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Predicting likelihood of customer attrition and retention measures

  • US 9,165,270 B2
  • Filed: 04/25/2009
  • Issued: 10/20/2015
  • Est. Priority Date: 12/20/2000
  • Status: Active Grant
First Claim
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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 for a plurality of customers from the at least one store, wherein the customer data includes transactions;

    identifying at least one customer that provides economic value to at least one store from the plurality of customers;

    linking each corresponding transaction of the customer data with one of the at least one customer and producing a reduced set of data limited to the at least one customer for faster processing;

    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;

    generating a loss model, using a computer, utilizing the identified risk factors, wherein generating the loss model comprises;

    tuning the loss model in response to a difference between previous actual data and data generated by the loss model exceeding an expected discrepancy, wherein tuning the loss model includes correcting the loss model for biases produced by lost customers and to filter customers that are beyond being retained by retention measures;

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

    generating and validating at least one retention measure for the at least one customer using the computer, wherein validating includes;

    calculating actual customer loss from applying the at least one retention measure at the at least one store;

    modeling an expected customer loss via the loss model; and

    determining a difference between the actual customer loss and the expected customer loss generated by the loss model over a time period to indicate an effectiveness of the at least one retention measure over that time period; and

    controlling processing of the computer to process the reduced set of data limited to the at least one customer to increase performance speed.

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