Content influencer scoring system and related methods

1. A content influencer scoring system comprising:

a plurality of influencer computers each associated with a respective content influencer and configured to receive respective influencer content associated with a given product for purchase, each content influencer having influencer historical performance data and legacy influencer content associated therewith; and

a remote server coupled to each of the plurality of influencer computers and configured to

obtain advertisement campaign data associated with an advertisement campaign for a given advertiser,

parse the advertisement campaign data for a plurality of advertisement keywords, and

match content influencers to the advertisement campaign data based on the plurality of advertisement keywords, and for each content influencer, generate an advertisement campaign score by at least

determining whether an amount of influencer historical performance data for the content influencer exceeds a threshold, and when so

determining whether the content influencer is suitable for the advertisement campaign based upon a product of a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and a document frequency (DF) of the advertisement keywords across the plurality of documents, and

when the content influencer is determined to be suitable, determining the advertisement campaign score based upon a logistic regression using the historical performance data so that the advertisement campaign score represents a probability the content influencer is top ranked for the advertisement campaign relative to other content influencers and so that the remote server processes the historical performance data with increased processing speed, and

when the amount of influencer historical performance data for the content influencer does not exceed the threshold

communicate the plurality of advertisement keywords to a further remote server,

obtain further historical performance data from the remote server, the further historical performance data comprising social reach and page view data, and

generate the advertisement campaign score based upon the social reach and page view data.

2. The content influencer scoring system of claim 1 wherein the remote server is configured to determine whether the content influencer is suitable for the advertisement campaign based upon converting the product of the TF and DF into a similarity matrix.

3. The content influencer scoring system of claim 1 wherein the remote server is configured to determine whether the content influencer is suitable for the advertisement campaign based upon determining a cosine similarity between given documents of the plurality thereof.

4. The content influencer scoring system of claim 3 wherein the remote server is configured to generate a cosine similarity matrix based upon the cosine similarly, the cosine similarity matrix being a measure between advertisement keywords to match prior advertisement campaigns and to the content influencer.

5. The content influencer scoring system of claim 1 wherein the remote server is configured to rank the content influencers in order based upon respective advertisement campaign scores.

6. The content influencer scoring system of claim 5 wherein the remote server is configured to communicate, to the advertiser for display, the content influencers in an order based upon the respective advertisement campaign scores.

7. The content influencer scoring system of claim 1 wherein the historical performance data comprises at least one of page-views, social followers, time-on-content, and amplification rate.

8. The content influencer scoring system of claim 1 wherein the remote server is configured to

generate a request for influencer content based upon the advertisement campaign data;

accept applications from the plurality of content influencers for the request for influencer content; and

permit the advertiser to selectively accept or deny accepted applications from ones of the plurality of content influencers that have accepted applications.

9. The content influencer scoring system of claim 1 wherein the remote server is configured to obtain the advertisement campaign data as natural language text, and parse the advertisement campaign data for the plurality of advertisement keywords based upon natural language processing.

10. A remote server for a content influencer scoring system comprising a plurality of influencer computers each associated with a respective content influencer and configured to receive respective influencer content associated with a given product for purchase, each content influencer having influencer historical performance data and legacy influencer content associated therewith, the remote server coupled to each of the plurality of influencer computers and comprising:

a memory; and

a processor coupled to the memory and configured to

obtain advertisement campaign data associated with an advertisement campaign for a given advertiser,

parse the advertisement campaign data for a plurality of advertisement keywords, and

match content influencers to the advertisement campaign data based on the plurality of advertisement keywords, and for each content influencer, generate an advertisement campaign score by at least

determining whether an amount of influencer historical performance data for the content influencer exceeds a threshold, and when so

determining whether the content influencer is suitable for the advertisement campaign based upon a product of a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and a document frequency (DF) of the advertisement keywords across the plurality of documents, and

when the content influencer is determined to be suitable, determining the advertisement campaign score based upon a logistic regression using the historical performance data so that the advertisement campaign score represents a probability the content influencer is top ranked for the advertisement campaign relative to other content influencers and so that the processor processes the historical performance data with increased processing speed, and

when the amount of influencer historical performance data for the content influencer does not exceed the threshold

communicate the plurality of advertisement keywords to a further remote server,

obtain further historical performance data from the remote server, the further historical performance data comprising social reach and page view data, and

generate the advertisement campaign score based upon the social reach and page view data.

