Managing customer loss using customer groups
First Claim
Patent Images
1. A computer-implemented method for managing customer loss, the method comprising:
- accessing, from electronic storage, customer information having multiple customer records, each customer record including multiple attribute values;
determining, using at least one processor, for each customer for which a record was accessed a churn likelihood representing the probability that the customer will be lost within a predetermined period of time;
computing, for each customer for which a record was accessed and using at least one processor, a composite-customer-value index for the customer as a sum of (1) a product-cost profitability measure for the customer determined by subtracting product costs from the customer'"'"'s net sales, (2) a sales-cost profitability measure for the customer determined by subtracting, from the product-cost profitability measure, additional direct and indirect sales costs associated with the customer, and (3) a result of dividing the sales-cost profitability measure for the customer by the product-cost profitability measure for the customer, the composite-customer-value index representing the value of the customer to a business enterprise;
for each customer for which a record was accessed, using at least one processor to normalize the composite-customer-value index for the customer and apply a first statistical coefficient to the composite-customer-value index for the customer to calculate a processed composite-customer-value index for the customer;
for each customer for which a record was accessed, using at least one processor to apply a second statistical coefficient to the churn likelihood for the customer to calculate a processed churn likelihood index for the customer, the second statistical coefficient being different than the first statistical coefficient;
for each customer for which a record was accessed, computing, using at least one processor, a value-churn measure for the customer the processed churn likelihood index for the customer and the processed composite-customer-value index for the customer;
accessing, for each of at least three likelihood-to-churn categories and using at least one processor, a predetermined threshold that identifies a range of value-churn measures to be used for determining customers to be associated with a particular likelihood-to-churn category from among the at least three likelihood-to-churn categories;
analyzing, using at least one processor, the determined value-churn measure for each customer with respect to the accessed predetermined thresholds;
based on the analysis, associating, using at least one processor, each customer with one of the at least three likelihood-to-churn categories, the association being based on the determined value-churn measure of a customer falling within the range of value-churn measures corresponding to the associated likelihood-to-churn category;
receiving, using at least one processor, user input selecting, from among the at least three likelihood-to-churn categories, a likelihood-to-churn category to be used to cluster customers that are associated with the selected likelihood-to-churn category;
based on receiving user input selecting a likelihood-to-churn category to be used to cluster customers, clustering, using at least one processor, customers associated with the selected likelihood-to-churn category into multiple groups of customers having a shared characteristic that is different than likelihood-to-churn and value to the business enterprise; and
defining, for each of the multiple groups of customers and using at least one processor, a promotional campaign that is targeted to customers having a value-churn measure falling within the range of value-churn measures corresponding to the selected likelihood-to-churn category and that is targeted to the shared characteristic of the customers clustered into the corresponding group.
3 Assignments
0 Petitions
Accused Products
Abstract
Techniques are provided for managing customer loss. Customers are first grouped using a predetermined category definition and then customers in one group are segmented based on common customer characteristics. The techniques may be used to categorize customers based on a likelihood of being lost and segmenting customers with a high likelihood of being lost into smaller, more homogenous groups of customers based on shared customer characteristics.
-
Citations
27 Claims
-
1. A computer-implemented method for managing customer loss, the method comprising:
- accessing, from electronic storage, customer information having multiple customer records, each customer record including multiple attribute values;
determining, using at least one processor, for each customer for which a record was accessed a churn likelihood representing the probability that the customer will be lost within a predetermined period of time; computing, for each customer for which a record was accessed and using at least one processor, a composite-customer-value index for the customer as a sum of (1) a product-cost profitability measure for the customer determined by subtracting product costs from the customer'"'"'s net sales, (2) a sales-cost profitability measure for the customer determined by subtracting, from the product-cost profitability measure, additional direct and indirect sales costs associated with the customer, and (3) a result of dividing the sales-cost profitability measure for the customer by the product-cost profitability measure for the customer, the composite-customer-value index representing the value of the customer to a business enterprise; for each customer for which a record was accessed, using at least one processor to normalize the composite-customer-value index for the customer and apply a first statistical coefficient to the composite-customer-value index for the customer to calculate a processed composite-customer-value index for the customer; for each customer for which a record was accessed, using at least one processor to apply a second statistical coefficient to the churn likelihood for the customer to calculate a processed churn likelihood index for the customer, the second statistical coefficient being different than the first statistical coefficient; for each customer for which a record was accessed, computing, using at least one processor, a value-churn measure for the customer the processed churn likelihood index for the customer and the processed composite-customer-value index for the customer; accessing, for each of at least three likelihood-to-churn categories and using at least one processor, a predetermined threshold that identifies a range of value-churn measures to be used for determining customers to be associated with a particular likelihood-to-churn category from among the at least three likelihood-to-churn categories; analyzing, using at least one processor, the determined value-churn measure for each customer with respect to the accessed predetermined thresholds;
based on the analysis, associating, using at least one processor, each customer with one of the at least three likelihood-to-churn categories, the association being based on the determined value-churn measure of a customer falling within the range of value-churn measures corresponding to the associated likelihood-to-churn category;
receiving, using at least one processor, user input selecting, from among the at least three likelihood-to-churn categories, a likelihood-to-churn category to be used to cluster customers that are associated with the selected likelihood-to-churn category;
