Statistical modeling methods for determining customer distribution by churn probability within a customer population

• US 20070185867A1
• Filed: 02/03/2006
• Published: 08/09/2007
• Est. Priority Date: 02/03/2006
• Status: Active Application

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Canale, Paolo, Maga, Matteo, Bohe, Astrid
• Application Number:
  US11/347,136
• Publication Number:
  US 20070185867A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system and method for managing churn among the customers of a business is provided. The system and method provide for an analysis of the causes of customer churn and identifies customers who are most likely to churn in the future. Identifying likely churners allows appropriate steps to be taken to prevent customers who are likely to chum from actually churning. The system included a dedicated data mart, a population architecture, a data manipulation module, a data mining tool and an end user access module for accessing results and preparing preconfigured reports. The method includes adopting an appropriate definition of churn, analyzing historical customer to identify significant trends and variables, preparing data for data mining, training a prediction model, verifying the results, deploying the model, defining retention targets, and identifying the most responsive targets.

Claims

1. A data mining system comprising;

a data mart for receiving and storing customer data from a plurality of data sources;

a data manipulation module for calculating derived variable values from the data stored in the data mart and for preparing an input data set including the derived variable values; and

a data mining tool adapted to discover groups of customer having one or more like characteristics based on data in the prepared data set.

2. The data mining system of claim 1 wherein the data mart stores a plurality of raw customer data values for individual customers and wherein the data manipulation module calculates the derived variable values from the raw customer data values.

3. The data mining system of claim 2 wherein the data mart receives multiple raw customer data sets over time, and wherein the data manipulation module is adapted to calculate a trend line for individual customers based on multiple customer data values associated with a particular customer variable received over time, and to calculate the slope of the trend line.

4. The data mining system of claim 2 wherein the data mart receives multiple raw customer data value sets over time, and wherein the data manipulation module is adapted to calculate a customer average for individual customers based on a plurality of raw customer data values associated with a particular customer variable received over time.

5. The data mining system of claim 4 wherein the data manipulation module is further adapted to calculate a customer distribution based on the calculated customer averages for individual customers;

define customer classes based on the distribution;

classify individual customers according to the defined classes based on where the average values calculated for individual customers fall within the distribution; and

store individual customers'"'"' classifications as derived variables.

6. The data mining system of claim 2 wherein the data manipulation module is adapted to create an input data file to be analyzed by the data mining tool, the input data file comprising a plurality of customer records, each customer record associated with a particular customer and including a plurality of customer variable values including raw customer variable values and derived customer variable values.

7. The data mining system of claim 1 wherein the data mining tool comprises a KXEN data mining tool.

8. The data mining system of claim 1 wherein the data mining tool comprises an SAS Data Miner.

9. A method of identifying groups of customers from within a large customer population having one or more customer, the method comprising:

defining a plurality of customer attribute variables wherein a customer attribute variable value quantifies a characteristic of a customer;

receiving customer data;

determining customer attribute variable values for individual customers in the customer population for the plurality of customer attribute variables;

creating a data mining input data set including the determined customer attribute variable values;

providing a data mining tool adapted to discover customer groups based on common attribute variable values; and

analyzing the input data set using the data mining tool.

10. The method of claim 9 wherein defining a plurality of customer attribute variables includes defining derived attribute variables whose values are derived from customer data values.

11. The method of claim 10 wherein defining a derived attribute variable comprises defining a plurality of customer classes, each class corresponding to one of a customer attribute variable value or a range of customer attribute variable values such that individual customers may be classified according to a customer attribute variable value associated with the customer.

12. The method of claim 11 wherein determining customer attribute variable values comprises classifying a customer based on the customer attribute variable value associated with the customer and the corresponding defined class, and storing the customer classification as a derived variable value.

13. The method of claim 10 wherein defining a derived attribute value comprises defining an algorithm for calculating derived attribute values from customer data values.

14. The method of claim 13 wherein the algorithm for calculating derived attribute values from customer data values comprises calculating an average from a plurality of customer data variable values associated with a customer and received over time.

15. The method of claim 13 wherein the algorithm for calculating derived attribute values from customer data values comprises calculating a best fitting trend line from a plurality of customer data variable values associated with a customer, wherein the plurality of customer data variable values are related with the same customer data variable and received over time, and calculating the slope of the best fitting trend line.

16. The method of claim 9 wherein defining a plurality of customer attribute values includes defining a derived attribute variable wherein individual customer values of the derived attribute variable are derived from the customer data by calculating an average data value from a plurality of data values associated with a customer and which are received over time, calculating the distribution of multiple customers based on the individual customer average data values and defining a plurality of customer classes based on the calculated distribution, assigning a customer to a customer class based on the average data value associated with the customer, the assigned class comprising the value of the derived variable associated with the customer.

