Media and marketing optimization with cross platform consumer and content intelligence

Abstract

The invention is directed to a computer-implemented method of analyzing video interactions on internet-supported computer platforms, such as online social media platforms, to extract video and audience intelligence, i.e. unique analytics, insights and recommendations for audience engagement optimization, audience engagement, network growth, advertising, and marketing purposes.

1 . A computer-implemented method comprising the following steps:
 generating analytical reporting comprising consumer interests comprising topics, themes, celebrities, media, brands, influencers, actors, movies, television shows, and combinations thereof;   profiling the consumer interests with social media averages for the consumer interests;   calculating a platform specific index report of over- or under-representation of the consumer interests; and   using the consumer interests profiling report to develop or improve a marketing campaign and/or advertising campaign, to develop or improve content creation strategies, or to provide intelligence related to a consumer section in combination with demographic sectors for measuring trends.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising computing platform averages based on social media subscription and interaction behavior, wherein general audience content creation, consumption and engagement patterns are identified on social media platforms and assigned quantitative metrics to reflect a proportion of audiences engaging in or with a topic, keyword, term, object, brand, media, influencer, movie, television show, or combination thereof. 
     
     
         3 . The computer-implemented method of  claim 1 , further comprising defining and assigning baseline scores for terms, key-phrases, entities, content, videos, content creators, media, brands, audience sectors, or topics in a given time period by using algorithmic steps of collection, aggregation, averaging and normalization across an online media platform. 
     
     
         4 . The computer-implemented method of  claim 1 , further comprising categorizing social media platform profiles and channels into categories and subcategories of topics using predictive modeling techniques. 
     
     
         5 . The computer-implemented method of  claim 1 , further comprising categorizing social media platform profiles and channels into categories of media, brand, influencer, entertainer, television show, movie, celebrity, actor, director, chef, politician, retailer, journalist, artist, comedian, news channels, or combination thereof, and sub-categories of the categories including brand name, movie name, movie genre, actor names, famous persons, influencer or entertainer names, or combinations thereof, using natural language processing and a combination of supervised and unsupervised machine learning. 
     
     
         6 . The computer-implemented method of  claim 1 , further comprising combining categories of content and content providers on social media with demographics of audiences interacting with the content and content providers, wherein the combinations calculate an analysis of audience interests, and wherein the demographics comprise age, gender and ethnicity, location, direct marketing area, brand or individual creator status, low-level categorical and topical attributes, high-level categorical and topical attributes, political views, cross-platform account linkages, education level and income range, or combinations thereof. 
     
     
         7 . The computer-implemented method of  claim 1 , further comprising calculating scores predicted for an audience sector and a general audience for social media platforms presented as platform averages, wherein the scores comprise an over-index or under-index factor conveying if the chosen audience segment over-indexes in terms of interest in a media, brand, influencer, actor, television show, movie, or combinations thereof. 
     
     
         8 . The computer-implemented method according to  claim 1 , further comprising algorithmically defining a Return on Investment (ROI) analysis, wherein the algorithmically defining Return on Investment (ROI) analysis comprises automatically scoring for content, audience, content provider, audience engagement types, audience demographics, audience interests, or advertising and marketing platforms, by analyzing trends and engagement in a given time frame, and using the scoring of the ROI analysis to automatically inform or suggest improved content creation, advertising, and marketing strategies. 
     
     
         9 . The computer-implemented method according to  claim 1 , further comprising analyzing post interactions on internet-supported computer platforms to extract intelligence and quantify a performance of social media posts and factors contributing to performance, and outputting predicted performance of audience engagement optimization, network growth, advertising, marketing, or combinations thereof. 
     
     
         10 . The computer-implemented method according to  claim 1 , further comprising calculating an engagement score measuring a likelihood of a trend, video, topic or creator to engage viewership, by measuring content against other posts and target audience from comments, likes, shares, or time engaged with content, and using weighted averaging and normalization for score quantification. 
     
     
         11 . The computer-implemented method according to  claim 1 , further comprising providing a mechanism for quantifying an estimated success of a piece of content, content provider, content publisher, or content marketer, the mechanism comprising extracting numerical representations based on text, images, video intelligence, target audience demographics, and interests, which is then processed to provide tailored content and audience analysis, and wherein the numerical representations are extracted algorithmically using key-phrase/entity extraction and vector formation from text data, applying a variety of filters on image and video data, and dividing audiences into clusters of interest and demographic groups. 
     
     
         12 . The computer-implemented method according to  claim 1 , further comprising assigning numerical scores to an engagement of social media audiences with video content and video creators, including trends, affinities, and performance above or below a baseline. 
     
