Target marketing

• US 8,768,746 B2
• Filed: 06/10/2013
• Issued: 07/01/2014
• Est. Priority Date: 10/21/2008
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Anderson, James R., Ramsey, Mark Steven, Selby, David Alec
• Primary Examiner(s): Loftis, Johnna
• Assistant Examiner(s): KARMIS, ALISSA D
• Application Number:
  US13/914,063
• Publication Number:
  US 20130268322A1
• Time in Patent Office: 386 Days
• Field of Search: 705/14.4, 705/14.43, 705/14.49, 705/14.66
• US Class Current: 705/7.33
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Claims

1. A method comprising:

receiving, by a computing system, total population data comprising a list of individuals;

identifying, by said computing system, a first peer group of individuals from said total population data, wherein said first peer group is associated with products or services of a first marketing action;

presenting, by said computing system to an end user, a first graphical user interface (GUI) comprising original attribute selection criteria;

receiving, by said computing system from said end user in response to said original attribute selection criteria, first selection data indicating selections associated with said attribute selection criteria;

generating, by a computer processor of said computing system based on said first selection data, a first list comprising related attributes characterizing said first peer group, wherein the related attributes consist of an age and gender spending habits of said first peer group over a given time period, wherein said related attributes comprise a first group of attributes, a second group of attributes, and a third group of attributes, wherein said first group of attributes comprises attributes directly associated with said marketing action, wherein said second group of attributes comprises attributes associated with demographic data related to said first peer group, wherein said third group of attributes comprises attributes associated with additional purchasing habits of said first peer group of individuals, and wherein said additional purchasing habits are not associated with said marketing action;

enabling, by said computing system in response to a command from said end user, an override resulting in removal a first attribute of the related attributes from the first list;

generating, by said computing system, a second list comprising said related attributes associated with said first peer group except said first attribute;

selecting, by said computing system, said related attributes from said second list, wherein said selecting is based on data associated with said total population data, and wherein said selecting said related attributes from said second list comprises;

calculating, by said computing system, a significance index value for each related attribute of said related attributes, wherein said calculating said significance index value for each said related attribute of said related attributes comprises;

determining, by said computing system, associated bin counts comprising normalized values for all said attributes associated with said first peer group;

calculating, by said computing system, a number of records associated with calculating mean values;

modifying, by said computing system, said associated bin counts by selecting specified bins located to the left and to the right of a specified bin and totaling said specified bins;

determining, by said computing system, multiple temporary significance index values based on said totaling said specified bins;

determining, by said computing system, that a value generated during said totaling said specified bins is greater than a specified percentage; and

setting, by said computing system, one value of said multiple temporary significance index values as said significance index value;

identifying, by said computing system, a target group of individuals of said total population data, wherein said target group of individuals is predicted to be associated with said products or services of said first marketing action;

associating, by said computing system, said related attributes with said individuals of said target group;

generating, by said computing system based on said associating, scores associated with said individuals of said target group;

calculating, by said computing system executing a Euclidean distance equation, normalized distance values between said target group of individuals and said related attributes;

capping, by said computing system, said normalized distance values at a specified percentile value;

computing, by said computing system, a vector for each individual of said group of individuals, wherein each said vector indicates a strength of purchase behavior for each said individual;

ranking, by said computing system, said individuals of said target group, wherein said ranking is based on said scores, and wherein said ranking indicates a status of each individual of said individuals of said target group with respect to each other individual of said individuals of said target group with respect to products or services of said marketing action;

generating, by said computing system, a ranking list based on said ranking, wherein said ranking list comprises said individuals of said target group in a ranked order.

2. The method of claim 1, further comprising:

selecting, by said computing system, a preferred group of individuals of said individuals of said target group, wherein said preferred group of individuals are associated with higher scores of said scores than all other individuals of said individuals of said target group;

generating, by said computing system, a modified ranking list from said ranking list such, wherein said modified ranking list consists of said preferred group of individuals in said ranked order;

storing, by said computing system, said modified ranking list; and

transmitting, by said computing system to said preferred group of individuals, said first marketing action.

3. The method of claim 1, wherein said selecting said related attributes from said second list comprises:

determining, by said computing system, a 75^th percentile of all said attributes associated with said first peer group; and

normalizing, by said computing system, all said attributes associated with said first peer group with respect to attributes associated with said 75^th percentile.

4. The method of claim 1, wherein said scores associated with said individuals of said target group indicate a relationship between said individuals of said target group and said first peer group of individuals.

5. The method of claim 1, further comprising:

receiving, by said computing system from said end user, second selection data associated with said related attributes characterizing said first peer group; and

before said associating said related attributes, modifying, by said computing system based on said second selection data, said first list comprising said related attributes characterizing said first peer group.

