Every business understands the importance of acquiring and sustaining its customer base. In a competitive, global economy, even the smaller local businesses can lose customers to competitors on the other side of the world. The cost of acquiring a new customer includes marketing and advertising, resources and personnel, customer support, search engine optimization and more. The cost to sustain a customer relationship is equally important and encompasses customer care, online and personal support, the cost of introducing new products and services, the cost of targeting specific customers with discount, offers and outreach designed to keep the customer engaged and interested.

Customer churn is the bane of business success and, while you may think that losing customers is inevitable, you can mitigate this problem by understanding which customers are likely to leave and go to the competition, and where you may have reached ‘saturation’ with products and services.

Customers don’t usually tell you that they are leaving or, more importantly, they don’t tell you WHY they are leaving. They simply close their account or stop visiting your site. Self-serve, assisted predictive modeling and predictive analytics can help you to identify the customers who are most likely to leave and allow you to develop processes and strategies, as well as new marketing, new products and services, and other strategies that will improve customer retention and reduce customer churn.

Predictive Analytics can help you identify those customers who are most likely to leave and improve marketing messaging and marketing campaigns to get to the issues your customers have and to create new services and products that will help you retain a customer.

Use Predictive Analytics to identify at risk customers and issues that will impact customer churn and customer retention.

Learn More: Customer Churn

View the Online Customer Churn Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

• Fraud Mitigation
• Quality Control
• Demand Planning
• Product and Service Cross-Sell and Upsell
• Maintenance Management
• Customer Targeting
• Human Resource Attrition
• Loan Approval
• Marketing Optimization
• Predictive Analytics Using External Data
• Online Target Marketing
• Students’ Academic Performance
• Crime Type Prediction

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to find out how your business users can leverage Predictive Analytics to increase the accuracy of predictive analysis and forecasting.

Advanced Analytics Use Cases, Augmented Analytics Use Cases, Customer Churn Analysis, Customer Churn Analytics, Predictive Analysis Use Cases, Predictive Analytics Use Cases