Social commerce return on investment

• US 10,360,582 B2
• Filed: 01/09/2013
• Issued: 07/23/2019
• Est. Priority Date: 01/09/2013
• Status: Active Grant

• Current Assignee: Dell Products LP (Dell Technologies Inc.)
• Original Assignee: Dell Products LP (Dell Technologies Inc.)
• Inventors: Gupta, Munish, Narang, Rajiv, Ehindero, Akintunde A., Bruce, Jennifer W., Tariq, Ahmed
• Primary Examiner(s): Sittner, Matthew T
• Application Number:
  US13/737,492
• Publication Number:
  US 20140195333A1
• Time in Patent Office: 2,386 Days
• Field of Search: 709204, 709224
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A system and method are disclosed for analyzing return on investment (ROI) for social commerce interactions. User data associated with a target group of social media users is collected and processed to generate a first set of social commerce metrics. The user data comprises a first set of social media interaction data corresponding to a first set of social commerce interactions with the target group of social media users. The user data is further processed to generate a second set of social commerce interactions, which are then performed. The performance of the second set of social commerce interactions generates a second set of social commerce interaction data, which in turn is processed to generate a second set of social commerce metrics. The first and second sets of social commerce metrics are then processed to generate social commerce ROI metrics.

Claims

1. A computer-implemented method for analyzing return on investment for social commerce interactions, comprising:

processing a set of user data associated with a set of social media users to generate a first set of social commerce metrics, the set of user data comprising a first set of social media interaction data corresponding to a first set of social commerce interactions with the set of social media users;

processing the set of user data and the first set of social media interaction data to generate a second set of social commerce interactions;

performing the second set of social commerce interactions with the set of social media users to generate a second set of social commerce interaction data;

processing the second set of social commerce interaction data to generate a second set of social commerce metrics; and

processing the first and second sets of social commerce metrics to generate a set of return on investment metrics corresponding to the second set of social commerce interactions; and

wherein

the set of user data is processed to generate a social presence map and a prioritization index, the prioritization index comprising a purchase index and a visit index, the purchase index indicating product line revenue, number of purchases and a size of each purchase by a social media user, the visit index indicating activity with content related to a product line of the social media user;

the first set of social media interaction data is processed to generate a predictive model;

the social presence map and the predictive model are processed to generate the first set of social commerce metrics; and

,

the prioritization index is processed when generating the second set of social commerce interactions.

2. The method of claim 1, wherein the second set of social commerce interactions comprises:

automated marketing operations;

sales lead nurturing operations; and

sales execution processes.

3. The method of claim 1, wherein the social presence map and the predictive model are further processed to identify triggers to initiate the second set of social commerce interactions.

4. The method of claim 1, wherein the predictive model is used to generate the sales leads.

5. The method of claim 1, wherein the set of user data comprises:

a set of active listening data;

a set of received media data;

a set of marketing and sales history data;

a set of social network behavior data;

a set of firmographic data;

a set of purchase history data; and

a set of vendor site visit footprint data.

6. The method of claim 1, wherein:

the purchase index is calculated as;

Purchase Index=R*PV*SOW

where;

R=Revenue generated in the past, PV=closeness to realizing a purchase and SOW=Share of Wallet.

7. The method of claim 6, wherein:

the closeness to realizing a purchase is calculated as;

8. The method of claim 1, wherein:

the visit index is calculated as;

Visit Index=Σ

(MV*W_website)

where;

MW=momentum of visit and Wwebsite=Weight of website.

9. The method of claim 8, wherein:

the momentum of visit is calculated as;

Momentum of Visit (MV)=Σ

Visits*Recency

where;

Visits=Number of Visits

Recency=Recency of the Visit [1/(gap between the visit week and the last week considered in the visit window)] and,

the weight of the website is calculated as;

i. W_A=1, W_B=1, W_VENDOR=0.5.

10. 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 and comprising instructions executable by the processor and configured for;

processing a set of user data associated with a set of social media users to generate a first set of social commerce metrics, the set of user data comprising a first set of social media interaction data corresponding to a first set of social commerce interactions with the set of social media users;

processing the set of user data and the first set of social media interaction data to generate a second set of social commerce interactions;

performing the second set of social commerce interactions with the set of social media users to generate a second set of social commerce interaction data;

processing the second set of social commerce interaction data to generate a second set of social commerce metrics; and

processing the first and second sets of social commerce metrics to generate a set of return on investment metrics corresponding to the second set of social commerce interactions; and

wherein

the set of user data is processed to generate a social presence map and a prioritization index, the prioritization index comprising a purchase index and a visit index, the purchase index indicating product line revenue, number of purchases and a size of each purchase by a social media user, the visit index indicating activity with content related to a product line of the social media user;

the first set of social media interaction data is processed to generate a predictive model;

the social presence map and the predictive model are processed to generate the first set of social commerce metrics; and

,

the prioritization index is processed when generating the second set of social commerce interactions.

11. The system of claim 10, wherein the second set of social media interactions comprises:

automated marketing operations;

sales lead nurturing operations; and

sales execution processes.

12. The system of claim 10, wherein the social presence map and the predictive model are further processed to identify triggers to initiate the second set of social commerce interactions.

13. The system of claim 10, wherein the predictive model is used to generate the sales leads.

14. The system of claim 10, wherein the set of user data comprises:

a set of active listening data;

a set of received media data;

a set of marketing and sales history data;

a set of social network behavior data;

a set of firmographic data;

a set of purchase history data; and

a set of vendor site visit footprint data.

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

processing a set of user data associated with a set of social media users to generate a first set of social commerce metrics, the set of user data comprising a first set of social media interaction data corresponding to a first set of social commerce interactions with the set of social media users;

processing the set of user data and the first set of social media interaction data to generate a second set of social commerce interactions;

performing the second set of social commerce interactions with the set of social media users to generate a second set of social commerce interaction data;

processing the second set of social commerce interaction data to generate a second set of social commerce metrics; and

processing the first and second sets of social commerce metrics to generate a set of return on investment metrics corresponding to the second set of social commerce interactions; and

wherein

the set of user data is processed to generate a social presence map and a prioritization index, the prioritization index comprising a purchase index and a visit index, the purchase index indicating product line revenue, number of purchases and a size of each purchase by a social media user, the visit index indicating activity with content related to a product line of the social media user;

the first set of social media interaction data is processed to generate a predictive model;

the social presence map and the predictive model are processed to generate the first set of social commerce metrics; and

,

the prioritization index is processed when generating the second set of social commerce interactions.

16. The non-transitory computer usable medium of claim 15, wherein the second set of social media interactions comprises:

automated marketing operations;

sales lead nurturing operations; and

sales execution processes.

17. The non-transitory computer usable medium of claim 15, wherein the social presence map and the predictive model are further processed to identify triggers to initiate the second set of social commerce interactions.

18. The non-transitory computer usable medium of claim 15, wherein the predictive model is used to generate the sales leads.

19. The non-transitory computer usable medium of claim 15, wherein the set of user data comprises:

a set of active listening data;

a set of received media data;

a set of marketing and sales history data;

a set of social network behavior data;

a set of firmographic data;

a set of purchase history data; and

a set of vendor site visit footprint data.