Method and Apparatus to Derive Product-Level Competitive Insights in Real-Time Using Social Media Analytics

• US 20140207560A1
• Filed: 03/21/2014
• Published: 07/24/2014
• Est. Priority Date: 02/15/2011
• Status: Abandoned Application

• Current Assignee: Dell Products LP (Dell Technologies Inc.)
• Original Assignee: Dell Products LP (Dell Technologies Inc.)
• Inventors: Dandekar, Shree A., Gupta, Munish, Daruvalla, Keisha
• Application Number:
  US14/221,371
• Publication Number:
  US 20140207560A1
• Time in Patent Office: Days
• US Class Current: 705/14.41
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A method and system are disclosed for providing near-real-time competitive insights associated with user interactions within a social media environment. A first and second set of social media data, respectively associated with a first and second set of social media interactions, are processed to generate a first and second set of social network advocacy (SNA) data in near-real-time. The resulting first and second sets of SNA data are then processed to generate a first and second set of competitive insight data, which respectively indicate a near-real-time measurement of sentiment and advocacy related to various aspects of a first and second product. The first and second sets of social pricing index data are then processed to generate a set of competitive insight differential data, which indicates a corresponding improvement or decline in sentiment or advocacy related to various aspects of the first and second products.

Claims

1. A computer-implementable method for providing near-real-time competitive insights associated with user interactions within a social media environment, comprising:

processing a first set of social media data to generate a first set of SNA data in near-real-time, the first set of social media data associated with a first set of user interactions within a social media environment corresponding to a first product;

processing the first set of SNA data to generate a first set of SNA Pulse (SNAP) metric data; and

processing the first set of SNAP metric data to generate a first set of competitive insight data corresponding to the first product.

2. The method of claim 1, further comprising:

processing a second set of social media data to generate a second set of SNA data in near-real-time, the second set of social media data associated with a second set of user interactions within a social media environment corresponding to a second product;

processing the second set of SNA data to generate a second set of SNAP metric data;

processing the second set of SNAP metric data to generate a second set of competitive insight data corresponding to the second product; and

processing the first and second sets of competitive insight data to generate a set of competitive insight differential data.

3. The method of claim 2, wherein:

the first set of competitive insight data is processed to generate a first aggregate competitive insight value; and

the second set of competitive insight data is processed to generate a second aggregate competitive insight value.

4. The method of claim 3, wherein the first and second sets of competitive insight data are processed to generate an aggregate competitive insight differential value.

5. The method of claim 4, wherein the first and second sets of competitive insight data respectively correspond to a set of product aspects comprising at least one member of the set of:

a product feature;

a product capability;

a product performance metric;

a product'"'"'s pricing;

a product'"'"'s quality;

a product'"'"'s purchase experience;

a product'"'"'s delivery experience; and

a product'"'"'s associated customer service.

6. The method of claim 5, wherein a predetermined weighting factor is applied to individual members of the first and second sets of competitive insight data to generate a first and second set of weighted competitive insight data.

7. A system comprising:

a processor;

a data bus coupled to the processor; and

a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations for near-real-time competitive insights associated with user interactions within a social media environment and comprising instructions executable by the processor and configured for;

processing a first set of social media data to generate a first set of SNA data in near-real-time, the first set of social media data associated with a first set of user interactions within a social media environment corresponding to a first product;

processing the first set of SNA data to generate a first set of SNA Pulse (SNAP) metric data; and

processing the first set of SNAP metric data to generate a first set of competitive insight data corresponding to the first product.

8. The system of claim 7, further comprising:

processing a second set of social media data to generate a second set of SNA data in near-real-time, the second set of social media data associated with a second set of user interactions within a social media environment corresponding to a second product;

processing the second set of SNA data to generate a second set of SNAP metric data;

processing the second set of SNAP metric data to generate a second set of competitive insight data corresponding to the second product; and

processing the first and second sets of competitive insight data to generate a set of competitive insight differential data.

9. The system of claim 8, wherein:

the first set of competitive insight data is processed to generate a first aggregate competitive insight value; and

the second set of competitive insight data is processed to generate a second aggregate competitive insight value.

10. The system of claim 9, wherein the first and second sets of competitive insight data are processed to generate an aggregate competitive insight differential value.

11. The system of claim 10, wherein the first and second sets of competitive insight data respectively correspond to a set of product aspects comprising at least one member of the set of:

a product feature;

a product capability;

a product performance metric;

a product'"'"'s pricing;

a product'"'"'s quality;

a product'"'"'s purchase experience;

a product'"'"'s delivery experience; and

a product'"'"'s associated customer service.

12. The system of claim 11, wherein a predetermined weighting factor is applied to individual members of the first and second sets of competitive insight data to generate a first and second set of weighted competitive insight data.

13. A non-transitory, computer-readable medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

processing a first set of social media data to generate a first set of SNA data in near-real-time, the first set of social media data associated with a first set of user interactions within a social media environment corresponding to a first product;

processing the first set of SNA data to generate a first set of SNA Pulse (SNAP) metric data; and

processing the first set of SNAP metric data to generate a first set of competitive insight data corresponding to the first product.

14. The non-transitory, computer-readable medium of claim 13, further comprising:

processing a second set of social media data to generate a second set of SNA data in near-real-time, the second set of social media data associated with a second set of user interactions within a social media environment corresponding to a second product;

processing the second set of SNA data to generate a second set of SNAP metric data;

processing the second set of SNAP metric data to generate a second set of competitive insight data corresponding to the second product; and

processing the first and second sets of competitive insight data to generate a set of competitive insight differential data.

15. The non-transitory, computer-readable medium of claim 14, wherein:

the first set of competitive insight data is processed to generate a first aggregate competitive insight value; and

the second set of competitive insight data is processed to generate a second aggregate competitive insight value.

16. The non-transitory, computer-readable medium of claim 15, wherein the first and second sets of competitive insight data are processed to generate an aggregate competitive insight differential value.

17. The non-transitory, computer-readable medium of claim 16, wherein the first and second sets of competitive insight data respectively correspond to a set of product aspects comprising at least one member of the set of:

a product feature;

a product capability;

a product performance metric;

a product'"'"'s pricing;

a product'"'"'s quality;

a product'"'"'s purchase experience;

a product'"'"'s delivery experience; and

a product'"'"'s associated customer service.

18. The non-transitory, computer-readable medium of claim 17, wherein a predetermined weighting factor is applied to individual members of the first and second sets of competitive insight data to generate a first and second set of weighted competitive insight data.

19. The non-transitory, computer-readable medium of claim 13, wherein the computer executable instructions are deployable to a client computer from a server at a remote location.

20. The non-transitory, computer-readable medium of claim 13, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.