based on receiving user input selecting a likelihood-to-churn category to be used to cluster customers, clustering, using at least one processor, customers associated with the selected likelihood-to-churn category into multiple groups of customers having a shared characteristic that is different than likelihood-to-churn and value to the business enterprise; anddefining, for each of the multiple groups of customers and using at least one processor, a promotional campaign that is targeted to customers having a value-churn measure falling within the range of value-churn measures corresponding to the selected likelihood-to-churn category and that is targeted to the shared characteristic of the customers clustered into the corresponding group. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 23, 24, 25, 26, 27)
- accessing, from electronic storage, customer information having multiple customer records, each customer record including multiple attribute values;
-
14. A computer-readable medium having embodied thereon a non-transitory computer program configured to manage customer loss, the medium comprising one or more non-transitory code segments, that when executed, causes one or more processors to:
-
access customer information having multiple customer records, each customer record including multiple attribute values; determine for each customer for which a record was accessed a churn likelihood representing the probability that the customer will be lost within a predetermined period of time; compute, for each customer for which a record was accessed, a composite-customer-value index for the customer as a sum of (1) a product-cost profitability measure for the customer determined by subtracting product costs from the customer'"'"'s net sales, (2) a sales-cost profitability measure for the customer determined by subtracting, from the product-cost profitability measure, additional direct and indirect sales costs associated with the customer, and (3) a result of dividing the sales-cost profitability measure for the customer by the product-cost profitability measure for the customer, the composite-customer-value index representing the value of the customer to a business enterprise; for each customer for which a record was accessed, normalize the composite-customer-value index for the customer and apply a first statistical coefficient to the composite-customer-value index for the customer to calculate a processed composite-customer-value index for the customer; for each customer for which a record was accessed, apply a second statistical coefficient to the churn likelihood for the customer to calculate a processed churn likelihood index for the customer, the second statistical coefficient being different than the first statistical coefficient; for each customer for which a record was accessed, compute a value-churn measure for the customer using the processed churn likelihood index for the customer and the processed composite-customer-value index for the customer;
access, for each of at least three likelihood-to-churn categories, a predetermined threshold that identifies a range of value-churn measures to be used for determining customers to be associated with a particular likelihood-to-churn category from among the at least three likelihood-to-churn categories;analyze the determined value-churn measure for each customer with respect to the accessed predetermined thresholds; based on the analysis, associate each customer with one of the at least three likelihood-to-churn categories, the association being based on the determined value-churn measure of a customer falling within the range of value-churn measures corresponding to the associated likelihood-to-churn category; receive user input selecting, from among the at least three likelihood-to-churn categories, a likelihood-to-churn category to be used to cluster customers that are associated with the selected likelihood-to-churn category; based on receiving user input selecting a likelihood-to-churn category to be used to cluster customers, cluster customers associated with the selected likelihood-to-churn category into multiple groups of customers having a shared characteristic that is different than likelihood-to-churn and value to the business enterprise; and define, for each of the multiple groups of customers, a promotional campaign that is targeted to customers having a value-churn measure falling within the range of value-churn measures corresponding to the selected likelihood-to-churn category and that is targeted to the shared characteristic of the customers clustered into the corresponding group. - View Dependent Claims (15, 16, 17, 18, 19, 20)
-
-
21. A system for managing customer loss, the system comprising:
-
a processor connected to a storage device and one or more input/output devices, wherein the processor is configured to;
access customer information having multiple customer records, each customer record including multiple attribute values;
determine for each customer for which a record was accessed a churn likelihood representing the probability that the customer will be lost within a predetermined period of time;compute, for each customer for which a record was accessed, a composite-customer-value index for the customer as a sum of (1) a product-cost profitability measure for the customer determined by subtracting product costs from the customer'"'"'s net sales, (2) a sales-cost profitability measure for the customer determined by subtracting, from the product-cost profitability measure, additional direct and indirect sales costs associated with the customer, and (3) a result of dividing the sales-cost profitability measure for the customer by the product-cost profitability measure for the customer, the composite-customer-value index representing the value of the customer to a business enterprise; for each customer for which a record was accessed, normalize the composite-customer-value index for the customer and apply a first statistical coefficient to the composite-customer-value index for the customer to calculate a processed composite-customer-value index for the customer; for each customer for which a record was accessed, apply a second statistical coefficient to the churn likelihood for the customer to calculate a processed churn likelihood index for the customer, the second statistical coefficient being different than the first statistical coefficient; for each customer for which a record was accessed, compute a value-churn measure for the customer using the processed churn likelihood index for the customer and the processed composite-customer-value index for the customer; access, for each of at least three likelihood-to-churn categories, a predetermined threshold that identifies a range of value-churn measures to be used for determining customers to be associated with a particular likelihood-to-churn category from among the at least three likelihood-to-churn categories; analyze the determined value-churn measure for each customer with respect to the accessed predetermined thresholds; based on the analysis, associate each customer with one of the at least three likelihood-to-churn categories, the association being based on the determined value-churn measure of a customer falling within the range of value-churn measures corresponding to the associated likelihood-to-churn category; receive user input selecting, from among the at least three likelihood-to-churn categories, a likelihood-to-churn category to be used to cluster customers that are associated with the selected likelihood-to-churn category; based on receiving user input selecting a likelihood-to-churn category to be used to cluster customers, cluster customers associated with the selected likelihood-to-churn category into multiple groups of customers having a shared characteristic that is different than likelihood-to-churn and value to the business enterprise; and define, for each of the multiple groups of customers, a promotional campaign that is targeted to customers having a value-churn measure falling within the range of value-churn measures corresponding to the selected likelihood-to-churn category and that is targeted to the shared characteristic of the customers clustered into the corresponding group. - View Dependent Claims (22)
-
Specification