Methods and systems for creating models for marketing campaigns
First Claim
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1. A method for increasing efficiency of a marketing system, the system comprising a database containing a plurality of prospective customers and customer demographic data, said method including the steps of:
- building models of predicted customer profiles, the models include risk models and marketing models, each model is a statistical analysis for predicting a behavior of a prospective customer to a marketing campaign, 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 an online analytical processing tool;
using the online analytical processing tool and the customer demographic data to analyze a combination of the models, each model combination includes a risk model and at least one of the marketing models;
determining a sequential order for combining the models prior to combining the models based on the model combination analysis performed by the online analytical processing tool;
using the online analytical processing tool to combine the models in the determined sequential order, wherein combining the models in the determined sequential order includes defining a target group of prospective customers from the plurality of prospective customers stored in the database, the target group including a list of prospective customers satisfying each of the combined models, the determined sequential order maximizes a number of prospective customers included within the target group; and
generating scores for each prospective customer included within the target group based on the predicted customer profiles wherein the online analytical processing tool generates the scores by combining the models in the determined sequential order, the scores representing at least one of a probable response by a customer to the marketing campaign, attrition of the customer, and risk associated with the customer.
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Abstract
Methods and systems for increasing efficiency of a marketing campaign are disclosed. The method uses a system including a database containing customer demographic data and includes the steps of building models of predicted customer profiles and generating scores for prospective customers in the database based on predicted customer profiles.
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Citations
21 Claims
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1. A method for increasing efficiency of a marketing system, the system comprising a database containing a plurality of prospective customers and customer demographic data, said method including the steps of:
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building models of predicted customer profiles, the models include risk models and marketing models, each model is a statistical analysis for predicting a behavior of a prospective customer to a marketing campaign, 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 an online analytical processing tool; using the online analytical processing tool and the customer demographic data to analyze a combination of the models, each model combination includes a risk model and at least one of the marketing models; determining a sequential order for combining the models prior to combining the models based on the model combination analysis performed by the online analytical processing tool; using the online analytical processing tool to combine the models in the determined sequential order, wherein combining the models in the determined sequential order includes defining a target group of prospective customers from the plurality of prospective customers stored in the database, the target group including a list of prospective customers satisfying each of the combined models, the determined sequential order maximizes a number of prospective customers included within the target group; and generating scores for each prospective customer included within the target group based on the predicted customer profiles wherein the online analytical processing tool generates the scores by combining the models in the determined sequential order, the scores representing at least one of a probable response by a customer to the marketing campaign, attrition of the customer, and risk associated with the customer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system configured for targeting market segments comprising:
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a customer database for storing a plurality of prospective customers; a graphical user interface for entering marketing campaign data; and models of predicted customer profiles based upon historic data that are embedded on an online analytical processing tool, the models include risk models and marketing models, each model is a statistical analysis for predicting a behavior of a prospective customer to a marketing campaign, 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, said online analytical processing tool configured to; analyze a combination of said models, each model combination includes a risk model and at least one of the marketing models, determine a sequential order for combining said models prior to combining said models based on the model combination analysis, combine said models in the determined sequential order, wherein combining said models in the determined sequential order includes defining a target group of prospective customers from the plurality of prospective customers stored in said database, the target group including a list of prospective customers satisfying each of the combined models, the determined sequential order maximizes a number of prospective customers included within the target group, and generate scores for each prospective customer included within the target group based on said predicted customer profiles by combining said models in the determined sequential order, the scores representing at least one of a probable response by a customer to the marketing campaign, attrition of the customer, and risk associated with the customer. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 21)
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20. A method for increasing efficiency of a marketing system, the system comprising a database containing a plurality of prospective customers and customer demographic data, said method including the steps of:
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building models of predicted customer profiles, the models include risk models and marketing models, each model is a statistical analysis for predicting a behavior of a prospective customer to a marketing campaign, 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 an online analytical processing tool; utilizing the online analytical processing tool and the customer demographic data to analyze each combination of the models, each model combination includes a risk model and at least one of the marketing models; determining a sequential order for combining the models prior to combining the models based on the model combination analysis performed by the online analytical processing tool; using the online analytical processing tool to combine the models in the determined sequential order, wherein combining the models in the determined sequential order includes defining a target group of prospective customers from the plurality of prospective customers stored in the database, the target group including a list of prospective customers satisfying each of the combined models, the determined sequential order maximizes a number of prospective customers included within the target group; and generating scores for each prospective customer included within the target group based on the predicted customer profiles wherein the online analytical processing tool generates the scores by combining the models in the determined sequential order, the scores representing at least one of a probable response by a customer to the marketing campaign, attrition of the customer, and risk associated with the customer.
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Specification
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Current AssigneeGeneral Electric Capital Corporation (General Electric Company)
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Original AssigneeGeneral Electric Capital Corporation (General Electric Company)
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InventorsNabe, Oumar, Samra, Balwinder S.
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Primary Examiner(s)Reagan, James A
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Application NumberUS09/474,588Time in Patent Office2,253 DaysField of Search705/10, 705/1, 705/2, 705/3, 705/7, 705/14, 705/26, 705/27, 705/77, 707/1, 707/2, 707/9, 707/10, 707100-102, 707 5- 7, 235/375, 235/383, 235/380, 235/381, 463/25, 370/352US Class Current705/7.28CPC Class CodesG06Q 10/0635 Risk analysis of enterprise...G06Q 20/085 involving remote charge det...G06Q 30/02 Marketing; Price estimation...G06Q 30/0201 Market modelling; Market an...G06Q 30/0245 SurveysG06Q 30/0269 based on user profile or at...G06Q 30/0276 Advertisement creationG06Q 30/0277 Online advertisementY10S 707/99931 Database or file accessingY10S 707/99932 Access augmentation or opti...Y10S 707/99935 Query augmenting and refini...Y10S 707/99936 Pattern matching accessY10S 707/99937 SortingY10S 707/99939 Privileged accessY10S 707/99942 Manipulating data structure...Y10S 707/99943 Generating database or data...
