Patent Publication Number: US-11042896-B1

Title: Content influencer scoring system and related methods

Description:
TECHNICAL FIELD 
     The present invention relates to the field of electronics, and more particularly, to the processing of influencer content and related methods. 
     BACKGROUND 
     Sales of a particular product or service may be based upon how well that product or service is marketed to a consumer. One form of marketing is called influencer marketing. Influencer marketing enables a brand, for example, to connect with potential customers. 
     Bloggers, for example, may be considered one type of highly regarded influencer. In many instances, a consumer may trust an influencer, such as a blogger, over other types of content, for example, brand content. An influencer may address the shortcomings of brand content or a typical marketing campaign by using the influencer&#39;s media reach to educate consumers on the given product and/or provide positive feedback about the given product. 
     A blogger, for example, may generate content for more than one brand. A blogger may also provide feedback in the form of a review that may not be helpful to a particular brand or marketing campaign. Even still further, a blogger may have previously blogged about a sensitive topic (e.g., political) unrelated to brand content or a marketing campaign. 
     U.S. Patent Application Publication No. 2016/0034974 to Neb is directed to a system for providing an influencer with an offer to endorse a brand&#39;s product or service. More particularly, Neb discloses selecting an influencer to endorse at least one product or service by an influencer-brand metric. The influencer brand metric includes identifiers to evaluate an influencer&#39;s awareness, appeal and relevance to a brand&#39;s image and their influence on consumer buying behavior. Each identifier may maintain a score to evaluate the influencers appeal and relevance. The influencer-brand metric includes static identifiers and dynamic identifiers, with both defining a score for an influencer suitable for endorsing a brand&#39;s product or service. The static identifiers include one or more of the following identifiers: endorsements completed, active presence on social websites, number of followers/fan/friends, hiring expenditure cost and success rate. The dynamic identifiers include one or more of the following identifiers: location, age, product category match, previous association between brand and influencer and competitive analysis. 
     SUMMARY 
     A content influencer scoring system may include 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 may have influencer historical performance data and legacy influencer content associated therewith. The system may also include 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 and parse the advertisement campaign data for a plurality of advertisement keywords. The remote server may also be configured to 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. The advertisement campaign score may be generated by at least determining whether the content influencer is suitable for the advertisement campaign based upon a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and based upon 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 the historical performance data to generate the advertisement campaign score. 
     The remote server may be configured to determine whether the advertisement campaign score based upon a logistic regression using the historical performance data, for example. The remote server may be configured to determine whether the content influencer is suitable by at least generating a cosine similarity matrix based upon the TF and the DF, for example. 
     The remote server may be configured to, when a content influencer has less than a threshold amount of historical performance data, communicate with a further remote server to determine whether the content influencer is suitable for the advertisement campaign based upon the plurality of advertisement keywords. The remote sever may be configured to determine whether the advertisement campaign score based upon further historical performance data from the further remote server. The further historical performance data may include at least one of social reach and page views, for example. 
     The remote server may be configured to rank the content influencers in order based upon respective advertisement campaign scores. The remote server may be configured to communicate, to the advertiser for display, the content influencers in an order based upon the respective advertisement campaign scores, for example. The historical performance data may include at least one of page-views, social followers, time-on-content, amplification rate, for example. 
     The remote server may be 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. The remote server may be 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. 
     A method aspect is directed to a method of scoring content influencers in a system that includes 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 may have influencer historical performance data and legacy influencer content associated therewith. The method may include 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 and parse the advertisement campaign data for a plurality of advertisement keywords. The method may also include using the remote server to 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. The advertisement campaign score may be generated by at least determining whether the content influencer is suitable for the advertisement campaign based upon a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and based upon a document frequency (DF) of the advertisement keywords across the plurality of documents, and, when the content influencer is determined to be suitable, determining whether the advertisement campaign score based upon the historical performance data to generate the advertisement campaign score. 
     A computer readable medium aspect is directed to a non-transitory computer readable medium for a content influencer scoring system that includes 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 may have influencer historical performance data and legacy influencer content associated therewith. The non-transitory computer readable medium may include 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. The operations may include obtaining advertisement campaign data associated with an advertisement campaign for a given advertiser and parsing the advertisement campaign data for a plurality of advertisement keywords. The operations may also include 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. The advertisement campaign score may be generated by at least determining whether the content influencer is suitable for the advertisement campaign based upon a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content, and based upon 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 the historical performance data to generate the advertisement campaign score. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a content influencer system according to an embodiment. 
         FIG. 2  is a more detailed schematic diagram of the system of  FIG. 1 . 
         FIG. 3  is a display including content influencers ordered based upon respective advertisement campaign scores communicated from the remote server of  FIG. 2   
         FIG. 4  is a flow chart illustrating operation of the remote server of  FIG. 2 . 
         FIG. 5  is a schematic diagram of a content influencer system according to another embodiment. 
         FIG. 6  is a flow chart illustrating operation of the remote server in  FIG. 5 . 
     
