In one embodiment, a promotional subsystem generates promotional plans that include promotionals, where each promotional targets one or more pieces of scheduled content. First, the promotional subsystem generates a statistical model based on historical respondent viewership data. The statistical model model maps a respondent viewing of a promotional that targets a piece of scheduled content to a probability of the respondent viewing the piece of scheduled content. Subsequently, the promotional subsystem generates a proposed promotional plan based on the statistical model, a schedule that includes the piece of scheduled content, and a risk tolerance. Advantageously, the promotional subsystem may be configured to generate different proposed promotional plans based on different risk tolerances associated with different viewership growth strategies. Automatically generating proposed promotional plans based on probabilities reduces the time required to identify an acceptable promotional plan compared to current techniques that generate a single promotional plan based on deterministic strategies.

Systems and methods for a configurable response-action engine are provided. Actions are generated for a conversation when an insight is received from a natural language processing system. Industry, segment, client specific instructions, third party data, a state for the lead and lead historical patterns are also received. A decision making action model is tuned using this information. An objective for the conversation may be extracted from the state information for the lead. The tuned model is then applied to the insight and objective to output an action. A response message may be generated for the action. The action is directed to cause a state transition of the lead to a preferred state. In another embodiment, systems and methods are presented for feature extraction from one or more messages. In yet other embodiments, systems and methods for message cadence optimization are provided.

Techniques for using machine learning to configure social media campaigns are disclosed. A social relationship management (SRM) service performs supervised machine learning to generate a learned model, at least by: generating feature vectors based on training data including campaign configuration data and one or more campaign success metrics; and performing pattern recognition on the feature vectors to determine one or more preferred campaign configurations. The SRM service publishes messages to one or more social media platforms and receives user interaction data associated with users' interactions with the messages. The SRM service performs unsupervised machine learning to update the learned model based at least in part on the user interaction data. The SRM service receives a request to configure a social media campaign, applies data associated with the request to the learned model to determine a preferred campaign configuration, and configures the social media campaign based on the preferred campaign configuration.

In general, embodiments of the present invention provide systems, methods and computer readable media configured to use a per-set level optimization of the rank order of promotions to be recommended to a consumer. In some embodiments, machine learning is used offline to generate a predictive diversity model that receives one or more similarity rank features associated with a promotion (e.g., category, price band) as input, and produces an output multiplier to be applied to the promotion's respective associated relevance score (e.g., a relevance score representing a prediction of the promotion's conversion rate without diversity features). At run time, per-set optimization of the ordering of a set of promotions is implemented by adjusting the respective associated relevance scores of the promotions using the diversity model and then re-ordering the set of promotions based on their respective adjusted relevance scores.

Aspects of the subject disclosure may include, for example, a method in which a processing system obtains data regarding location and trajectory of a prospective advertising viewer, and monitoring the activity and physical environment of the viewer. The method also includes predicting a future condition of the viewer, and predicting a time, location and duration of an exposure of the viewer to an advertising display, in order to define an advertising placement window. The method also includes predicting a level of viewer attention to the advertising display while within the placement window. The method further includes predicting an effectiveness of advertising content exposure to the viewer, based on the advertising content and the predicted attention level; offering an ad placement in the advertising placement window to an advertising entity; and placing an advertisement provided by the advertising entity in accordance with the advertising placement window. Other embodiments are disclosed.

In a promotion offering system, a promotion offering may be presented to a consumer on more than one occasion. The previous presentation of the promotion to the consumer may affect a subsequent presentation of the same promotion to the same consumer. The present invention provides an apparatus and method for analyzing the effect a previous presentation of a promotion may have on a consumer when the promotion is presented to the consumer at a subsequent time.

A gig parking system is provided with a mobile platform having a secure access mechanism permitting a trusted parking agent to gain control over the platform. The platform may include a location subsystem to determine the geographic location of the platform and a communications subsystem to transmit verification information including an identification code associated with the parking agent, an identification code associated with the vehicle, and geographic location. A parking software application may permit the selection the destination parking location from a plurality of potential destination parking locations, which may have weighted values with a corresponding reward, and which may be given to either, or both, the platform owner and the parking agent. The reward may be based upon additional factors, such as vehicle starting parking location, distance between starting and destination parking locations, travel time between starting and destination parking locations, time of day, and day of the week.

A dynamically regulated advertising delivery control system. A campaign is operated by sending bids to an exchange responsive to receiving bid requests from the exchange, each bid request representing an opportunity to expose a browser to content. Won bid notifications are received from the exchange and exposure notifications are received from exposed browsers. Failed exposures are detected by detecting won bid notification identifiers without corresponding exposure notification identifiers. Responsive to the failed exposures exceeding an upper limit, the campaign is operated in a throttled mode by sending bids to the exchange in response to a fraction of the suitable bid requests received from the exchange and ignoring some suitable bid requests. Responsive to detecting successful exposures in the throttled mode, the operation of the campaign is dynamically regulated by increasing the fraction.

Systems and methods described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.

Methods, computer-readable media, and devices for auditing digital content to validate that the digital content is authentic, secure, and reaching the intended audience are disclosed. In one example, a method performed by a processing system including at least one processor includes launching a web browser application, wherein the launching includes instantiating a simulated user profile, and wherein the simulated user profile includes a simulated web browsing history, detecting, by the processing system, an item of digital content that is presented to the web browser application in response to the simulated user profile, determining, by the processing system, a relevance of the item of digital content to the simulated user profile, and generating, by the processing system, a report that indicates the relevance of the item of digital content to the user profile.