On demand selection of marketing offers in response to inbound communications

• US 7,881,959 B2
• Filed: 05/03/2005
• Issued: 02/01/2011
• Est. Priority Date: 05/03/2005
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Selby, David A., Ramsey, Mark S.
• Primary Examiner(s): Sterrett; Jonathan G.
• Assistant Examiner(s): Lan; Tzu-Hsiang (Sean)
• Application Number:
  US11/121,215
• Publication Number:
  US 20060253309A1
• Time in Patent Office: 2,100 Days
• Field of Search: None
• US Class Current: 705/7.36
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

A database system and method for ordering on demand a plurality of marketing offers for offering to a candidate in response to an inbound communication from the candidate. The database system comprises a first database structure storing a first list identifying marketing offers, a second database structure storing a second list of candidates, and a database manager software application stored on a computer readable medium. The database manager software application comprises an on demand optimization tool. The on demand optimization tool is for optimizing and sorting, the marketing offers from the first list with for a first candidate from the second list in response to an inbound communication from the first candidate.

Claims

1. A database system, comprising a computer processor coupled to a computer-readable memory unit, a database manager software application, a first database structure storing a first list identifying marketing events, a second database structure storing a second list of candidates, and a third database structure storing a data model consisting of credit card balances, purchasing history, returned-items history, past marketing events that have been accepted by said candidates, and past spending habits associated with said second list of candidates, wherein said third database structure further comprises a predetermined total budget for said candidates regarding an amount of money that an entity offering said marketing events is willing to spend to offer said marketing events to the said candidates, wherein said database manager software application is stored on said computer-readable memory unit, wherein said database manager software application comprises an on demand optimization tool and a computing tool, and wherein each marketing event from said first list comprises a marketing offer consisting of a credit based offer and an identified channel means for communicating said marketing offer, said memory unit comprising instructions that when executed by the computer processor implements a method comprising:

calculating, by said computing tool, value scores associated with said marketing events from said first list, wherein each value score of said value scores is associated with a marketing event from said first list, wherein said value scores are consisting of an expected profit gain associated with each said marketing offer, and wherein each said value score is calculated based on an annual return of credit repayments vs infrastructure costs balanced against a risk of a candidate of said second list of candidates defaulting on said credit repayments;

applying, by said on demand optimization tool, constraint data to said marketing events, wherein said constraint data comprises first data, second data, and third data, wherein said first data consists of an amount of money regarding execution costs for offering said marketing events to a candidate, wherein said second data consists of a total budgeted amount allotted for offering all of said marketing events, wherein said third data comprises a maximum number of times for offering a marketing event of said marketing event to said candidate, and wherein said execution costs comprise costs for promotional materials, mailing costs, and telemarketing costs;

calculating, by said computing tool, acceptance probability scores for said marketing events from said first list for said candidate, wherein each acceptance probability score of said acceptance probability scores is used to determine a probability that said candidate of said second list will respond to and accept said marketing events within a specified time frame, wherein said acceptance probability scores are calculated using said predetermined total budget and said data model, and wherein said acceptance probability scores are computed simultaneously in parallel;

receiving, by said computer processor, an inbound communication initiated by and from said candidate;

receiving, by said computer processor, candidate data associated with said candidate;

calculating, by said computing tool, a ranking score for each of said marketing events with respect to said candidate, wherein said calculating each said ranking score comprises multiplying each said acceptance response probability score with an associated value score of said value scores;

optimizing, by said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidate;

sorting, by said computer processor executing said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidate;

generating, by said computer processor in response to said sorting, a ranking list comprising a ranked version of said marketing events and said ranking scores for said candidate;

storing, by said database system, said ranking list;

applying, by said optimization tool, timing constraint data to each ranking score of said ranking scores, wherein said timing constraint data comprises timing related data consisting of a maximum number of offerings for each said marketing event;

removing, by said optimization tool in response to results of said applying said timing constraint data, a group of marketing events and associated ranking scores from said ranking list resulting in a second ranking list comprising first remaining marketing events and first remaining ranking scores, wherein said group of marketing events comprise marketing events comprising timing constraints specified by said timing constraint data;

applying, by said optimization tool, monetary constraint data to first remaining value scores associated with said first remaining ranking scores, wherein said monetary constraint data consists of an amount of money regarding execution costs for offering said remaining marketing events to said candidate, wherein said execution costs consist of costs for promotional materials, mailing costs, telemarketing costs, and infrastructure costs, and wherein said applying said monetary constraint data comprises subtracting said monetary constraint data from said predetermined total budget;