11. The remote server of claim 10 wherein the processor is configured to determine whether the content influencer is suitable for the advertisement campaign based upon converting the product of the TF and DF into a similarity matrix.

12. The remote server of claim 10 wherein the processor is configured to determine whether the content influencer is suitable for the advertisement campaign based upon determining a cosine similarity between given documents of the plurality thereof.

13. The remote server of claim 10 wherein the processor is configured to obtain the advertisement campaign data as natural language text, and parse the advertisement campaign data for the plurality of advertisement keywords based upon natural language processing.

14. A method of scoring content influencers in a system comprising a plurality of influencer computers each associated with a respective content influencer and configured to receive respective influencer content associated with a given product for purchase, each content influencer having influencer historical performance data and legacy influencer content associated therewith, the method comprising:

using a remote server coupled to each of the plurality of influencer computers to

obtain advertisement campaign data associated with an advertisement campaign for a given advertiser,

parse the advertisement campaign data for a plurality of advertisement keywords, and

match content influencers to the advertisement campaign data based on the plurality of advertisement keywords, and, for each content influencer, generate an advertisement campaign score by at least

determining whether an amount of influencer historical performance data for the content influencer exceeds a threshold, and when so

determining whether the content influencer is suitable for the advertisement campaign based upon a product of a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and a document frequency (DF) of the advertisement keywords across the plurality of documents, and

when the content influencer is determined to be suitable, determining the advertisement campaign score based upon a logistic regression using the historical performance data so that the advertisement campaign score represents a probability the content influencer is top ranked for the advertisement campaign relative to other content influencers and so that the remote server processes the historical performance data as input to the logistic regression with increased processing speed, and

when the amount of influencer historical performance data for the content influencer does not exceed the threshold

communicate the plurality of advertisement keywords to a further remote server,

obtain further historical performance data from the remote server, the further historical performance data comprising social reach and page view data, and

generate the advertisement campaign score based upon the social reach and page view data.

15. The method of claim 14 wherein using the remote server comprises using the remote server to determine whether the content influencer is suitable for the advertisement campaign based upon converting the product of the TF and DF into a similarity matrix.

16. The method of claim 14 wherein using the remote server comprises using the remote server to determine whether the content influencer is suitable for the advertisement campaign based upon determining a cosine similarity between given documents of the plurality thereof.

17. The method of claim 14 wherein using the remote server comprises using the remote server to obtain the advertisement campaign data as natural language text, and parse the advertisement campaign data for the plurality of advertisement keywords based upon natural language processing.

18. A non-transitory computer readable medium for a content influencer scoring system comprising a plurality of influencer computers each associated with a respective content influencer and configured to receive respective influencer content associated with a given product for purchase, each content influencer having influencer historical performance data and legacy influencer content associated therewith, the non-transitory computer readable medium comprising computer executable instructions that when executed by a processor of a remote server coupled to each of the plurality of influencer computers causes the processor to perform operations comprising:

obtaining advertisement campaign data associated with an advertisement campaign for a given advertiser;

parsing the advertisement campaign data for a plurality of advertisement keywords; and

matching content influencers to the advertisement campaign data based on the plurality of advertisement keywords, and for each content influencer, generating an advertisement campaign score by at least

determining whether an amount of influencer historical performance data for the content influencer exceeds a threshold, and when so

determining whether the content influencer is suitable for the advertisement campaign based upon a product of a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and a document frequency (DF) of the advertisement keywords across the plurality of documents, and

when the content influencer is determined to be suitable, determining the advertisement campaign score based upon a logistic regression using the historical performance data so that the advertisement campaign score represents a probability the content influencer is top ranked for the advertisement campaign relative to other content influencers and so that the processor processes the logistic regression with increased processing speed, and

when the amount of influencer historical performance data for the content influencer does not exceed the threshold

communicate the plurality of advertisement keywords to a further remote server,

obtain further historical performance data from the remote server, the further historical performance data comprising social reach and page view data, and

generate the advertisement campaign score based upon the social reach and page view data.

19. The non-transitory computer readable medium of claim 18 wherein the operations comprise determining whether the content influencer is suitable for the advertisement campaign based upon converting the product of the TF and DF into a similarity matrix.

20. The non-transitory computer readable medium of claim 18 wherein the operations comprise obtaining the advertisement campaign data as natural language text, and parse the advertisement campaign data for the plurality of advertisement keywords based upon natural language processing.