    
    
     DETAILED DESCRIPTION 
     The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout, and prime notation is used to indicate similar elements in alternative embodiments. 
     Referring initially to  FIG. 1 , a content influencer scoring system  20  may be particularly advantageous for influencing a purchaser. Such a system  20  may be implemented in various stages, each of which will be described. A first stage, strategy to pursue an advertising campaign, involves an advertiser or brand  21 , such as, for example, a consumer package goods (CPG) brand. The CPG brand  21  executes an advertisement campaign  22  in an execution stage. The advertisement campaign  22  may include one or more content influencers  23  which are chosen based on certain elements in a community engagement stage. Further details of the how content influencers  23  are chosen will be described in further detail below. 
     In a content creation stage, the influencer creates influencer content  24 . In a social amplification stage  25 , the influencer content  24  is sent to users (e.g., purchaser computers  35 ) and shared, for example, by email, social media, etc. A remote server  40  may collect data in a data collection stage, which may provide an application programming interface (API)  26  for content tracking and social listening. Collected data may be stored in an engagement database  27  of the remote server  40 . A campaign dashboard  28  may be provided, for example, to the CPG brand  21  to gauge effectiveness of the advertisement campaign  22 . Further details of the content influencer scoring system  20  including the remote server  40  will be described below. 
     Referring additionally to  FIGS. 2 and 3 , the content influencer scoring system  20  illustratively includes influencer computers  30   a - 30   n . Each influencer computer  30   a - 30   n  includes a memory, a processor coupled to the memory, and an input device, such as, for example, a virtual or physical keyboard. Each content influencer computer may also include a display  34   a - 34   n  coupled to the processor as will be appreciated by those skilled in the art, an influencer computer  30   a - 30   n  may be any of a personal computer (PC), tablet computer, and a smartphone. Of course, each influencer computer  30   a - 30   n  may be another type of computer and may each be a different type. 
     Each influencer computer  30   a - 30   n  is associated with respective influencer content  24  and configured to receive respective influencer content associated with a given product for purchase. As will be appreciated by those skilled in the art, an influencer is termed as such because an influencer typically has a relatively large following and the ability to influence their followers through their content (i.e., influencer content). For example, a relatively strong influencer may be an advocate for the given product for purchase, and that influencer may be more influential to potential buyers of the given product as compared to a static advertisement. It should be appreciated that while a given product for purchase is described herein, a product for purchase may include a service or have a service component. 
     A content influencer  23  may be a blogger, for example, and provide blog posts as the influencer content  24 . The influencer content  24  may also include written content and image content and have certain influencer characteristics associated therewith. Exemplary influencer characteristics may include the type of influencer content  24  (blog, article, email, social media post, etc.), the content type (e.g., recipe, how-to, etc.), and/or the type of product for purchase associated with the influencer content (e.g., food, household item). The influencer characteristics may include other and/or additional characteristics. 
     In one example, the content influencer  23  may be a food blogger and may provide a recipe that uses a Brand-X ingredient. In other examples, the influencer content  24  may be or include a tutorial using the given product for purchase and/or an article about a related item or service to the given product for purchase. Of course, the influencer content  24  may be other and/or additional types of content, for example, social media posts, etc. 
     Each content influencer  23  has influencer historical performance data  43  associated therewith. The historical performance data  43  may be stored in a memory  42  of the remote server  40 . The historical performance data  43  may include any one or more of page-views, social followers, time-on-content, and amplification rate. The historical performance data  43  may be collected based upon legacy influencer content  44  associated with each content influencer  23 . For example, if a given content influencer  23  has previously published influencer content  24  within the system  20  (i.e., legacy influencer content  44 ), the remote server  40  may determine and store data corresponding to the performance of the content influencers. 
