Industry specific brand benchmarking system based on social media strength of a brand

• US 8,620,718 B2
• Filed: 08/17/2012
• Issued: 12/31/2013
• Est. Priority Date: 06/04/2012
• Status: Active Grant

• Current Assignee: Unmetric, Inc. (Cision Ltd.)
• Original Assignee: Unmetric, Inc. (Cision Ltd.)
• Inventors: Varghese, Joseph, Krishnasami, Kumaravel, Narayan, Lakshmanan
• Primary Examiner(s): Jarrett, Scott L
• Application Number:
  US13/587,928
• Publication Number:
  US 20130325550A1
• Time in Patent Office: 501 Days
• Field of Search: 705/7.29
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

A brand monitoring platform (BMP) for brand benchmarking based on a brand'"'"'s social media strength is provided. The BMP acquires input information on the brand and identifies industries related to the brand and competing brands. The BMP acquires social media information related to the brand and the competing brands from multiple social media sources via a network, dynamically generates categories in one or more hierarchical levels in each of the industries based on an independent analysis of the social media information, and sorts the social media information into the categories using a sorting interface. The BMP generates an aggregate score using an audience score determined by measuring an aggregate reach of the brand and the competing brands based on weighted audience score metric parameters, and an engagement score determined by measuring interaction between the brand and the competing brands and their followers based on weighted engagement score metric parameters.

Claims

1. A computer implemented method for benchmarking a brand based on social media strength of said brand, comprising:

providing a brand monitoring platform comprising at least one processor configured to monitor said brand in a virtual social media environment;

acquiring input information on said brand by said brand monitoring platform;

identifying industries related to said brand and competing brands in said identified industries using said acquired input information on said brand by said brand monitoring platform;

acquiring social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in said virtual social media environment by said brand monitoring platform via a network;

dynamically generating categories in one or more hierarchical levels in each of said identified industries by said brand monitoring platform based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources;

sorting said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels by said brand monitoring platform using a sorting interface provided by said brand monitoring platform;

determining an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment by said brand monitoring platform based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information;

determining an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers by said brand monitoring platform based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information;

generating an aggregate score for said brand and said each of said competing brands by said brand monitoring platform using said determined audience score and said determined engagement score; and

determining social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands by said brand monitoring platform based on said aggregate score;

whereby said brand is benchmarked in comparison with said competing brands in said virtual social media environment based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment.

2. The computer implemented method of claim 1, wherein said dynamically generated categories comprise a location of each of said identified industries related to said brand and said each of said competing brands, a location of each of a plurality of authors of said social media information, types of said social media sources utilized by said brand and said each of said competing brands, and marketing elements.

3. The computer implemented method of claim 1, wherein said brand monitoring platform generates said aggregate score for said brand and said each of said competing brands by determining a weighted average of said determined audience score and said determined engagement score.

4. The computer implemented method of claim 1, wherein said weighted audience score metric parameters comprise a number of said followers of said brand and said each of said competing brands at said each of said social media sources, a rate of growth of said number of said followers of said brand and said each of said competing brands, a number of recommendations for said brand and said each of said competing brands at said each of said social media sources from each of said followers, a number of references made to said brand and said each of said competing brands at said each of said social media sources by said followers, and aggregate responses to one or more of products, services, and events associated with said brand and said each of said competing brands.

5. The computer implemented method of claim 1, wherein said weighted engagement score metric parameters comprise nature of responses to one or more brand actions of said brand and said each of said competing brands from each of said followers of said brand and said each of said competing brands, a number of brand notification messages, sentiments of said followers towards said brand and said each of said competing brands, a number of fan posts extracted from said acquired social media information, and relevance of said fan posts to said brand and said each of said competing brands.

6. The computer implemented method of claim 1, wherein said determination of said audience score for said brand and said each of said competing brands by said brand monitoring platform comprises:

normalizing measures corresponding to each of said audience score metric parameters;

assigning individual weights to said audience score metric parameters; and

determining a weighted average of said normalized measures corresponding to said each of said audience score metric parameters using said assigned individual weights.

