Artificial intelligence identification of early adopter audiences for marketing campaigns

A method for authorizing execution of targeted marketing campaigns for streaming media intellectual property (IP) audio and video assets, the method comprising:

• streaming, by media asset consumers, media IP assets distributed to digital service providers (DSPs) via a media asset distributor, for which the consumers have obtained streaming access through subscription or agreement to other terms of use from the DSPs;
• interacting through viewing, creating, commenting, and rating on a computing device, by social media users, with user generated short-form video content (UGC) on social media platforms, wherein the user generated short-form video content may incorporate a plurality of media IP assets;
• documenting and compiling context-specific audience behaviors relating to the streaming and the UGC interactions, including a combination of, but not limited to, viewing, streaming, liking, adding to collection, skipping, and inferred ways of initiating a stream or view event based on a context of the stream or view event within a DSP or social media client, of the consumers or the social media users respectively using computing devices that receive the context-specific audience behaviors as input locally, and record and send the context-specific audience behaviors to remote DSP and social media platform computing databases that synthesize the context-specific audience behaviors into trend data reports, wherein items in the trend data reports comprise specific media IP assets;
• implementing a marketing analytics pipeline by:
• receiving or retrieving the trend data reports from the DSPs and the social media platforms, wherein the trend data reports are received or retrieved using a reporting tool API (application programming interface) that provides an interface between said DSPs and the social media platforms and a consumption database controlled by the media asset distributor,
• compiling records of the context-specific audience behaviors relating to the streaming and the UGC interactions from the trend data reports into geodemographic groups including geographic regions, genders, ages and DSP subscription status separated into active and passive engagement contexts and without identification of individual consumers for both the DSPs and the social media platforms,
• creating a ranked list of one or more media IP assets and corresponding marketing actions based on the context-specific audience behaviors on the DSPs and the social media platforms,
• retrieving links on the DSPs for the one or more media IP assets in the ranked list of one or more media IP assets, the links being retrieved by use of an API of an associated DSP and/or a smartlink API,
• creating for each media IP asset in the ranked list of one or more media IP assets, using the smartlink API, one or more landing pages comprising associated links on the DSPs, and
• creating marketing campaigns for media IP asset managers/owners including the one or more landing pages comprising the associated links on the DSPs for each of the one or more media IP assets in the ranked list;
• presenting the ranked list of one or more media IP assets and corresponding marketing actions and the marketing campaigns to the media IP asset managers/owners in a graphical user interface (GUI), wherein the GUI includes a media IP asset and a corresponding listing of marketing campaigns, and wherein the GUI enables the media IP asset managers/owners to manage funds, set territory-specific budgets and upload, store and select audio and visual promotional advertisement material to use as creative and copy for the marketing campaigns;
• receiving modifications of the funds, the territory specific budgets and the audio and visual promotional advertisement material made via the GUI by the media IP asset managers/owners;
• and
• receiving authorization through the GUI to execute the marketing campaigns from the media IP asset managers/owners,
• wherein the marketing analytics pipeline is further implemented by:
• a marketing action analytics hub receiving growth and re-engagement opportunities from an opportunity detection analytics hub or from a marketing platform directly and identifying high growth potential audiences using predictive models of engagement;
• and
• the marketing platform interacting with the marketing action analytics hub and creating targeted marketing campaigns based around the high growth potential audiences, the marketing platform further suggesting marketing actions to the media IP asset managers/owners, wherein the marketing actions are designed for delivery to the high growth potential audiences on marketing channels via the targeted marketing campaigns;
• wherein the marketing action analytics hub is comprised of an offline model generation component and an online audience generation component and uses a targeting method based on media IP asset geodemographic group data compiled from the UGC interactions of individual consumers and associated interests for generating descriptions of the high growth potential audiences;
• wherein the offline model generation component is configured to use the compiled media IP asset geodemographic group data contained in the consumption database to fit statistical models based on consumption patterns of distributed media IP assets on a regular interval by assigning the media IP assets into categories in an asset category clustering component and employing both a cold start audience generation component and an alternating least squares matrix factorization component to generate category-specific high growth potential audiences and context-specific high growth potential audiences through a module selection component;
• wherein the cold start audience generation component is comprised of a category growth audiences generation subcomponent which is configured to receive a tensor {tilde over (G)}ik:=,b,rxi,tk where xi is a first media IP asset in a set of media IP assets mc for a category c and tk is a date in a date range that has been processed by a change detection component over the date range where denotes a level and b a trend which arises from modeling daily counts of overall DSP streams as noisy measurements of a latent multivariate state vector from the change detection component and r is an indicator feature, along with a tensor Vijk which denotes an aggregated consumption of an i-th media IP asset from a geodemographic group j on a k-th date and to generate category-specific cold start audiences through a plurality of audience generation methods including an early adopter generation method that uses an early adopter date range function which takes the tensor {tilde over (G)}ik as input, groups continuous dates tk into negative, neutral, and positive regions to generate a set of growth events and then ranks the geodemographic groups in the tensor Vijk according to a binary vote system where votes are determined by an early adopter metric that transforms a geodemographic group consumption time series for the geodemographic group j and a global consumption time series through smoothing and scaling to form consumption curves for the geodemographic group j and the global consumption time series respectively, where a geodemographic group is voted yes and included in a category-specific early adopter audience if there is a positive signed area from any of subregions i with max(Ai)>0 where Ai is a signed area between the geodemographic group and global consumption curves and within an i-th subregion of an early adopter date range defined to be a date range τeai between a start of a growth event and an end date of an i-th constituent positive region of the growth event.