     The remote server  40  is coupled to each of the influencer computers  30   a - 30   n , for example, over a communications network, such as, the Internet. The remote server  40  includes a processor  41  coupled to the memory  42 . While functions of the remote server  40  are described herein, it should be appreciated by those skilled in the art that the functions are performed through cooperation of the processor  41  and memory  42 . 
     Referring now additionally to the flowchart  60  in  FIG. 4 , beginning at Block  62 , details of operation of the content influencer scoring system  20  with respect to the remote server  40  and selection of a content influencer  23  for an advertisement campaign  22  will now be described. At Block  64 , the remote server  40  obtains advertisement campaign data  45  associated with the advertisement campaign  22  for a given advertiser  21 . The advertisement campaign data  45  may be obtained as natural language text. For example, the advertiser  21  may provide as input to the remote server  40  a text string along the lines of “I would like a campaign including bloggers for cake.” The remote server  40 , at Block  66 , parses the advertisement campaign data  45  for advertisement keywords, for example, based upon natural language processing. For example, the remote server  40  may parse for nouns, such as, in the above example, “cake.” Other terms or keywords may be generated or parsed, for example, synonyms of one or more keywords (e.g., in the above example, cookies, dessert, sweets, etc.). 
     At Block  68 , the remote server  40  generates a request for influencer content  24  based upon the advertisement campaign data  45 . More particularly, the remote server  40  may communicate an electronic message and/or posting for content influencers  23  to apply to create influencer content  24  for the advertising campaign  22  (i.e., the given product). The remote server  40  accepts applications from interested content influencers  23  for the request for influencer content  24  (Block  70 ). For example, interested content influencers  23  may, by way of respective influencer computers  30   a - 30   n , apply to be a content influencer for the advertising campaign  22 , wherein the applications are communicated from the respective influencer computers to the remote server  40 . 
     The remote server  40  matches content influencers  23  (e.g., content influencers that have made application) to the advertisement campaign data  45  based on the advertisement keywords, and for each content influencer  23 , generates an advertisement campaign score (Blocks  72 ,  74 ). As will be appreciated by those skilled in the art, matching content influencers  23  with a brand or retailer  21  (i.e., an advertising campaign  22 ) typically requires an understanding of a potential content influencer&#39;s interests and whether they have a high probability of performing well relative to other content influencers who have also applied to the request for influencer content for the advertisement campaign. 
     The remote server  40  matches the content influencers  23  to advertisement campaign data  45  by determining whether the content influencer  23  is suitable for the advertisement campaign  22  (Block  72 ). The determination of suitability determines an affinity for particular brands, products, and lifestyle themes, for example, and based upon the natural language processing text analysis. To determine suitability, the remote server  40  applies a term frequency (TF) of the advertisement keywords for each of a plurality of documents (e.g., blog posts) from the legacy influencer content  44 . The TF measures the frequency of the words or terms in any given document. 
     Suitability is also determined based upon a document frequency (DF) of the advertisement keywords across the documents. The DF, which may also be referred to as an inverse document frequency (IDF), measures the uniqueness of a word or term. In other words, the IDF measures how infrequently the word or term occurs across all the documents (e.g., all blog posts by the given content influencer  23 ). 
     The remote server  40  multiplies the TF by the IDF. The product of the TF and IDF for a given keyword or term provides a relative measure of how frequent that keyword is used in the document multiplied by how unique the keyword is in the entire corpus of all documents (i.e., collection of documents or influencer content). This measure advantageously may provide a relative importance of the keyword in the document compared to other documents. In particular, the measure may be determined by:
 
 W   x,y   =tf   x,y ×log( N/df   x )
 
wherein tf x,y  is the frequency of the term x within document y, df x  is the number of documents that include the term or keyword, and N is the total number of documents.
 
