There are several ways that Data-Mining can be used. A useful way is through customer attrition, which includes companies. Companies should have a complete retention strategy, due to gathering new customers is costly. Being able to retain customers during a downfall, when that is happening people are looking for a lower cost alternative. Knowing why a customer is leaving is important. There are customer churn analysis and apprehending methods, and trend analysis. Along with the customer churn profiling.
Developing Customer Retention Strategies. Customer Retention is important because getting a new customer is expensive rather than keeping the existing one. Retention is meaningful to most companies because the cost of having new customers is much more than the cost of keeping good relationships with their current customers.
How to contain customers during a downfall? During a downfall is the time when people look for a lower cost alternative. In these circumstances, down-selling can be key to retaining customers. When economy overcomes, the customers are likely to move up to the original services they had subscribed too. Down falling is better than losing customers. However that can have negative effects on sales, this should be carried out carefully on customers who are at risk of churning to lower cost alternatives, methods can be used to identify customers at risk of churning.
Knowing why a customer leaves. The most important thing in customer retention is to know the customer well enough, know there expectations, satisfactions, and their tendencies. By knowing these about a customer the more efficient retention strategies can be developed. And this will be the key to successful retention marketing.
Customer churn analysis and apprehending methods. The importance of a customer retention strategy is to recognize the customers who are likely to churn. Once they are identified, actions can be taken. These analytic techniques are used to identify customer groups that are at high defection risk;
• Churn trend analysis: Analyze trends in customer segments. Customer segments with increasing churn trends can be identified.
• Customer churn profiling: Develop profiles of churn risk groups based on demographic, geographic, psychographic attributes and service usage patterns.
• Customer churn scoring and segmentation: Build predictive models that can predict likelihood of defection and perform segmentation based of defection scoring.
Customer churn trend analysis. Trends can be determined with time-series analytic tools. It can describe trends of customer segments in different ways: geographic, demographic, psychographic factors, and others. It uses time-series modeling to identify trends. A powerful tool for detecting churn trends are sales trend analysis.
What are customer churn profiling? Profiling churns can tell customer segments and reasons why customers are leaving the service. Combined customer information can be very large. It may be dozens of fields. Analyzing data with many variables is a challenge. Profiles of high risk groups can be exactly identified right away.
References
Rosella. (2005). Retrieved May 24, 2013, from Predictive Knowledge & Data Mining: http://www.roselladb.com/customer-retention.htm