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

• Current Assignee: General Electric Capital Corporation (General Electric Company)
• Original Assignee: General Electric Capital Corporation (General Electric Company)
• Inventors: Nabe, Oumar, Samra, Balwinder S.
• Primary Examiner(s): Hafiz, Tariq R.
• Assistant Examiner(s): BOSWELL, BETH V
• Application Number:
  US09/474,631
• Time in Patent Office: 2,260 Days
• Field of Search: 705/10, 705/8, 705/9, 705/14, 705/7, 707/5, 707/8, 707/10, 707/101, 395/50
• US Class Current: 705/14.41
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements

Abstract

Method and systems using models for evaluating marketing campaign data in the form of database scores, stored procedures, and OLAP multidimensional structures. Models are used to target segments for marketing. The models are mathematical algorithms that map customer and/or account attributes such as, a customer'"'"'s propensity to attrite, default on payments, and expected profitability. The method includes the steps of evaluating models using OLAP structures based on campaign drivers, that can segment gains charts to discover where a model is under performing and evaluating models performance over time to discover user defined trends.

Claims

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.

2. A method according to claim 1 wherein said step of defining user trends further comprises the step of determining where profitability has been changing over time.

3. A method according to claim 1 wherein said step of defining user trends further comprises the step of determining where a response rate has been changing over time.

4. A method according to claim 1 wherein said step of defining user trends further comprises the step of determining where a number of accounts are being closed.

5. A method according to claim 1 wherein said step of evaluating the model combination is accomplished by creating history structures based on user defined attributes.

6. A method according to claim 1 wherein said step of defining user trends further comprises the step of analyzing a particular population segment.

7. A method according to claim 1 wherein said step of evaluating a performance of the model combination over time further comprises the step of maintaining feedback into a targeting engine to improve subsequent modeling cycles.

8. A method according to claim 1 wherein said step of defining user trends further comprises the step of using gains charts to illustrate model performance in segments.

9. A method according to claim 1 wherein said step of combining the models in the determined sequential order further comprises the step of:

storing in a database historical data for a plurality of potential customers including for each potential customer at least one of an age, a gender, a marital status, an income, a transaction history, and a transaction measure;

determining a sequential order for combining the models by applying each model to be combined to each of the plurality of potential customers included in the database; and

combining the models in the determined sequential order to define the initial customer group by applying a first model included in the determined sequential order to each of the plurality of potential customers included in the database to generate a first segment of only those potential customers satisfying the first model, applying a second model included in the determined sequential order to the first segment to generate a second segment of only those potential customers satisfying the combination of the first and second models, and then applying each subsequent model included in the determined sequential order to a segment generated by the combination of each prior model.

10. A system for evaluating marketing campaign data, said system comprising:

a customer database further comprising historical campaign results;

a graphical user interface for presentation of trend analysis data; and

a computer comprising a targeting engine, the computer is coupled to the database and the graphical user interface, the targeting engine embedded with 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, the targeting engine is configured to;

determine a sequential order for combining the models, the model combination includes a risk model and at least one marketing model;

combine the models in the determined sequential order 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 said 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;

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

evaluate a performance of the model combination over time; and

define trends relating to the marketing campaign data.

11. A system according to claim 10 wherein said targeting engine is further configured to evaluate a combination of models, wherein the combined models include time based multidimensional On Line Analytical Processing (OLAP) history structures.

12. A system according to claim 10 wherein said targeting engine is further configured to discover user defined trends.

13. A system according to claim 10 wherein said targeting engine is further configured to determine where profitability has been changing over time.

14. A system according to claim 10 wherein said targeting engine is further configured to determine where a response rate has been changing over time.

15. A system according to claim 10 wherein said targeting engine is further configured to determine where a number of accounts are being closed.

16. A system according to claim 10 wherein said targeting engine is further configured to determine propensity of a customer to avail themselves to other products over time.

17. A system according to claim 10 wherein said targeting engine is further configured to check a performance of the model combination based on user defined criteria.

18. A system according to claim 10 wherein said targeting engine is further configured to analyze a particular population segment.

19. A system according to claim 10 wherein said targeting engine is further configured to maintain feedback to improve subsequent modeling cycles.

20. A system according to claim 10 wherein said targeting engine is further configured to use gains charts to illustrate customer trends.

21. A system according to claim 10 wherein said database further comprises historical data for a plurality of potential customers including for each potential customer at least one of an age, a gender, a marital status, an income, a transaction history, and a transaction measure, and said targeting engine is further configured to:

determine a sequential order for combining the models by applying each model to be combined to each of the plurality of potential customers included in said database; and

combine the models in the determined sequential order to define the initial customer group by applying a first model included in the determined sequential order to each of the plurality of potential customers included in the database to generate a first segment of only those potential customers satisfying the first model, applying a second model included in the determined sequential order to the first segment to generate a second segment of only those potential customers satisfying the combination of the first and second models, and then applying each subsequent model included in the determined sequential order to a segment generated by the combination of each prior model.

22. A method of evaluating marketing campaign data, the data being in the form of customer lists, database scores, stored procedures, and On Line Analytical Processing (OLAP) multidimensional structures, said method comprising the steps of:

storing in a database historical data for a plurality of potential customers including for each potential customer at least one of an age, a gender, a marital status, an income, a transaction history, and a transaction measure;

providing a plurality of analytic models including marketing and risk 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 by applying each model to be combined to each of the plurality of potential customers included in the database, 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;

generating gains charts by comparing customers included in the target group to corresponding marketing campaign results;

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

evaluating over time and over a plurality of marketing campaigns at least one of a performance of the model combination; and

identifying user defined trends including identifying trends within segments by analyzing structures of a plurality of marketing campaigns in chronological order.