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Churn Model: How to Effectively Retain the Spending of An Attriting Customer
Generally, it's more difficult and expensive to acquire new customers and win back attriting customers than to retain existing customers. Thus, it’s important for companies to understand the reasons customers churn and estimate the risk associated with individual customers. But often marketers find it challenging to identify which customers will drop out.
Areas of improvement
- Identify improvement that can be made to increase revenue
- Notice a decline in sales from retained customers
Goal
To effectively and efficiently retain the spending of an attriting customer
FocusKPI provides the output of a predictive churn model as a measure of the immediate or future risk of customer cancellation. Our predictive behavior analytics use your data to identify the customers that are at a high risk of attrition so you can target these customers with retention measures before they drop out.
Data Sampling
Model Universe: around 695,000 online customer sites in P5 FY14 with tenure 12+ periods
Model Sample
Business Sites: 87,890, 13% of total universe
Revenue Attrition Customers: 15,996, 18.2% of Model Sample
Non-revenue attrition customers in next 3 periods: 71, 894, 81.8% of Model Sample
Variable Selection
Over 4,000 variables have been tested in the modeling development. 51 variables have been identified to be significant drivers
Key Drivers Identification
Score and identify 10 key drivers from a total of 51 drivers
Customers with Risk Ranking
Rank customers with risk (who are most likely to stop repurchasing papers)
In prioritizing spending on retention efforts, results can be greatly improved via an emphasis on an early intervention based on customer behavior.
- Retention efforts have primarily focused on post attrition effort at the customer level, most time it is too late
- Individuals with high sales are dropping spending sharply within compressed timeframes
- In focusing on higher revenue customers, we must balance its focus on the small business with efforts to retain the larger accounts
- Escalating retention efforts must not only consider marketing efforts but also processes for moving at-risk accounts into higher-touch programs such as Preferred Customer Program, Rewards, and even Contract programs
Recommendations
- Further exploration into the patterns of behavior of most valuable attriting customers
- Develop predictive models to identify valuable at-risk customers
- Develop triggers for use in Elity to make retention efforts timely and scalable
- Test and revise vehicles and offers to identify most effective retention mechanisms
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Generally, it's more difficult and expensive to acquire new customers and win back attriting customers than to retain existing customers. Thus, it’s important for companies to understand the reasons customers churn and estimate the risk associated with individual customers. But often marketers find it challenging to identify which customers will drop out.
Areas of improvement
- Identify improvement that can be made to increase revenue
- Notice a decline in sales from retained customers
Goal
To effectively and efficiently retain the spending of an attriting customer
FocusKPI provides the output of a predictive churn model as a measure of the immediate or future risk of customer cancellation. Our predictive behavior analytics use your data to identify the customers that are at a high risk of attrition so you can target these customers with retention measures before they drop out.
Data Sampling
Model Universe: around 695,000 online customer sites in P5 FY14 with tenure 12+ periodsModel Sample Business Sites: 87,890, 13% of total universe Revenue Attrition Customers: 15,996, 18.2% of Model Sample Non-revenue attrition customers in next 3 periods: 71, 894, 81.8% of Model Sample
Variable Selection
Over 4,000 variables have been tested in the modeling development. 51 variables have been identified to be significant drivers
Key Drivers Identification
Score and identify 10 key drivers from a total of 51 drivers
Customers with Risk Ranking
Rank customers with risk (who are most likely to stop repurchasing papers)
In prioritizing spending on retention efforts, results can be greatly improved via an emphasis on an early intervention based on customer behavior.
- Retention efforts have primarily focused on post attrition effort at the customer level, most time it is too late
- Individuals with high sales are dropping spending sharply within compressed timeframes
- In focusing on higher revenue customers, we must balance its focus on the small business with efforts to retain the larger accounts
- Escalating retention efforts must not only consider marketing efforts but also processes for moving at-risk accounts into higher-touch programs such as Preferred Customer Program, Rewards, and even Contract programs
Recommendations
- Further exploration into the patterns of behavior of most valuable attriting customers
- Develop predictive models to identify valuable at-risk customers
- Develop triggers for use in Elity to make retention efforts timely and scalable
- Test and revise vehicles and offers to identify most effective retention mechanisms