applying, by said computer processor to said first remaining marketing events, data indicating a reason for said inbound communication from said candidate;

applying, by said computer processor to said first remaining marketing events, risk factors associated with offering said group of marketing events to said candidate, said risk factors associated with a credit report for said candidate;

removing, by said optimization tool in response to results of said applying said monetary constraint data, said data indicating a reason for said inbound communication from said candidate, and said applying said risk factors, a second group of marketing events and associated ranking scores from said second ranking list resulting in a third ranking list comprising second remaining marketing events and second remaining ranking scores;

ranking in simultaneously in parallel, by said optimization tool, said second remaining ranking scores;

generating, by said computer processor in response to said ranking said second remaining ranking scores, a final ranking list comprising said second remaining marketing events of said marketing events and said second remaining ranking scores in a specified order resulting from said ranking said second remaining ranking scores; and

offering, by said computer processor during said inbound communication, said second remaining marketing events of said final ranking list.

2. The database system of claim 1, wherein each of said acceptance probability scores are computed on demand based on said reason for said inbound communication from said first candidate.

3. The database system of claim 1, wherein said on demand optimization tool is for generating a priority list comprising said sorted marketing offers, and wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first list to said first candidate upon receiving said inbound communication.

4. The database system of claim 1, wherein said on demand optimization tool is for generating a priority list comprising a first group of marketing offers from said sorted marketing offers that fall within a specified set of constraints, and wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first group of marketing offers to said first candidate upon receiving said inbound communication.

5. The database system of claim 1, wherein said second list of candidates comprises existing customers of an entity that is offering said marketing offers from said first list.

6. The database system of claim 1, wherein each marketing offer is selected from the group consisting of a product offer and a service offer.

7. The database system of claim 1, wherein said optimizing and said sorting are based on said reason for said inbound communication from said first candidate.

8. A selection method, comprising:

providing a database system comprising a computer processor coupled to a computer-readable memory unit, a database manager software application, a first database structure storing a first list identifying marketing events, a second database structure storing a second list of candidates, and a third database structure storing a data model consisting of credit card, purchasing history, returned-items history, past marketing events that have been accepted by said candidates, and past spending habits associated with said second list of candidates, wherein said third database structure further comprises a predetermined total budget for said candidates regarding an amount of money that an entity offering said marketing events is willing to spend to offer said marketing events to the said candidates, wherein said database manager software application is stored on said computer-readable memory unit, wherein said database manager software application comprises an on demand optimization tool and a computing tool, and wherein each marketing event from said first list comprises a marketing offer consisting of a credit based offer and an identified channel means for communicating said marketing offer;

calculating, by said computing tool, value scores associated with said marketing events from said first list, wherein each value score of said value scores is associated with a marketing event from said first list, wherein said value scores are consisting of an expected profit gain associated with each said marketing offer, and wherein each said value score is calculated based on an annual return of credit repayments vs infrastructure costs balanced against a risk of a candidate of said second list of candidates defaulting on said credit repayments;

applying, by said on demand optimization tool, constraint data to said marketing events, wherein said constraint data comprises first data, second data, and third data, wherein said first data consists of an amount of money regarding execution costs for offering said marketing events to a candidate, wherein said second data consists of a total budgeted amount allotted for offering all of said marketing events, wherein said third data comprises a maximum number of times for offering a marketing event of said marketing event to said candidate, and wherein said execution costs comprise costs for promotional materials, mailing costs, and telemarketing costs;

calculating, by said computing tool, acceptance probability scores for said marketing events from said first list for said candidate, wherein each acceptance probability score of said acceptance probability scores is used to determine a probability that a different said candidate of said second list will respond to and accept said marketing events within a specified time frame, wherein said acceptance probability scores are calculated using said predetermined total budget and said data model, and wherein said acceptance probability scores are computed simultaneously in parallel;

receiving, by said computer processor, an inbound communication initiated by and from said candidate;

receiving, by said computer processor, candidate data associated with said candidate;

calculating, by said computing tool, a ranking score for each of said marketing events with respect to said candidate, wherein said calculating each said ranking score comprises multiplying each said acceptance response probability score with an associated value score of said value scores;

optimizing, by said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidate;

sorting, by said computer processor executing said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidate;

generating, by said computer processor in response to said sorting, a ranking list comprising a ranked version of said marketing events and said ranking scores for said candidate;

storing, by said database system, said ranking list;

applying, by said optimization tool, timing constraint data to each ranking score of said ranking scores, wherein said timing constraint data comprises timing related data consisting of a maximum number of offerings for each said marketing event;