6. The method of claim 5, wherein said modifying said first list comprises:

removing, by said computing system from said first list, at least one attribute of said related attributes characterizing said first peer group, wherein said related attributes do not comprise said least one attribute.

7. The method of claim 5, wherein said modifying said first list comprises:

adding, by said computing system to said first list, at least one new attribute characterizing said first peer group, wherein said related attributes comprises said least one new attribute.

8. The method of claim 1, further comprising:

before said generating said first list, receiving by said computing system from said end user, a request for detailed attribute selection criteria associated with said original attribute selection criteria;

presenting, by said computing system to said end user in response to said request, a second (GUI) comprising said detailed attribute selection criteria; and

receiving, by said computing system from said end user in response to said detailed attribute selection criteria, second selection data indicating selections associated with said detailed attribute selection criteria, wherein said generating said first list is further based on said second selection data.

9. The method of claim 1, wherein said original attribute selection criteria is presented to said end user as a plurality of attribute indicators, and wherein each attribute indicator of said plurality of attribute indicators comprises an attribute indicator selection mechanism for allowing said end user to enable attributes associated with each said attribute indicator.

10. A computer program product, comprising a program storage device having a computer readable program code stored therein, said computer readable program code comprising an algorithm that when executed by a computer processor of a computing system implements a method comprising:

receiving, by said computing system, total population data comprising a list of individuals;

identifying, by said computing system, a first peer group of individuals from said total population data, wherein said first peer group is associated with products or services of a first marketing action;

presenting, by said computing system to an end user, a first graphical user interface (GUI) comprising original attribute selection criteria;

receiving, by said computing system from said end user in response to said original attribute selection criteria, first selection data indicating selections associated with said attribute selection criteria;

generating, by a computer processor of said computing system based on said first selection data, a first list comprising related attributes characterizing said first peer group, wherein the related attributes consist of an age and gender spending habits of said first peer group over a given time period, wherein said related attributes comprise a first group of attributes, a second group of attributes, and a third group of attributes, wherein said first group of attributes comprises attributes directly associated with said marketing action, wherein said second group of attributes comprises attributes associated with demographic data related to said first peer group, wherein said third group of attributes comprises attributes associated with additional purchasing habits of said first peer group of individuals, and wherein said additional purchasing habits are not associated with said marketing action;

enabling, by said computing system in response to a command from said end user, an override resulting in removal a first attribute of the related attributes from the first list;

generating, by said computing system, a second list comprising said related attributes associated with said first peer group except said first attribute;

selecting, by said computing system, said related attributes from said second list, wherein said selecting is based on data associated with said total population data, and wherein said selecting said related attributes from said second list comprises;

calculating, by said computing system, a significance index value for each related attribute of said related attributes, wherein said calculating said significance index value for each said related attribute of said related attributes comprises;

determining, by said computing system, associated bin counts comprising normalized values for all said attributes associated with said first peer group;

calculating, by said computing system, a number of records associated with calculating mean values;

modifying, by said computing system, said associated bin counts by selecting specified bins located to the left and to the right of a specified bin and totaling said specified bins;

determining, by said computing system, multiple temporary significance index values based on said totaling said specified bins;

determining, by said computing system, that a value generated during said totaling said specified bins is greater than a specified percentage; and

setting, by said computing system, one value of said multiple temporary significance index values as said significance index value;

identifying, by said computing system, a target group of individuals of said total population data, wherein said target group of individuals is predicted to be associated with said products or services of said first marketing action;

associating, by said computing system, said related attributes with said individuals of said target group;

generating, by said computing system based on said associating, scores associated with said individuals of said target group;

calculating, by said computing system executing a Euclidean distance equation, normalized distance values between said target group of individuals and said related attributes;

capping, by said computing system, said normalized distance values at a specified percentile value;

computing, by said computing system, a vector for each individual of said group of individuals, wherein each said vector indicates a strength of purchase behavior for each said individual;

ranking, by said computing system, said individuals of said target group, wherein said ranking is based on said scores, and wherein said ranking indicates a status of each individual of said individuals of said target group with respect to each other individual of said individuals of said target group with respect to products or services of said marketing action;

generating, by said computing system, a ranking list based on said ranking, wherein said ranking list comprises said individuals of said target group in a ranked order.

