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Managing customer loss using customer groups

  • US 7,813,952 B2
  • Filed: 06/03/2003
  • Issued: 10/12/2010
  • Est. Priority Date: 06/04/2002
  • Status: Active Grant
First Claim
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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.

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