17. The method of claim 9 wherein providing a data mining tool comprises providing an SAS Data Miner data mining tool.

18. The method of claim 9 wherein providing a data mining tool comprises providing a KXEN data mining tool

19. A method of preparing customer data for data mining comprising:

defining a variable which provides a quantifiable measure of a customer characteristic;

obtaining a plurality of individual variable values, each value associated with an individual customer among a plurality of customers in a customer population;

generating a customer distribution based on the individual variable values for the plurality of customers in the customer population;

defining a plurality of customer classes based on the customer distribution;

assigning a customer classification to a customer based on the defined class to which the variable value associated with the customer belongs; and

storing the customer classification as a prepared variable value associated with the customer.

20. The method of preparing customer data for data mining of claim 19 wherein defining a variable which provides a quantifiable measure of a customer characteristic comprises identifying a customer data variable for which a customer data variable value is received for individual customers on a regular basis.

21. The method of preparing customer data for data mining of claim 20 wherein defining a variable which provides a quantifiable measure of a customer characteristic further comprises defining an algorithm for calculating an average of a plurality of customer data variable values associated with a customer and received over time.

22. The method of preparing customer data for data mining of claim 20 wherein defining a variable which provides a quantifiable measure of a customer characteristic further comprises defining an algorithm for calculating a best fit trend line from a plurality of customer data variable values associated with a customer and received over time, and calculating the slope of the trend line.

23. A method of improving the performance of a data mining tool, comprising:

receiving raw data from at least one data source;

calculating derived variable values from the raw data; and

including the derived variable values in a data set provided as input to the data mining tool.

24. The method of improving the performance of a data mining tool of claim 23 wherein receiving raw data comprises receiving a plurality of customer data variable values for a plurality of customer data variables, the customer data associated with individual customers received at regular intervals over time.

25. The method of improving the performance of a data mining tool of claim 24 wherein calculating derived variable values comprises calculating, for individual customers, an average of a plurality of customer data variable values received over time, each customer data variable value relating to the same customer data variable.

26. The method of improving the performance of a data mining tool of claim 24 wherein calculating derived variable values comprises calculating a best fit trend line for individual customers from a plurality of customer data variable values related to the same customer data variable and received over time, and calculating the slope of the trend line.

27. The method of improving the performance of a data mining tool of claim 24 wherein calculating derived variable values comprises identifying a customer data variable;

generating a customer distribution based on customer data variable values associated with individual customers; and

classifying individual customers based on their position within the customer distribution as defined by the customer data variable values associated with the individual customers, the customer classifications comprising derived variable values.

28. A method of maximizing a data mining tool'"'"'s discovery power comprising:

receiving raw customer data from a plurality of data sources;

defining a plurality of derived variables wherein derived variable values may be calculated from the raw customer data;

calculating derived variable values for individual customers; and

including the derived variable values in an input data set provided to the data mining tool for analysis.

29. The method of maximizing a data mining tool'"'"'s discovery power of claim 28 wherein calculating derived variable values comprises calculating a slope of a best fit trend line fitted to multiple observation values of a customer data variable included in the raw customer data.

30. The method of maximizing a data mining tool'"'"'s discovery power of claim 28 wherein calculating derived variable values comprises calculating an average of multiple observation values of a customer data variable included in the raw customer data.

31. The method of maximizing a data mining tool'"'"'s discovery power of claim 28 wherein calculating derived variable values comprises classifying individual customers according to a value of a customer data variable associated with the individual customers relative to values of the customer data variable associated with other customers;

the customer classification comprising the calculated derived variable value.

32. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises customer revenue.

33. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises average customer revenue.

34. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises monthly average traffic volumes.

35. The method of maximizing a data mining tool'"'"'s discovery power of claim 34 wherein the monthly average traffic volumes comprise at least one of monthly average international traffic volume, local traffic volume, long distance traffic volume, and to mobile traffic volume.

36. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises the monthly average number of event occurrences of a specified event type.

37. The method of maximizing a data mining tool'"'"'s discovery power of claim 36 wherein the specified event typed is selected from the comprising:

voice, SMS, MMS, content download, and chat.

38. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises monthly average discount amount.

39. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises monthly average due credit amount.

40. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises monthly average recharge amount.

41. The method of maximizing a data mining tool'"'"'s discovery power of claim 31 wherein the customer data variable comprises average customer revenue monthly average number of recharges.