     The remote server  40 , also as part of the suitability determination, generates a cosine similarity matrix based upon the TF and IDF. More particularly, the TF-IDF (i.e., product) information is converted into a similarity matrix. The cosine similarity between two vectors (or two documents on the vector space) is a measure that calculated the cosine of the angle between the terms present in them. The cosine similarity matrix uses a matrix of 0s and 1s which measures the angular distance of words to match affinities between prior advertising campaigns and content influencers. 
     When the content influencer  23  is determined to be suitable at Block  72 , the remote server  40  determines whether the content influencer is capable for the advertisement campaign  22  based upon the historical performance data  43  to generate the advertisement campaign score. More particularly, the remote server  40  determines capability based upon a logistic regression using the historical performance data  43 . Using a logistic regression advantageously may provide interoperability of coefficients, operational efficacy, and increased speed as compared to other classification models, as will be appreciated by those skilled in the art. The following equation represents the logistic regression: 
               P   ⁡     (   X   )       =     1     1   +     e     -     (     α   +       β   1     ⁢     X   1       +       β   2     ⁢     X   2       +       β   3     ⁢     X   3         )                   
wherein P(X) is the dependent variable or outcome, β 1 x 1  is the product of the first feature and first weight, β 2 x 2  is the product of the second feature and second weight and so on. Capability may be considered a measure of performance of a given content influencer  23  relative to other content influencers (e.g., in-network).
 
     The advertisement campaign score for a given content influencer  23  is defined by P(X). Example results for P(X) are 0.98, 0.75, and 0.63. The P(X) results may be normalized to a 0-100 scale. The advertisement campaign score represents the probability that the corresponding content influencer  23  will perform in the top half of all advertisement campaign participants. For example, if a given content influencer  23  has a score or P(X) of 0.63, they would have a 63% probability of being in the top half of any given advertising campaign. As another example, if a given content influencer  23  had a score of 0.98, they have a 98% probability of being in a top half of any given campaign. 
     Exemplary features or types of historical performance data  43  for input into the logistic regression may include page-views, a number of social followers, an amplification rate, and amount of time spent by users on the influencer content. Content influencer membership and engagement may also be considered as inputs to the logistic regression, for example, application rate, percent of denied applications, and length of time as a content influencer  23  with the remote server  40  (i.e., in-network). Potential audience or users may also be used as inputs to the logistic regression, for example, network centrality metrics, location, gender, and/or age. Other features may include a history of the content influencer&#39;s cost per post, cost per view, and cost per engagement, content influencer&#39;s bid for the advertising campaign, content attributes (e.g., photography sharpness, focus, and depth of field, and blog layout/design), and prior engagement history (e.g., ability to follow instructions, timeliness, and typographical errors). Of course, any number of input and types of inputs may be provided to the logistic regression. 
     However, it may be desirable to reduce a relatively large number of inputs or features to the logistic regression, for example, for increased processing speed. Accordingly, the features or inputs may be reduced to three features: mean performance percentile, mean competitive rank, and the square root of the number of home page views of the content influencer. 
     Once the advertisement campaign scores are generated for each content influencer  23  by the remote server  40 , the remote server ranks the content influencers based upon respective advertisement campaign scores, for example, with the content influencer having the highest score first in a list (Block  80 ). At Block  82 , the remote sever  40  communicates, to the advertiser  21  for display on a display  46 , for example, of an advertiser computer, the content influencers  23  in order based upon the respective advertisement campaign scores  51 . A star  52  next to a content influencer&#39;s advertisement campaign score  51  is indicative of an affinity to the advertiser by either or both of category and brand. A reach  53  is also communicated for display, for example, social media reach on respective social media platforms. Notes  54 , both client and content manager, for each content influencer  23  may also be displayed along with a current round of selection  55 . 
     At Block  84 , the remote server  40  permits the advertiser  21  to selectively accept/approve or deny the accepted applications from the content influencers  23  that have accepted applications. The status  56  may be displayed. The process ends at Block  86 . 
     As will be appreciated by those skilled in the art, the content influencer scoring system  20  advantageously may provide increased matching or connection of brands and retailers with content influencers that have a relatively loyal social following. In other words, the system  20  may provide more consistent and higher quality of matching relatively to conventional techniques. Conventional matching approaches for selection of content influencers  23  vary widely with respect to quality, and the highly accurate matching (i.e., quality) cannot by typically replicated, for example, using an automated routine and machine learning as in the present system  20 . 
     Referring now to  FIG. 5  and the flowchart  60 ′ in  FIG. 6 , in another embodiment, the remote server  40 ′ may communicate with a further remote server  47 ′ or third party server when there may not be enough historical performance data  43 ′ to make an advertisement campaign score determination. More particularly, at Block  71 ′, the remote server  40 ′ determines whether a content influencer has less than a threshold amount of historical performance data  43 ′ (e.g., a new content influencer), and if so, the remote server communicates with the further remote server  47 ′ to determine whether the content influencer  23 ′ is suitable for the advertisement campaign  22 ′ based upon the advertisement keywords (Block  76 ′). Using the advertisement keywords as input, the remote server  40 ′ queries the further remote server  47 ′ for further historical performance data  48 ′ (i.e., out-of-network historical performance data) for suitable content influencers  23 ′. The further historical performance data  48 ′ may include social reach and/or page views. The remote server  40 ′ then determines whether the content influencer  23 ′ is capable (Block  78 ′) based upon the further historical performance data. The capability determination may be determined by calculating the advertisement campaign score for that particular content influencer  23 ′ based upon the following equation:
 
