System and Method for Predicting Likelihood of Customer Attrition and Retention Measures

• US 20090276289A1
• Filed: 04/25/2009
• Published: 11/05/2009
• Est. Priority Date: 12/20/2000
• Status: Active Grant

• Current Assignee: Acoustic, L.P.
• Original Assignee: International Business Machines Corporation
• Inventors: Dickinson, Jonathan, Desai, Paritosh
• Granted Patent: US 9,165,270 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

The present invention relates to a system and method for customer retention. Historical transaction and customer data may be received from stores. Likewise, recent customer transaction data may be received from the stores. The transactions are linked to each customer. Attriters, historical customers who discontinued shopping, are identified. Next, risk factors for attrition may be identified by examining the attriters'"'"' transaction history for commonalities. From the risk factors a loss model may be generated. The loss model may be used, in conjunction with current transaction data, to generate the likelihood of loss for each of the current customers, which may then be reported. Retention measures may be generated for each customer by comparing the customer'"'"'s transactions to the loss model and the risk factors. The retention measures may be outputted to the stores, and a price optimization system. Likewise, the retention measures may be validated by comparing actual customer loss to the loss model.

Claims

1. A method for predicting likelihood of customer attrition, useful in association with at least one customer, the method comprising:

receiving historical data from at least one store, wherein the historical data includes historical transactions, and wherein the historical transactions are associated with historical customers;

receiving customer data from the at least one store, wherein the customer data includes transactions;

linking each transaction of the customer data with one of the at least one customer;

identifying attriters, wherein the attriters are the historical customers who engage in an attrition behavior during the historical data;

identifying risk factors for attrition from the historical data, wherein the risk factors are defined by the historical transactions, and wherein one of the risk factors is frequency;

generating a loss model utilizing the identified risk factors;

generating likelihood of loss for each of the at least one customer by comparing the linked transactions to the loss model; and

reporting the likelihood of loss for each customer.

2. The method recited by claim 1, wherein the attrition behavior is defined by configurable criteria.

3. The method recited by claim 2, wherein the attrition behavior is defined as at least one of customers who discontinue shopping, customers who stop shopping in a given time frame, and decline in average purchase size from a historical basis.

4. The method recited by claim 1, further comprising generating at least one retention measure for the at least one customer, wherein the retention measure is generated by comparing the linked transactions to the loss model and the risk factors.

5. The method recited by claim 4, wherein the at least one retention measure is validated, wherein validation includes applying the at least one retention measure, calculating actual customer loss and comparing the actual customer loss to the loss model.

6. The method recited by claim 1, further comprising calibrating the loss model by discounting the frequency risk factor of the attriters after date of attrition.

7. The method recited by claim 1, further comprising identifying high value customers from the at least one customer, wherein the high value customers provide the at least one store economic value.

8. The method recited by claim 1, wherein the risk factors include at least one of behavioral categories, tenure, basket value, number of adults, available customer information, number of stored shopped at, store quality, competitor type, and gaps in shopping frequency.

9. The method recited by claim 1, wherein the linking each transaction utilizes at least one of payment identifiers, loyalty program identifiers, registry identifiers, or biometric identifiers.

10. The method recited by claim 9, wherein conflicts by the at least one identifiers is resolved in favor of the most accurate identifier.

11. The method recited by claim 1, further comprising cleansing the received customer data from at least one store.

12. The method recited by claim 4, further comprising outputting the at least one retention measure to the at least one store.

13. The method recited by claim 4, further comprising outputting the at least one retention measure to a price optimization system, whereby the price optimization system uses the retention measure to generate a preferred set of prices for one or more products, and wherein the preferred set of prices is configured to limit customer loss by a set threshold.

14. The method recited by claim 6, further comprising generating a loss value for each customer by comparing the likelihood of loss for said customer with the economic value said customer provides the at least one store.

15. A likelihood of customer attrition predictor, useful in association with at least one customer, the likelihood of customer attrition predictor comprising:

a data analyzer configured to receive historical data from at least one store, wherein the historical data includes historical transactions, and wherein the historical transactions are associated with historical customers, and wherein the data analyzer further is configured to receive customer data from the at least one store, wherein the customer data includes transactions, and wherein the data analyzer further is configured to identify attriters, wherein the attriters are the historical customers who engage in an attrition behavior during the historical data;

an identity determiner configured to link each transaction of the customer data with one of the at least one customer;

a loss model engine configured to;

identify risk factors for attrition from the historical data, wherein the risk factors are defined by the historical transactions, and wherein one of the risk factors is frequency;

generate a loss model utilizing the identified risk factors;

a likelihood of loss determiner configured to generate likelihood of loss for each of the at least one customer by comparing the linked transactions to the loss model; and

a reporter configured to report the likelihood of loss for each customer.

16. The apparatus of claim 15, wherein the data analyzer is enabled to define the attrition behavior by configurable criteria.

17. The apparatus of claim 16, wherein the attrition behavior is defined as at least one of customers who discontinue shopping, customers who stop shopping in a given time frame, and decline in average purchase size from a historical basis.

18. The apparatus of claim 15, further comprising a stratagem generator configured to generate at least one retention measure for the at least one customer, wherein the retention measure is generated by comparing the linked transactions to the loss model and the risk factors.

19. The apparatus of claim 18, wherein the stratagem generator is further configured to validate the at least one retention measure, wherein validation includes applying the at least one retention measure, calculating actual customer loss and comparing the actual customer loss to the loss model.

20. The apparatus of claim 18, wherein the model engine is further configured to calibrate the loss model by discounting the frequency risk factor of the attriters after date of attrition.

21. The apparatus of claim 15, further comprising a valuable customer identifier configured to identify high value customers from the at least one customer, wherein the high value customers provide the at least one store economic value.

22. The apparatus of claim 15, wherein the risk factors include at least one of behavioral categories, tenure, basket value, number of adults, available customer information, number of stored shopped at, store quality, competitor type, and gaps in shopping frequency.

23. The apparatus of claim 15, wherein the linking each transaction utilizes at least one of payment identifiers, loyalty program identifiers, registry identifiers, or biometric identifiers.

24. The apparatus of claim 21, wherein conflicts by the at least one identifiers is resolved in favor of the most accurate identifier.

25. The apparatus of claim 15, further comprising a data cleanser configured to cleanse the received customer data from at least one store.

26. The apparatus of claim 18, further comprising an outputter configured to output the at least one retention measure to the at least one store.

27. The apparatus of claim 17, further comprising an outputter configured to output the at least one retention measure to a price optimization system, whereby the price optimization system uses the retention measure to generate a preferred set of prices for one or more products, and wherein the preferred set of prices is configured to limit customer loss by a set threshold.

28. The apparatus of claim 18, further comprising a loss value generator configured to generate a loss value for each customer by comparing the likelihood of loss for said customer with the economic value said customer provides the at least one store.