Methods and systems for analyzing marketing campaigns

• US 6,970,830 B1
• Filed: 12/29/1999
• Issued: 11/29/2005
• 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): BOYCE, ANDRE D
• Application Number:
  US09/474,539
• Time in Patent Office: 2,162 Days
• Field of Search: 705/10
• US Class Current: 705/7.28
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

Methods and systems for analyzing the success of a marketing campaign by using campaign results and an original campaign database are disclosed. The method includes the steps of profiling results of the marketing campaign against a list of user defined dimensions (analytic models may be used to derive dimensions) and assigning a score to the results of the marketing campaign.

Claims

1. A method of analyzing the success of a marketing campaign by using a targeting engine, campaign results and an original campaign database, said method comprising the steps of:

embedding within the targeting engine a plurality of analytic models including marketing and risk models, the marketing models including 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;

using the targeting engine to determine a sequential order for combining the models;

combining the models embedded within the targeting engine in the determined sequential order to define an initial customer group including a list of customers satisfying each of the combined models and rank ordered by projected profitability wherein projected profitability is based on 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 maximizes a number of customers included between the high profit end and the profitability baseline, a target group includes the customers included between the high profit end of the list and the profitability baseline, the profitability baseline defines marginal returns for a customer equal to zero;

deriving a list of user defined dimensions for the customers included in the target group, the user defined dimensions include marketing defined dimensions and risk defined dimensions;

profiling results of the marketing campaign against the marketing defined dimensions and the risk defined dimensions; and

assigning a score to the results of the marketing campaign based on the marketing defined dimensions and the risk defined dimensions.

2. A method according to claim 1 wherein said step of profiling results of the marketing campaign further comprises the step of comparing accounts targeted by the marketing campaign against those accounts not targeted by the marketing campaign.

3. A method according to claim 2 wherein said step of comparing accounts targeted by the marketing campaign against those accounts not targeted by the marketing campaign further comprises the step of selecting the differences between targeted and non-targeted accounts.

4. A method according to claim 1 wherein said step of profiling results of the marketing campaign further comprises the step of ensuring that the marketing campaign is reaching a targeted population base.

5. A method according to claim 1 wherein said step of profiling results of the marketing campaign further comprises the step of capturing graphically, clusters of data built using statistical procedures.

6. A method according to claim 1 wherein said step of profiling results of the marketing campaign further comprises the step of using the user defined dimensions and the campaign result to construct a gains chart.

7. A method according to claim 1 wherein said step of assigning a score to the results of the marketing campaign further comprises the step of rank ordering user defined segments.

8. A method according to claim 7 wherein said step of rank ordering user defined segments further comprises the step of showing where the model works best.

9. A method according to claim 7 wherein said step of rank ordering user defined segments further comprises the step of showing where the model performance needs to be addressed.

10. A method according to claim 1 wherein said step of profiling results of the marketing campaign against the list of user defined dimensions further comprises:

comparing the results of the marketing campaign against the marketing defined dimensions and the risk defined dimensions; and

using the targeting engine to generate gains charts based on the comparison of the marketing campaign results against the marketing defined dimensions and the risk defined dimensions.

11. A method according to claim 1 wherein said step of embedding within the targeting engine a plurality of analytic models including marketing and risk models further comprises embedding within the targeting engine a plurality of analytic models including marketing and risk models, the risk models include a payment behavior prediction model, a delinquency model, a bad debt model, a fraud detection model, a bankruptcy model, and a hit and run model.

12. A system configured to analyze success of a marketing campaign, said system comprising:

a customer database comprising campaign results and an original campaign database;

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

a plurality of analytic models including marketing and risk models embedded within a targeting engine, the marketing models comprising 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 system configured to;

determine a sequential order for combining the models,

combine the models in the determined sequential order to define 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 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 maximizes a number of customers included between the high profit end and the profitability baseline, a target group includes the customers included between the high profit end of the list and the profitability baseline, the profitability baseline defines marginal returns for a customer equal to zero,

derive a list of user defined dimensions including marketing defined dimensions and risk defined dimensions for generating a marketing campaign for the customers included in the target group,

profile results of the marketing campaign against said marketing defined dimensions and said risk defined dimensions, and

assign a score to the results of the marketing campaign based on said marketing defined dimensions and said risk defined dimensions.

13. A system according to claim 12 further configured to compare accounts targeted by the marketing campaign against those accounts not targeted by the marketing campaign.

14. A system according to claim 13 further configured to select differences between targeted and non-targeted accounts.

15. A system according to claim 12 further configured to ensure that the marketing campaign is reaching a targeted population base.

16. A system according to claim 12 further configured to capture graphically, clusters of data built using statistical procedures.

17. A system according to claim 12 further configured to construct gains charts from user defined models and campaign results.

18. A system according to claim 12 further configured to rank order user defined segments.

19. A system according to claim 18 further configured to show where the model works best.

20. A system according to claim 18 further configured to show where the model performance needs to be addressed.

21. A system according to claim 12 wherein said system is further configured to:

compare the results of the marketing campaign against said marketing defined dimensions and said risk defined dimensions; and

utilize said targeting engine to generate gains charts based on the comparison of the marketing campaign results against said marketing defined dimensions and said risk defined dimensions.

22. A system according to claim 12 wherein said risk models comprise a payment behavior prediction model, a delinquency model, a bad debt model, a fraud detection model, a bankruptcy model, and a hit and run model.