Score= w   1   +w   2 *sqrt(social reach)+ w   3 *sqrt(homepage views)
 
wherein w x  are respective weights.
 
     The remote server  40 ′ may rank the out-of-network content influencer with the other content influencers (in-network) and communicate all the content influencers  23 ′ to the advertiser  21 ′ for display. 
     If at Block  71 ′, the content influencer  23 ′ has enough legacy influencer content, the remote server  40 ′ matches the content influencers to the advertisement campaign data  45 ′ as described above with respect to the embodiments in  FIGS. 1-4 . Other steps illustrated but not specifically described in the present embodiment are similar to those described above with the respect to the embodiments in  FIGS. 1-4  and need no further discussion herein. 
     A method aspect is directed to a method of scoring content influencers  23  in a system  20  that includes influencer computers  30   a - 30   n  each associated with a respective content influencer and configured to receive respective influencer content  24  associated with a given product for purchase. Each content influencer  23  may have influencer historical performance data  43  and legacy influencer content  44  associated therewith. 
     The method includes include using a remote server  40  coupled to each of the plurality of influencer computers  30   a - 30   n  to obtain advertisement campaign data  45  associated with an advertisement campaign  22  for a given advertiser  21  and parse the advertisement campaign data for advertisement keywords. The method also includes using the remote server  40  to match content influencers  23  to the advertisement campaign data  45  based on the plurality of advertisement keywords, and, for each content influencer, generate an advertisement campaign score. The advertisement campaign score is generated by at least determining whether the content influencer is suitable for the advertisement campaign  22  based upon a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content  44 , and based upon 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 the historical performance data. 
     A computer readable medium aspect is directed to a non-transitory computer readable medium for a content influencer scoring system  20  that includes influencer computers  30   a - 30   n  each associated with a respective content influencer  23  and configured to receive respective influencer content  24  associated with a given product for purchase. Each content influencer  23  may have influencer historical performance data  43  and legacy influencer content  44  associated therewith. The non-transitory computer readable medium includes computer executable instructions that when executed by a processor  41  of a remote server  40  coupled to each of the plurality of influencer computers  30   a - 30   n  causes the processor to perform operations. The operations include obtaining advertisement campaign data  45  associated with an advertisement campaign  22  for a given advertiser  21  and parsing the advertisement campaign data for a plurality of advertisement keywords. The operations also include matching content influencers  23  to the advertisement campaign data  45  based on the plurality of advertisement keywords, and, for each content influencer, generate an advertisement campaign score. The advertisement campaign score is generated by at least determining whether the content influencer  23  is suitable for the advertisement campaign  22  based upon a term frequency (TF) of the advertisement keywords for each of a plurality of documents from the legacy influencer content  44 , and based upon a document frequency (DF) of the advertisement keywords across the plurality of documents, and, when the content influencer is determined to be suitable, determining whether the advertisement campaign score based upon the historical performance data  43 . 
     Many modifications and other embodiments of the invention will come to the mind of one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is understood that the invention is not to be limited to the specific embodiments disclosed, and that modifications and embodiments are intended to be included within the scope of the appended claims.