7. The computer implemented method of claim 1, wherein said determination of said engagement score for said brand and said each of said competing brands by said brand monitoring platform comprises:

normalizing measures corresponding to each of said engagement score metric parameters;

assigning individual weights to said engagement score metric parameters; and

determining a weighted average of said normalized measures corresponding to said each of said engagement score metric parameters using said assigned individual weights.

8. The computer implemented method of claim 1, further comprising acquiring inputs for said sorting of said acquired social media information related to said brand and said competing brands in said each of said identified industries into said one or more of said dynamically generated categories in said one or more hierarchical levels by said brand monitoring platform from a user via said sorting interface.

9. The computer implemented method of claim 1, wherein said determination of said audience score and said engagement score for said brand and said each of said competing brands by said brand monitoring platform comprises normalizing measures corresponding to one or more of said audience score metric parameters and one or more of said engagement score metric parameters respectively, based on a location of each of said identified industries related to said brand and said each of said competing brands, for reducing statistical differences in said measures triggered by a difference of said location of said each of said identified industries related to said brand and said each of said competing brands.

10. The computer implemented method of claim 1, further comprising configuring one or more of said weighted audience score metric parameters and one or more of said weighted engagement score metric parameters for said determination of said audience score and said engagement score respectively, by said brand monitoring platform based on predetermined criteria.

11. The computer implemented method of claim 1, wherein said independent analysis of said acquired social media information related to said brand and said competing brands from said each of said social media sources for said dynamic generation of said categories by said brand monitoring platform comprises determining clusters of similar content portions from said acquired social media information and identifying one or more common categories applicable to said brand and said each of said competing brands in said each of said identified industries from said determined clusters of said similar content portions.

12. The computer implemented method of claim 1, further comprising normalizing measures corresponding to each of said weighted audience score metric parameters by said brand monitoring platform for reducing statistical differences between extreme said measures corresponding to said each of said weighted audience score parameters.

13. The computer implemented method of claim 1, further comprising normalizing measures corresponding to each of said weighted engagement score metric parameters by said brand monitoring platform for reducing statistical differences between extreme said measures corresponding to said each of said weighted engagement score parameters.

14. A computer implemented system for benchmarking a brand based on social media strength of said brand, comprising:

a brand monitoring platform comprising at least one processor configured to execute modules of said brand monitoring platform for monitoring said brand in a virtual social media environment, said modules of said brand monitoring platform comprising;

an information acquisition module that acquires input information on said brand;

an industry identification module that identifies industries related to said brand and competing brands in said identified industries using said acquired input information on said brand;

said information acquisition module that acquires social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in said virtual social media environment via a network;

a category generation module that dynamically generates categories in one or more hierarchical levels in each of said identified industries based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources;

a sorting module that sorts said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels using a sorting interface;

a scoring module that determines an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information;

said scoring module that determines an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information;

said scoring module that generates an aggregate score for said brand and said each of said competing brands using said determined audience score and said determined engagement score; and

said scoring module that determines said social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands based on said aggregate score for said benchmarking of said brand based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment;

whereby said generated aggregate score of said brand and said each of said competing brands benchmarks said brand based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment.

15. The computer implemented system of claim 14, wherein said dynamically generated categories comprise a location of each of said identified industries related to said brand and said each of said competing brands, a location of each of a plurality of authors of said social media information, types of said social media sources utilized by said brand and said each of said competing brands, and marketing elements.

16. The computer implemented system of claim 14, wherein said scoring module generates said aggregate score for said brand and said each of said competing brands by determining a weighted average of said determined audience score and said determined engagement score.