11. A computing system comprising a processor coupled to a program storage device having computer readable program code stored therein, storing instructions that when executed by the processor implements a ranking method, said method comprising:

receiving, by said computing system, total population data comprising a list of individuals;

identifying, by said computing system, a first peer group of individuals from said total population data, wherein said first peer group is associated with products or services of a first marketing action;

presenting, by said computing system to an end user, a first graphical user interface (GUI) comprising original attribute selection criteria;

receiving, by said computing system from said end user in response to said original attribute selection criteria, first selection data indicating selections associated with said attribute selection criteria;

generating, by a computer processor of said computing system based on said first selection data, a first list comprising related attributes characterizing said first peer group, wherein the related attributes consist of an age and gender spending habits of said first peer group over a given time period, wherein said related attributes comprise a first group of attributes, a second group of attributes, and a third group of attributes, wherein said first group of attributes comprises attributes directly associated with said marketing action, wherein said second group of attributes comprises attributes associated with demographic data related to said first peer group, wherein said third group of attributes comprises attributes associated with additional purchasing habits of said first peer group of individuals, and wherein said additional purchasing habits are not associated with said marketing action;

enabling, by said computing system in response to a command from said end user, an override resulting in removal a first attribute of the related attributes from the first list;

generating, by said computing system, a second list comprising said related attributes associated with said first peer group except said first attribute;

selecting, by said computing system, said related attributes from said second list, wherein said selecting is based on data associated with said total population data, and wherein said selecting said related attributes from said second list comprises;

calculating, by said computing system, a significance index value for each related attribute of said related attributes, wherein said calculating said significance index value for each said related attribute of said related attributes comprises;

determining, by said computing system, associated bin counts comprising normalized values for all said attributes associated with said first peer group;

calculating, by said computing system, a number of records associated with calculating mean values;

modifying, by said computing system, said associated bin counts by selecting specified bins located to the left and to the right of a specified bin and totaling said specified bins;

determining, by said computing system, multiple temporary significance index values based on said totaling said specified bins;

determining, by said computing system, that a value generated during said totaling said specified bins is greater than a specified percentage; and

setting, by said computing system, one value of said multiple temporary significance index values as said significance index value;

identifying, by said computing system, a target group of individuals of said total population data, wherein said target group of individuals is predicted to be associated with said products or services of said first marketing action;

associating, by said computing system, said related attributes with said individuals of said target group;

generating, by said computing system based on said associating, scores associated with said individuals of said target group;

calculating, by said computing system executing a Euclidean distance equation, normalized distance values between said target group of individuals and said related attributes;

capping, by said computing system, said normalized distance values at a specified percentile value;

computing, by said computing system, a vector for each individual of said group of individuals, wherein each said vector indicates a strength of purchase behavior for each said individual;

ranking, by said computing system, said individuals of said target group, wherein said ranking is based on said scores, and wherein said ranking indicates a status of each individual of said individuals of said target group with respect to each other individual of said individuals of said target group with respect to products or services of said marketing action;

generating, by said computing system, a ranking list based on said ranking, wherein said ranking list comprises said individuals of said target group in a ranked order.

12. The computing system of claim 11, wherein said method further comprises:

selecting, by said computing system, a preferred group of individuals of said individuals of said target group, wherein said preferred group of individuals are associated with higher scores of said scores than all other individuals of said individuals of said target group;

generating, by said computing system, a modified ranking list from said ranking list such, wherein said modified ranking list consists of said preferred group of individuals in said ranked order;

storing, by said computing system, said modified ranking list; and

transmitting, by said computing system to said preferred group of individuals, said first marketing action.

13. The computing system of claim 11, wherein said selecting said related attributes from said second list comprises:

determining, by said computing system, a 75^th percentile of all said attributes associated with said first peer group; and

normalizing, by said computing system, all said attributes associated with said first peer group with respect to attributes associated with said 75^th percentile.

14. The computing system of claim 11, wherein said method further comprises:

receiving, by said computing system from said end user, second selection data associated with said related attributes characterizing said first peer group; and

before said associating said related attributes, modifying, by said computing system based on said second selection data, said first list comprising said related attributes characterizing said first peer group.

15. The computing system of claim 14, wherein said modifying said first list comprises:

removing, by said computing system from said first list, at least one attribute of said related attributes characterizing said first peer group, wherein said related attributes do not comprise said least one attribute.

16. The computing system of claim 14, wherein said modifying said first list comprises:

adding, by said computing system to said first list, at least one new attribute characterizing said first peer group, wherein said related attributes comprises said least one new attribute.

17. The computing system of claim 11, wherein said method further comprises:

before said generating said first list, receiving by said computing system from said end user, a request for detailed attribute selection criteria associated with said original attribute selection criteria;

presenting, by said computing system to said end user in response to said request, a second (GUI) comprising said detailed attribute selection criteria; and

receiving, by said computing system from said end user in response to said detailed attribute selection criteria, second selection data indicating selections associated with said detailed attribute selection criteria, wherein said generating said first list is further based on said second selection data.

18. The computing system of claim 11, wherein said original attribute selection criteria is presented to said end user as a plurality of attribute indicators, and wherein each attribute indicator of said plurality of attribute indicators comprises an attribute indicator selection mechanism for allowing said end user to enable attributes associated with each said attribute indicator.

19. The computing system of claim 11, wherein said scores associated with said individuals of said target group indicate a relationship between said individuals of said target group and said first peer group of individuals.