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“Customer Churn is often treated like the Flu, it is very difficult to totally eliminate it. However, just like medical breakthroughs it can be diagnosed and cured.”
Retention is important to most companies because the cost of acquiring new customers is much greater than the cost of keeping relationships with current customers.
The most important thing in customer retention is to understand your customers and the more you know about them the better: their expectations, satisfaction levels, demographic & geographic & psychographic tendencies, etc. If you understand more about your customers and know more about which customer groups are defecting to rival providers, more effective retention strategies can be developed. This is the key to the successful retention marketing.
Obviously, the most important customer retention strategy is to identify customers who are likely to potential defect to rival providers. Once they are identified, customer retention programs can be developed and preventive actions can be taken. The following data mining techniques (customer churn analysis) can be used to identify customer groups having high defection risk:
- Defector Profiling: Develop profiles of risky groups based on demographic, geographic, psychographic attributes and service usage patterns. Profiling defectors can reveal the reasons why your customers are not staying with you.
- Defection Scoring and Segmentation: Build predictive models that can predict likelihood of defection and perform segmentation based on defection scoring. Neural network provides a way to identify customer groups that have shown to have high churn ratios in high accuracy.
You have spent a long time in acquiring this customer. Make sure you don’t let them leave you. Retain & Motivate them to stay. Let us tell you how.
Customer surveys are very important means for identifying potential problems in your services. Most common reasons for customer defection include inadequate service quality and high cost. A process frequently employed is to ask customers to rate your services along with their demographic and psychographic profiles. Analyzing surveys can reveal customer groups who are not happy with your services and then action can be taken to prevent customer defections.
Customer churns occur from various customer segments for variety of reasons. It's not possible to detect churns by predictive modeling alone is not the only solution to detect customer churn. We may have to use a combination of various techniques to detect churn:
- Rule-based Churn Detection: Emerging churn patterns cannot be captured by predictive modeling. Rules may be used to describe high churn-risk customer segments.
- Segmentation and segment-specific Predictive Modeling: Distribution of churns and non-churns in customer segments makes it harder to use predictive modeling. Special predictive models can be developed for the segments.
- Combining current and past Predictive Models: Combining past and recent predictive models can be used as a useful mechanism to predict customer churn balancing the inherent problems with current and past data.
It’s time you gave your Customer’s what they need. Listen Carefully and find our how and what.
Once churn detection models are developed, they can be applied to customer database on a regular basis, say, weekly, monthly, etc. This will identify customers who have potential for defection but have not been contacted for retention purposes in recent times. Preventive actions can be followed for customers who have been identified as potential risky.
Few simple steps:
- Churn Investigating – Identify “triggers” causing customers to defect
- Multivariate Modeling – Use Modeling to weigh and rank each churn trigger
- Churn Monitoring – Implement strategy and measures to focus on major triggers
- Anti Churn Strategy – Quickly Implement anti churn strategy to retain customers
Successful companies have identified that it is far more profitable to first investigate why customers are leaving. Once you are clear which root cause need to be addressed targeting potential defectors with specific offers can be executed with precision. Last but not the least, lowering churn supports a primary objective to attain maximum profitability which is a result of complex interactions of metrics such as customer life time value, gross adds, cost of acquisition, cost of servicing the customer etc. Churn reduction initiatives impact other departments and you need to ensure that the organizational objectives are realigned to accommodate the impact.
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