removing, by said optimization tool in response to results of said applying said timing constraint data, a group of marketing events and associated ranking scores from said ranking list resulting in a second ranking list comprising first remaining marketing events and first remaining ranking scores, wherein said group of marketing events comprise marketing events comprising timing constraints specified by said timing constraint data;

applying, by said optimization tool, monetary constraint data to first remaining value scores associated with said first remaining ranking scores, wherein said monetary constraint data consists of an amount of money regarding execution costs for offering said remaining marketing events to said candidate, wherein said execution costs consist of costs for promotional materials, mailing costs, telemarketing costs, and infrastructure costs, and wherein said applying said monetary constraint data comprises subtracting said monetary constraint data from said predetermined total budget;

applying, by said computer processor to said first remaining marketing events, data indicating a reason for said inbound communication from said candidate;

applying, by said computer processor to said first remaining marketing events, risk factors associated with offering said group of marketing events to said candidate, said risk factors associated with a credit report for said candidate;

removing, by said optimization tool in response to results of said applying said monetary constraint data, said data indicating a reason for said inbound communication from said candidate, and said applying said risk factors, a second group of marketing events and associated ranking scores from said second ranking list resulting in a third ranking list comprising second remaining marketing events and second remaining ranking scores;

ranking in simultaneously in parallel, by said optimization tool, said second remaining ranking scores;

generating, by said computer processor in response to said ranking said second remaining ranking scores, a final ranking list comprising said second remaining marketing events of said marketing events and said second remaining ranking scores in a specified order resulting from said ranking said second remaining ranking scores; and

offering, by said computer processor during said inbound communication, said second remaining marketing events of said final ranking list.

9. The method of claim 8, wherein each of said acceptance probability scores are computed on demand based on said reason for said inbound communication from said first candidate.

10. The method of claim 8, further comprising generating by the on demand optimization tool, a priority list comprising said sorted marketing offers, wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first list to said first candidate upon receiving said inbound communication.

11. The method of claim 8, further comprising generating by the on demand optimization tool, a priority list comprising a first group of marketing offers from said sorted marketing offers that fall within a specified set of constraints, wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first group of marketing offers to said first candidate upon receiving said inbound communication.

12. The method of claim 8, wherein said second list of candidates comprises existing customers of an entity that is offering said marketing offers from said first list.

13. The method of claim 8, wherein each marketing offer is selected from the group consisting of a product offer and a service offer.

14. The method of claim 8, wherein said optimizing and said sorting are additionally based on said reason for said inbound communication from said first candidate.

15. A process for integrating computing infrastructure, comprising integrating computer-readable code into a computing system comprising a computer processor, wherein the code in combination with the computing system comprises a database system comprising a database manager software application stored on a computer readable medium, a first database structure storing a first list identifying marketing events, a second database structure storing a second list of candidates, and a third database structure storing a data model consisting of credit card balances, purchasing history, returned-items history, past marketing events that have been accepted by said candidates, and past spending habits associated with said second list of candidates, wherein said third database structure further comprises a predetermined total budget for said candidates regarding an amount of money that an entity offering said marketing events is willing to spend to offer said marketing events to the said candidates, wherein said database manager software application is stored on said computer-readable memory unit, wherein said database manager software application comprises an on demand optimization tool and a computing tool, and wherein each marketing event from said first list comprises a marketing offer consisting of a credit based offer and an identified channel means for communicating said marketing offer, and wherein the code in combination with the computing system is adapted to implement a method for performing the steps of:

calculating, by said computing tool, value scores associated with said marketing events from said first list, wherein each value score of said value scores is associated with a marketing event from said first list, wherein said value scores are consisting of an expected profit gain associated with each said marketing offer, and wherein each said value score is calculated based on an annual return of credit repayments vs infrastructure costs balanced against a risk of a candidate of said second list of candidates defaulting on said credit repayments;

applying, by said on demand optimization tool, constraint data to said marketing events, wherein said constraint data comprises first data, second data, and third data, wherein said first data consists of an amount of money regarding execution costs for offering said marketing events to a candidate, wherein said second data consists of a total budgeted amount allotted for offering all of said marketing events, wherein said third data comprises a maximum number of times for offering a marketing event of said marketing event to said candidate, and wherein said execution costs comprise costs for promotional materials, mailing costs, and telemarketing costs;

calculating, by said computing tool, acceptance probability scores for said marketing events from said first list for said candidate, wherein each acceptance probability score of said acceptance probability scores is used to determine a probability that said candidate of said second list will respond to and accept said marketing events within a specified time frame, wherein said acceptance probability scores are calculated using said predetermined total budget and said data model, and wherein said acceptance probability scores are computed simultaneously in parallel;

