AI Models


Retailers and e-commerce businesses are actively working to understand their customers' behavior, identify the factors that lead to customer churn, and devise strategies to retain customers. These efforts are particularly important for customers who have a high lifetime value.


By analyzing data such as purchasing history, demographics, and interactions with the company, retailers can gain valuable insights into why customers may stop shopping with them. Furthermore, by implementing machine learning algorithms that can analyze this data and identify patterns or trends, companies can more effectively predict which customers are at risk of churning and what strategies can be employed to retain them. 


Retailers can enable targeted and personalized retention efforts, such as email campaigns, discounts, or addressing operational issues by building a customer churn prediction model and training it on historical customer data. Additionally, by analyzing the characteristics of different customer segments, retailers can tailor these retention efforts to meet the needs of specific segments while improving revenue and improving ROI on retention budgets. The business can also address customer satisfaction and revenue challenges by incorporating relevant data into the churn prediction model and monitoring key customer metrics in real-time.


  • Collect and analyze customer data such as purchase history, demographics, and any interactions with the company such as service calls or requests
  • Build and use a churn prediction model using historical customer data
  • Differentiate between addressable and random churn
  • Implement personalized retention efforts for customers identified as at-risk
  • Monitor customer engagement and behavior on an ongoing basis
  • Use dashboards to track and analyze customer churn rate and retention efforts
  • Get data-driven insights into customer behavior and preferences

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* Values are approximates arrived at based on earlier experience and/or existing literature. Contact us to find out how you can measure the ROI on this solution for your business