17. The computer implemented system of claim 14, wherein said weighted audience score metric parameters comprise a number of said followers of said brand and said each of said competing brands at said each of said social media sources, a rate of growth of said number of said followers of said brand and said each of said competing brands, a number of recommendations for said brand and said each of said competing brands at said each of said social media sources from each of said followers, a number of references made to said brand and said each of said competing brands at said each of said social media sources by said followers, and aggregate responses to one or more of products, services, and events associated with said brand and said each of said competing brands.

18. The computer implemented system of claim 14, wherein said weighted engagement score metric parameters comprise nature of responses to one or more brand actions of said brand and said each of said competing brands from each of said followers of said brand and said each of said competing brands, a number of brand notification messages, sentiments of said followers towards said brand and said each of said competing brands, a number of fan posts extracted from said acquired social media information, and relevance of said fan posts to said brand and said each of said competing brands.

19. The computer implemented system of claim 14, wherein said scoring module performs:

normalizing measures corresponding to each of said audience score metric parameters;

assigning individual weights to said audience score metric parameters; and

determining a weighted average of said normalized measures corresponding to said each of said audience score metric parameters using said assigned individual weights for said determination of said audience score for said brand and said each of said competing brands.

20. The computer implemented system of claim 14, wherein said scoring module performs:

normalizing measures corresponding to each of said engagement score metric parameters;

assigning individual weights to said engagement score metric parameters; and

determining a weighted average of said normalized measures corresponding to said each of said engagement score metric parameters using said assigned individual weights for said determination of said engagement score for said brand and said each of said competing brands.

21. The computer implemented system of claim 14, wherein said sorting module acquires inputs for said sorting of said acquired social media information related to said brand and said competing brands in said each of said identified industries into said one or more of said dynamically generated categories in said one or more hierarchical levels from a user via said sorting interface.

22. The computer implemented system of claim 14, wherein said scoring module normalizes measures corresponding to one or more of said audience score metric parameters and one or more of said engagement score metric parameters, based on a location of each of said identified industries related to said brand and said each of said competing brands during said determination of said audience score and said engagement score respectively, for said brand and said each of said competing brands, for reducing statistical differences in said measures triggered by a difference of said location of said each of said identified industries related to said brand and said each of said competing brands.

23. The computer implemented system of claim 14, wherein said modules of said brand monitoring platform further comprise a configuration module that configures one or more of said weighted audience score metric parameters and one or more of said weighted engagement score metric parameters for said determination of said audience score and said engagement score respectively, based on predetermined criteria.

24. The computer implemented system of claim 14, wherein said category generation module determines clusters of similar content portions from said acquired social media information and identifies one or more common categories applicable to said brand and said each of said competing brands in said each of said identified industries from said determined clusters of said similar content portions during said independent analysis of said acquired social media information related to said brand and said competing brands from said each of said social media sources for said dynamic generation of said categories.

25. The computer implemented system of claim 14, wherein said scoring module performs:

normalizing measures corresponding to each of said weighted audience score metric parameters for reducing statistical differences between extreme said measures corresponding to said each of said weighted audience score metric parameters; and

normalizing measures corresponding to each of said weighted engagement score metric parameters for reducing statistical differences between extreme said measures corresponding to said each of said weighted engagement score metric parameters.

26. A computer program product comprising a non-transitory computer readable storage medium, said non-transitory computer readable storage medium storing computer program codes comprising instructions executable by at least one processor, said computer program codes comprising:

a first computer program code for acquiring input information on a brand;

a second computer program code for identifying industries related to said brand and competing brands in said identified industries using said acquired input information on said brand;

a third computer program code for acquiring social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in a virtual social media environment via a network;

a fourth computer program code for dynamically generating categories in one or more hierarchical levels in each of said identified industries based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources;

a fifth computer program code for sorting said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels using a sorting interface;

a sixth computer program code for determining an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information;

a seventh computer program code for determining an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information; and

an eighth computer program code for generating an aggregate score for said brand and said each of said competing brands using said determined audience score and said determined engagement score, said eighth computer program code for determining social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands by said brand monitoring platform based on said aggregate score.