receiving, by said computer processor, an inbound communication initiated by and from said candidate;

receiving, by said computer processor, candidate data associated with said candidate;

calculating, by said computing tool, a ranking score for each of said marketing events with respect to said candidate, wherein said calculating each said ranking score comprises multiplying each said acceptance response probability score with an associated value score of said value scores;

optimizing, by said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidate;

sorting, by said computer processor executing said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidate;

generating, by said computer processor in response to said sorting, a ranking list comprising a ranked version of said marketing events and said ranking scores for said candidate;

storing, by said database system, said ranking list;

applying, by said optimization tool, timing constraint data to each ranking score of said ranking scores, wherein said timing constraint data comprises timing related data consisting of a maximum number of offerings for each said marketing event;

removing, by said optimization tool in response to results of said applying said timing constraint data, a group of marketing events and associated ranking scores from said ranking list resulting in a second ranking list comprising first remaining marketing events and first remaining ranking scores, wherein said group of marketing events comprise marketing events comprising timing constraints specified by said timing constraint data;

applying, by said optimization tool, monetary constraint data to first remaining value scores associated with said first remaining ranking scores, wherein said monetary constraint data consists of an amount of money regarding execution costs for offering said remaining marketing events to said candidate, wherein said execution costs consist of costs for promotional materials, mailing costs, telemarketing costs, and infrastructure costs, and wherein said applying said monetary constraint data comprises subtracting said monetary constraint data from said predetermined total budget;

applying, by said computer processor to said first remaining marketing events, data indicating a reason for said inbound communication from said candidate;

applying, by said computer processor to said first remaining marketing events, risk factors associated with offering said group of marketing events to said candidate, said risk factors associated with a credit report for said candidate;

removing, by said optimization tool in response to results of said applying said monetary constraint data, said data indicating a reason for said inbound communication from said candidate, and said applying said risk factors, a second group of marketing events and associated ranking scores from said second ranking list resulting in a third ranking list comprising second remaining marketing events and second remaining ranking scores;

ranking in simultaneously in parallel, by said optimization tool, said second remaining ranking scores;

generating, by said computer processor in response to said ranking said second remaining ranking scores, a final ranking list comprising said second remaining marketing events of said marketing events and said second remaining ranking scores in a specified order resulting from said ranking said second remaining ranking scores; and

offering, by said computer processor during said inbound communication, said second remaining marketing events of said final ranking list.

16. The process of claim 15, wherein each of said acceptance probability scores are computed on demand based on said reason for said inbound communication from said first candidate.

17. The process of claim 15, wherein said method further comprises generating by the on demand optimization tool, a priority list comprising said sorted marketing offers, and wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first list to said first candidate upon receiving said inbound communication.

18. The process of claim 15, wherein said method further comprises generating by the on demand optimization tool, a priority list comprising a first group of marketing offers from said sorted marketing offers that fall within a specified set of constraints, wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first group of marketing offers to said first candidate upon receiving said inbound communication.

19. The process of claim 15, wherein said second list of candidates comprises existing customers of an entity that is offering said plurality of marketing offers.

20. The process of claim 15, wherein each marketing offer is selected from the group consisting of a product offer and a service offer.

21. The process of claim 15, wherein said method further comprises optimizing and said sorting said ranking scores additionally based on said reason for said inbound communication from said first candidate.

22. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code comprising an algorithm adapted to implement a method within a database system, said database system, comprising a computer processor coupled to a computer-readable memory unit, a database manager software application, a first database structure storing a first list identifying marketing events, a second database structure storing a second list of candidates, and a third database structure storing a data model consisting of credit card balances, purchasing history, returned-items history, past marketing events that have been accepted by said candidates, and past spending habits associated with said second list of candidates, wherein said third database structure further comprises a predetermined total budget for said candidates regarding an amount of money that an entity offering said marketing events is willing to spend to offer said marketing events to the said candidates, wherein said database manager software application is stored on said computer-readable memory unit, wherein said database manager software application comprises an on demand optimization tool and a computing tool, and wherein each marketing event from said first list comprises a marketing offer consisting of a credit based offer and an identified channel means for communicating said marketing offer, said method comprising the steps of:

calculating, by said computing tool, value scores associated with said marketing events from said first list, wherein each value score of said value scores is associated with a marketing event from said first list, wherein said value scores are consisting of an expected profit gain associated with each said marketing offer, and wherein each said value score is calculated based on an annual return of credit repayments vs infrastructure costs balanced against a risk of a candidate of said second list of candidates defaulting on said credit repayments;