     
         13 . The computer-implemented method according to  claim 1 , further comprising processing audience consumption behaviors from interactions on social media platforms and calculating recommendations or predictions for media analysts, content creators, direct marketers, or combinations thereof. 
     
     
         14 . The computer-implemented method according to  claim 1 , further comprising identifying and disaggregating audience segments by viewing, commenting, sharing, and other engagement behavior, wherein the audience segments are extracted from groups of undifferentiated social media messages. 
     
     
         15 . The computer-implemented method according to  claim 1 , further comprising measuring popularity, trendiness, or virality of video content themes, including messages, topics, perspectives, sentiments, brands, media, or persona according to computed baselines, topical baselines, sub-topical baselines, or combinations thereof. 
     
     
         16 . The computer-implemented method according to  claim 1 , further comprising scoring attributes of videos and video consumers using data analysis techniques and machine learning on social media content including text, image, audio, video, or combinations thereof. 
     
     
         17 . The computer-implemented method according to  claim 1 , further comprising assigning scores to terms, key-phrases, and entities algorithmically extracted from social media text, image, and video content that are trending in terms of attracting engaged viewership in a given time period compared to a different or previous time period. 
     
     
         18 . The computer-implemented method according to  claim 1 , further comprising defining and assigning a baseline score for terms, key-phrases, entities, content, videos, content creators, audience sectors and topics in a given time period by using algorithmic steps of collection, aggregation, averaging, and normalization. 
     
     
         19 . The computer-implemented method according to  claim 1 , further comprising calculating an affinity score measuring a likelihood that content, topics, and content makers will attract different audience sectors by combining data of audience attributes including age, gender, location, topics of interest, interest groups, or combinations thereof, wherein the measuring is performed algorithmically using text matching and key-phrase extraction techniques and by using a combination of distance metrics, geographical distance, and/or similarity to trending items on social media platforms in a given time period. 
     
     
         20 . A computer-implemented method comprising automatically detecting and recognizing sensitive Personally Identifiable Information (PII) within publicly available text written by a consumer on a social media platform using natural language processing techniques and machine learning algorithms that detect PII presence and recognize a type of PII. 
     
     
         21 . The computer-implemented method of  claim 20 , further comprising automatically removing the Personally Identifiable Information (PII) from storage databases if the PII is detected. 
     
     
         22 . The computer-implemented method of  claim 20 , further comprising using the natural language processing and a combination of supervised and unsupervised machine learning algorithms to detect the Personally Identifiable Information (PII) in the publicly available text written by a consumer on a social media platform. 
     
     
         23 . The computer-implemented method of  claim 20 , further comprising algorithmically detecting a location of the Personally Identifiable Information (PII) in the publicly available text written by a consumer on a social media platform and categorizing the detected PII into categories of PII. 
     
     
         24 . The computer-implemented method according to  claim 20 , further comprising using language and phrases, identified using the natural language processing techniques, that content consumers and content producers use to describe social media profiles, interests, and/or content preferences and comments, and using predictive modeling to detect and recognize email addresses, location information such as geographical coordinates, phone numbers, contact information, street address(es), IP address(es), social security number, bank account details, or combinations thereof. 
     
     
         25 . A computer-implemented method comprising matching social media consumers to location-based profiles based on content consumption and engagement patterns on social media, using predicted demographics and linked audience to automatically connect offline data to online data for marketing or advertising to the social media consumers. 
     
     
         26 . The computer-implemented method according to  claim 25 , further comprising matching profiles from a first social media platform to a second social media platform using predictive modeling using names, comments, typographic patterns, emojis, hyperlink usage, demographics, posting behavior, language, phrases, profile descriptions, content interests, or combinations thereof, identified using natural language processing techniques. 
     
     
         27 . The computer-implemented method according to  claim 25 , further comprising using data from two or more of a consumer, content, engagement, or demographics, automatically gathered from two or more social media platforms, to match a social media profile to a non-social media profile. 
     
     
         28 . The computer-implemented method according to  claim 25 , further comprising matching social media data with additional location-based profile data comprising demographics, location, user first name, user last name, user middle name, income band, education level, household income, number of children, or combinations thereof. 
     
     
         29 . The computer-implemented method according to  claim 25 , further comprising deduplicating social media user matches with location-based profile matches based on calculated accuracy of data and enriched data predictions made on social media data. 
     
     
         30 . The computer-implemented method according to  claim 25 , further comprising automated processing for finding audiences based on a content interest and/or demographic criteria across different social media platforms, matching consumers from social media to a location-based profile dataset based on interests and demographic predictions across social media platforms, performing deduplication algorithmically, converting files to a format compliant or compatible with an identity link store, and automatically uploading the files to the identity link store.