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Methods and systems for analyzing historical trends in marketing campaigns

  • US 7,010,495 B1
  • Filed: 12/29/1999
  • Issued: 03/07/2006
  • Est. Priority Date: 12/29/1999
  • Status: Expired due to Fees
First Claim
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1. A method of evaluating marketing campaign data, the data being in the form of database scores, stored procedures, and On Line Analytical Processing (OLAP) multidimensional structures, said method comprising the steps of:

  • providing a plurality of analytic models including risk models and marketing models, each model is a statistical analysis for predicting a behavior of a prospective customer, wherein a risk model predicts a likelihood of whether the prospective customer will at least one of pay on time, be delinquent with a payment, and declare bankruptcy, and wherein the marketing models include a net present value/profitability model, a prospect pool model, a net conversion model, an attrition model, a response model, a revolver model, a balance transfer model, and a reactivation model;

    embedding the models within a targeting engine;

    determining a sequential order for combining the models using the targeting engine, the model combination includes a risk model and at least one of the marketing models;

    combining the models in the determined sequential order using the targeting engine to generate marketing campaign data including a target group by defining an initial customer group, the initial customer group includes a list of customers satisfying each of the combined models and rank ordered by projected profitability wherein projected profitability is based on at least one of a probable response by a customer to the marketing campaign, attrition of the customer, and risk associated with the customer, the list includes a high profit end, a moderate profit section, and a low profit end, the high profit end including customers having a highest projected profitability, the low profit end including customers having a lowest projected profitability, the moderate profit section including a profitability baseline, wherein the determined sequential order provides a greater number of customers included between the high profit end and the profitability baseline than any other sequential order of combining the models, the target group includes the customers included between the high profit end of the list and the profitability baseline;

    evaluating the model combination using structures that segment gains charts to discover where the model combination is under performing;

    evaluating a performance of the model combination over time; and

    defining user trends.

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