Methods and Systems for Identifying Customer Status for Developing Customer Retention and Loyality Strategies

1 . A method of identifying customer status for developing customer retention and loyalty strategies using a computing device, the method comprising:

calculating ( 202 ) likelihood of next action taken by customers based on customer attributes and associated attribute weights stored in a customer data base;

calculating ( 203 ) customer churn-risk scores based on customer attributes that vary over time using the computing device;

determining ( 207 ) what-if-scenarios for each customer based on churn-risk scores in order to identify the next-best-action to reduce probability of customer churn;

determining ( 208 ) when-to-act time thresholds for each customer based on churn-risk scores in order to identify when a non-high risk customer of churning will likely become a high-risk customer of churning at some later time; and

selecting ( 209 ) customer retention and loyalty strategies for customers based on the churn-risk scores, what-if-scenarios, and when-to-act time thresholds.

2 . The method of claim 1 further comprising preparing ( 201 ) customer data representing a number of actions taken by individual customers and customer attributes.

3 . The method of claim 2 , wherein preparing the customer data further comprises splitting the customer data into a training data set and test data set.

4 . The method of claim 1 further comprising:

comparing ( 204 ) likelihood of next action and churn-risk scores to likelihood of next action and churn-risk scores of a test data set of the customer data base; and

adjusting ( 206 ) parameters and repeating the steps of predicting likelihood of next action and computing customer churn-risk scores, when the method of claim 1 produces unacceptable results.

5 . The method of claim 1 , wherein calculating ( 203 ) customer churn-risk scores further comprises calculating for each customer a churn-risk score based on the customer's last action date, last action number for assigning the customer to a particular stratum, s, weights of the attributes from the stratum s, and values of the attributes on the last action date.

6 . The method of claim 1 , wherein the customer churn-risk score further comprises the probability of no action taken by the customer for a period of time.

7 . The method of claim 1 , wherein the determining ( 207 ) what-if-scenarios for each customer further comprises creating a data set within the customer data where for each customer a hypothetical action of a certain type performed on a certain date is added to the customer data base and attribute values that depend on the hypothetical action are updated to reflect the change.

8 . The method of claim 1 , wherein determining ( 207 ) what-if-scenarios for each customer further comprises:

for each customer, computing a hypothetical churn-risk score as if the customer had performed an action of a certain type on a particular day; and

creating what-if-scenarios performed at different times in the future for combinations of actions based on the likelihood of the customer taking a certain type of action.

9 . The method of claim 1 , wherein determining ( 208 ) the when-to-act time thresholds further comprise computing when, from a date of analysis, a non-high risk customer of churning will likely become a high-risk customer of churning at some later time.

10 . The method of claim 1 , wherein determining ( 208 ) the when-to-act time thresholds further comprises determining the time from the date of analysis when the customer's churn-risk score is greater than a churn-risk threshold.

11 . An article comprising at least one computer readable medium having instructions executable by a computing device to perform a method of identifying customer status for developing customer retention and loyalty strategies, the method comprising:

calculating ( 202 ) likelihood of next action taken by customers based on customer attributes and associated attribute weights stored in a customer data base;

calculating ( 203 ) customer churn-risk scores based on customer attributes vary over time using the computing device;

determining ( 207 ) what-if-scenarios for each customer based on churn-risk scores in order to identify the next-best-action to reduce probability of customer churn;

determining ( 208 ) when-to-act time thresholds for each customer based on churn-risk scores in order to identify when a non-high risk customer of churning will likely become a high-risk customer of churning at some later time; and

selecting ( 209 ) customer retention and loyalty strategies for customers, based on the churn-risk scores, what-if-scenarios and when-to-act time thresholds.

12 . The article of claim 1 further comprising preparing ( 201 ) customer data representing a number of actions taken by individual customers and customer attributes.

13 . The article of claim 1 , wherein calculating ( 203 ) customer churn-risk scores further comprises calculating for each customer a churn-risk score based on the customer's last action date, last action number for assigning the customer to a particular stratum, s, weights of the attributes from the stratum s, and values of the attributes on the last action date.

14 . The article of claim 1 , wherein determining ( 207 ) what-if-scenarios for each customer further comprises:

for each customer, computing a hypothetical churn-risk score as if the customer had performed an action of a certain type on a particular day; and

creating what-if-scenarios performed at different times in the future for combinations of actions based on the likelihood of the customer taking a certain type of action.

15 . The article of claim 1 , wherein determining ( 208 ) the when-to-act time thresholds further comprises determining the time from the date of analysis when the customer's churn-risk score is greater than a churn-risk threshold.