Incorporating Actionable Feedback to Dynamically Evolve Campaigns

• US 20130343536A1
• Filed: 06/22/2012
• Published: 12/26/2013
• Est. Priority Date: 06/22/2012
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Dey, Kuntal, Nagar, Seema
• Application Number:
  US13/530,398
• Publication Number:
  US 20130343536A1
• Time in Patent Office: Days
• US Class Current: 379/266.08
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Techniques, an apparatus and an article of manufacture for incorporating contextual reinforcement to dynamically evolve an information campaign. A method includes determining an evolution of an information campaign with respect to at least one end objective up to a pre-determined point of advancement in the life cycle of the information campaign, predicting a future progression of the information campaign from the pre-determined point of advancement with respect to the at least one end objective based on said evolution and at least one learned model of progression, wherein said future progression includes a prediction of a potential outcome of the information campaign at one or more given time points in the life cycle, and incorporating a contextual reinforcement campaign into the information campaign to dynamically evolve the information campaign toward the at least one end objective, creating an evolved information campaign, wherein the reinforcement campaign is based on said future progression.

Claims

1. A method for incorporating contextual reinforcement to dynamically evolve an information campaign, the method comprising:

determining an evolution of an information campaign with respect to at least one end objective up to a pre-determined point of advancement in the life cycle of the information campaign;

predicting a future progression of the information campaign from the pre-determined point of advancement with respect to the at least one end objective based on said evolution and at least one learned model of progression, wherein said future progression includes a prediction of a potential outcome of the information campaign at one or more given time points in the life cycle; and

incorporating a contextual reinforcement campaign into the information campaign to dynamically evolve the information campaign toward the at least one end objective, creating an evolved information campaign, wherein the reinforcement campaign is based on said future progression;

wherein at least one of the steps is carried out by a computer device.

2. The method of claim 1, where said determining comprises determining an evolution of a class of campaigns.

3. The method of claim 2, wherein a class refers to a grouping of related campaigns.

4. The method of claim 1, where said determining comprises generating a success curve to quantify the evolution of the information campaign and to characterize a campaign class to which the information campaign belongs.

5. The method of claim 4, comprising leveraging the success curve to measure success of the information campaign with respect to the at least one end objective via comparison with success curves of other campaigns of the same campaign class.

6. The method of claim 4, comprising comparing the success curve with an expected curve of evolution of success status over time to quantify the deviation of a current status of the campaign with respect to an expected status at a given time.

7. The method of claim 1, wherein said incorporating comprises automatically incorporating a contextual reinforcement campaign into the information campaign by:

identifying existing reinforcement campaigns in an appropriate context from a repository;

scoring said identified reinforcement campaigns; and

automatically incorporating a predetermined number of the identified reinforcement campaigns.

8. The method of claim 1, wherein said incorporating comprises semi-automatically incorporating a contextual reinforcement campaign into the information campaign by:

identifying existing reinforcement campaigns in an appropriate context from a repository;

scoring said identified reinforcement campaigns; and

presenting a predetermined number of the identified reinforcement campaigns to a human reviewer for incorporation.

9. The method of claim 1, wherein said determining comprises evaluating the information campaign at one or more specified intervals of time after the information campaign is launched.

10. The method of claim 1, wherein said at least one learned model of progression includes at least one model based upon learning derived from campaigns belonging to the same class as the information campaign, and as found at similar stages from respective launches as the information campaign.

11. The method of claim 1, wherein said predicting comprises labeling the information campaign a potential failure if a number of failures greater than a pre-determined threshold of failures occur in the future progression.

12. The method of claim 1, comprising triggering composition of a contextual reinforcement campaign if the number of failures surpasses the pre-determined threshold of failures occur in the future progression.

13. The method of claim 1, wherein said determining comprises determining the evolution of the information campaign at a target level, wherein a target includes an individual or a group of individuals participating in the information campaign.

14. The method of claim 1, wherein said determining comprises determining the evolution of the information campaign across all target groups.

15. The method of claim 1, wherein said predicting comprises predicting the future progression of the information campaign at a target level, wherein a target includes an individual or a group of individuals participating in the information campaign.

16. The method of claim 1, wherein said predicting comprises predicting the future progression of the information campaign across all target groups.

17. The method of claim 1, wherein the context for a contextual reinforcement campaign is derived from the information campaign, the information campaign class, targets of the information campaign and/or prior contextual campaigns within the campaign class.

18. The method of claim 1, comprising computing a campaign-compatibility score for a target of the information campaign with respect to campaign class and the contextual reinforcement campaign.

19. An article of manufacture comprising a computer readable storage medium having computer readable instructions tangibly embodied thereon which, when implemented, cause a computer to carry out a plurality of method steps comprising:

determining an evolution of an information campaign with respect to at least one end objective up to a pre-determined point of advancement in the life cycle of the information campaign;

predicting a future progression of the information campaign from the pre-determined point of advancement with respect to the at least one end objective based on said evolution and at least one learned model of progression, wherein said future progression includes a prediction of a potential outcome of the information campaign at one or more given time points in the life cycle; and

incorporating a contextual reinforcement campaign into the information campaign to dynamically evolve the information campaign toward the at least one end objective, creating an evolved information campaign, wherein the reinforcement campaign is based on said future progression.

20. A system for incorporating contextual reinforcement to dynamically evolve an information campaign, comprising:

at least one distinct software module, each distinct software module being embodied on a tangible computer-readable medium;

a memory; and

at least one processor coupled to the memory and operative for;

determining an evolution of an information campaign with respect to at least one end objective up to a pre-determined point of advancement in the life cycle of the information campaign;

predicting a future progression of the information campaign from the pre-determined point of advancement with respect to the at least one end objective based on said evolution and at least one learned model of progression, wherein said future progression includes a prediction of a potential outcome of the information campaign at one or more given time points in the life cycle; and

incorporating a contextual reinforcement campaign into the information campaign to dynamically evolve the information campaign toward the at least one end objective, creating an evolved information campaign, wherein the reinforcement campaign is based on said future progression.