applying, by said on demand optimization tool, constraint data to said marketing events, wherein said constraint data comprises first data, second data, and third data, wherein said first data consists of an amount of money regarding execution costs for offering said marketing events to a candidate, wherein said second data consists of a total budgeted amount allotted for offering all of said marketing events, wherein said third data comprises a maximum number of times for offering a marketing event of said marketing event to said candidate, and wherein said execution costs comprise costs for promotional materials, mailing costs, and telemarketing costs;

calculating, by said computing tool, acceptance probability scores for said marketing events from said first list for said candidate, wherein each acceptance probability score of said acceptance probability scores is used to determine a probability that said candidate of said second list will respond to and accept said marketing events within a specified time frame, wherein said acceptance probability scores are calculated using said predetermined total budget and said data model, and wherein said acceptance probability scores are computed simultaneously in parallel;

receiving, by said computer processor, an inbound communication initiated by and from said candidate;

receiving, by said computer processor, candidate data associated with said candidate;

calculating, by said computing tool, a ranking score for each of said marketing events with respect to said candidate, wherein said calculating each said ranking score comprises multiplying each said acceptance response probability score with an associated value score of said value scores;

optimizing, by said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said first candidate;

sorting, by said computer processor executing said on demand optimization tool, each said marketing offer from said first list for said candidate from said second list in response to said inbound communication from said candidates;

generating, by said computer processor in response to said sorting, a ranking list comprising a ranked version of said marketing events and said ranking scores for said candidate;

storing, by said database system, said ranking list;

applying, by said optimization tool, timing constraint data to each ranking score of said ranking scores, wherein said timing constraint data comprises timing related data consisting of a maximum number of offerings for each said marketing event;

removing, by said optimization tool in response to results of said applying said timing constraint data, a group of marketing events and associated ranking scores from said ranking list resulting in a second ranking list comprising first remaining marketing events and first remaining ranking scores, wherein said group of marketing events comprise marketing events comprising timing constraints specified by said timing constraint data;

applying, by said optimization tool, monetary constraint data to first remaining value scores associated with said first remaining ranking scores, wherein said monetary constraint data consists of an amount of money regarding execution costs for offering said remaining marketing events to said candidate, wherein said execution costs consist of costs for promotional materials, mailing costs, telemarketing costs, and infrastructure costs, and wherein said applying said monetary constraint data comprises subtracting said monetary constraint data from said predetermined total budget;

applying, by said computer processor to said first remaining marketing events, data indicating a reason for said inbound communication from said candidate;

applying, by said computer processor to said first remaining marketing events, risk factors associated with offering said group of marketing events to said candidate, said risk factors associated with a credit report for said candidate;

removing, by said optimization tool in response to results of said applying said monetary constraint data, said data indicating a reason for said inbound communication from said candidate, and said applying said risk factors, a second group of marketing events and associated ranking scores from said second ranking list resulting in a third ranking list comprising second remaining marketing events and second remaining ranking scores;

ranking in simultaneously in parallel, by said optimization tool, said second remaining ranking scores;

generating, by said computer processor in response to said ranking said second remaining ranking scores, a final ranking list comprising said second remaining marketing events of said marketing events and said second remaining ranking scores in a specified order resulting from said ranking said second remaining ranking scores; and

offering, by said computer processor during said inbound communication, said second remaining marketing events of said final ranking list.

23. The computer program product of claim 22, wherein each of said acceptance probability scores are computed on demand based on said reason for said inbound communication from said first candidate.

24. The computer program product of claim 22, wherein said method further comprises generating by the on demand optimization tool, a priority list comprising said sorted marketing offers, and wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first list to said first candidate upon receiving said inbound communication.

25. The computer program product of claim 22, wherein said method further comprises generating by the on demand optimization tool, a priority list comprising a first group of marketing offers from said sorted marketing offers that fall within a specified set of constraints, wherein said priority list prioritizes an order in which to offer each of said marketing offers from said first group of marketing offers to said first candidate upon receiving said inbound communication.

26. The computer program product of claim 22, wherein said second list of candidates comprises existing customers of an entity that is offering said marketing offers from said first list.

27. The computer program product of claim 22, wherein each marketing offer is selected from the group consisting of a product offer and a service offer.

28. The computer program product of claim 22, wherein said method further comprises optimizing and said sorting said ranking scores additionally based on said reason for said inbound communication from said first candidate.