Systems and methods for predicting an outcome for a marketing asset. The systems and methods comprise first determining by a computing device, for a marketing asset received by the computing device, at least one asset type of the marketing asset. The marketing asset is parsed, by the computing device, into a plurality of segmented components, based on the determined at least one asset type. For at least one of the plurality of segmented components, at least one discrete marketing message conveyed by the marketing asset is determined, by applying at least one of a predictive model or rules to the at least one segmented component, where the at least one predictive model or rules are stored in a memory associated with the computing device. The method further includes determining, for each of the at least one discrete marketing message, at least one associated score. The method yet further includes inputting the determined at least one associated score to a trained predictive model to obtain a predicted marketing outcome, where the trained predictive model was trained using training scores associated with a multitude of training discrete marketing messages as independent variables and corresponding training marketing outcome data as dependent variables.

Universal tags are placed on various web pages of a website. Unlike prior art action tags that indicate that an action has occurred, the universal tag sends a broader range of data capturing a user's experience on the website (e.g., whether an item was placed in a shopping cart, etc.) to a server. The server obtains an identity of the user from its own cookie on the user's device and stores the information received with the universal tag in connection with the user's account. A graphical user interface is used to define the information to be sent upon activation of the universal tag.

An information processing apparatus includes a control unit configured to execute the processing of obtaining specific information, from a vehicle having an advertisement display unit, which is to be used to distribute an advertisement to be displayed by the advertisement display unit on the outside of the vehicle, selecting a plurality of vehicles based on the specific information, and controlling the plurality of vehicles to cause their advertisement display units to display the advertisement cooperatively.

According to an implementation, a method for digital information retrieval in a social media platform includes transmitting, over a network, information to render a timeline of social content for a user of a client application. The timeline of social content includes messages posted on the messaging platform by user accounts that are connected to a user account of the user in a connection graph. The method includes computing, using a machine-learning algorithm inputted with relevance signals, a relevance level between the user account of the user and a user account not linked to the user account of the user in the connection graph, and transmitting information about a profile of the user to a computing device associated with the user account not linked to the user account of the user in response to the relevance level being greater than a threshold level.

A processor collecting advertisement (ad) events of a user using a mobile device, such as a mobile phone or eyewear, parsing the ad events from the mobile device, and generating an ad receptivity profile on the granularity of a user identification (ID) and an hour of day. In one example, the processor computes the percentage of ad time watched by the individual user, such as on an hourly basis, and by monitoring a click-through rate (CTR) of the respective user as a measure for user ad receptivity. The processor adjusts an ad allocation/ad load on a per user basis according the user level ad receptivity profile, resulting in dynamically providing ads on the mobile device display when a user is active and receptive viewing the ads.

A processor having a performance engine tracking user engagement of advertisement (ad) events using a mobile device, such as a mobile phone or eyewear, to generate a user level ad receptivity profile. In one example, the processor tracks both the percentage of ad time watched by the individual user, such as on an hourly basis, and ad engagement such as by monitoring a click-through rate (CTR) of the respective user as a measure for user ad receptivity. The processor downloads data from the performance engine to a server processor, and the server processor adjusts an ad allocation/ad load on a per user basis according the user level ad receptivity profile. The server processor dynamically provides ads on the mobile device display when a user is active and receptive viewing the ads.

Systems and methods for generating an optimal allocation of marketing investment for a marketing budget based on a marketing variable without requiring historical time-series data or survey data are disclosed. A first advertising elasticity is determined for the marketing variable based on a meta-analysis of a normative database. A second advertising elasticity is determined based on financial data for the offering being analyzed. The first and second advertising elasticities are combined to determine the optimal allocation.

Systems, devices, media, instructions, and methods are provided for presentation of media collections with automated interactive advertising. In one embodiment, a client device receives content elements for display as part of a content collection. Advertising data is also received for display between selected content elements. Interaction elements are merged with the create an advertising element. During display of the advertising data, the interaction elements are presented on the client device output, and are controllable via user inputs. In various embodiments, interaction data recorded at the device is used to manage the presentation of future advertising data.

A digital advertising system includes at least one processor configured to execute a plurality of functional modules including an analytics module to receive and analyze client attributes associated with a website visitor and a requested website to define an analytics event. The analytics module ingests and enriches data within the analytics event and provides it to a machine learning module that generates prediction models for potential bids. A management platform receives the bidding prediction and generates candidate configs. An optimization module receives the candidate configs and applies weights and additional features to select a config and generate an optimized script for the selected config. A deployment module receives the optimized script and delivers the script to the website visitor.

A portable billboard is presented including a portable media projection subsystem to selectively project media and to supply an enablement signal in response to the media being projected. A location device supplies a geographic location of the media projection subsystem. A verifier receives the enablement signal and the geographic location, and supplies verification information responsive to the media being projected from a stationary location for a predetermined minimum duration of time. A communications subsystem receives verification information and either stores the information for subsequent downloads, or transmits the information to a central controlling server. A targeting subsystem permits an entity to select a target stationary location from a plurality of value weighted target stationary locations. The targeting application typically provides a reward in response to a value of the selected target stationary location.