The present invention is directed to machine-learning based methods and systems related to dynamically inserting items multimedia content into media broadcasts. By using machine-learning based models, the performance of different items of multimedia content with different audiences can be automatically simulated, resulting in recommendations for where, when and how to optimally distribute those items of multimedia content. The multimedia content can be distributed by dynamically integrating that multimedia content into a streaming video feed. The reaction of an audience to the multimedia content is then automatically monitored, collected, and analyzed using machine-learning techniques, allowing the reaction of the audience to the multimedia content to be automatically determined. This reaction can then be input back into the machine-learning based simulator, further refining future predictions for the performance of items of multimedia content with audiences.

Methods and systems for improved and efficient exemplary campaign message generation are disclosed. By automating the exemplary campaign message generation process, the system can improve the selection efficiency and quality of these messages, which can be used to inspire prospective campaign generators. Furthermore, the selected exemplary messages are dynamically diversified to meet the needs of different campaign generators.

Methods and systems for improved and efficient campaign message classification are disclosed. By automating the campaign message classification process, the system can improve efficiency in categorizing and managing campaign messages. The system can predict a message's type or characteristics via an ensemble model that comprises one or more logic-rule model(s) and machine learning language model(s). The ensemble model can process various data and predict a message's type or characteristics based on an aggregated prediction mechanism.

Apparatuses, methods, and systems for determining winning arms of electronic testing. One method includes obtaining historical data related to the testing, creating a histogram based on the historical data, the histogram including bins and weights, creating a distribution by computing concentration parameters of the distribution from the weights of the histogram, executing the testing, receiving new data collected based on the execution of the test, allocating the new data into same bins as the bins of the histogram of the historical data yielding a new data bin count, computing a posterior distribution comprising updating the distribution using the same bins and the new data bin counts and the concentration parameters of the distribution in closed form vie conjugate prior formulae, inferring corresponding central tendencies of samplings of a metric distribution, constructing an overall utility distribution for each arms of the test, and determining a winning arm of the testing.

A system and method for performing cross-platform data analytics of advertising campaign information. The system comprises a data sanitizing module for receiving information related to at least one campaign from a plurality of advertising platforms and to produce a normalized dataset having data values that comply with a unified format; a storage and transformation (TS) engine for transforming data values in the normalized dataset into a format defined in a relaxed data schema, thereby resulting with a relaxed dataset, the TS engine is further configured to analyze the relaxed dataset to compute a plurality of campaign measurements of measurable data values included in the relaxed dataset; a data-mart module for storing the relaxed dataset together with the computed campaign measurements; and a management user interface (UI) module for allowing allow client devices access to data stored in the data-mart module, wherein the data-mart module is optimized for providing an accelerated data for data stored therein.

A system for geographic, temporal, and location-based detection and analysis of mobile communication devices in a communication network is disclosed. The system comprises a database and a server communicatively coupled to the database that receives location data from a vendor server, sets a boundary around each location included in the location data, receives messages from mobile communication devices responsive to display of content on the mobile communication devices, analyzes mobile communication device data corresponding to determined mobile communication identifiers from cell sites, and applies a plurality of exclusions to the mobile communication devices based on the analysis to exclude mobile communication devices based on dwell times within the boundary, historical device patterns within the boundary, and/or dwell times patterns in relation to one or more geographic features in the boundary. The server then generates and displays a list of non-excluded mobile communication devices based on the plurality of exclusions applied.

A method that incorporates teachings of the present disclosure may include, for example, transmitting media content to a group of set top boxes for presentation with an overlay superimposed onto the media content, where the overlay can include a timeline corresponding to the presentation of the media content and receiving a first comment from a first set top box of the group of set top boxes, where the first comment is presentable with the overlay and the media content by the group of set top boxes. Additional steps can include receiving a tag generated at a first set top box during presentation of the media content, presenting the tag while the media content is presented, correlating the tag to a portion of the media content timeline and analyzing metadata associated with the portion of the timeline that is tagged to determine a first marketing parameter. Other embodiments are disclosed.

Provided in various aspects, are systems and methods for organizing and delivering commodity information to a consumer in an easily understood user interface. In some embodiments, the user interface is specially configured to present a matrix of information to the consumer, blending branded and non-branded opportunities. Branded opportunities can be configured to present commodity information in association with a specific provider or the provider's brand. Combining branded opportunities with unbranded opportunities enables commodity providers to target brand loyal customers while at the same time providing discounted opportunities without diluting brand associations. The matrix based organization can be implemented in conjunction with commodity data feed providers, including for example, rental car information suppliers to deliver commodity information in an easily understood user interface. Matrix displays can organize branded and non-branded offers while at the same time minimizing the data over-load such a volume of offers conventionally presents.

Methods and systems for matching user information between data sets, while preserving data privacy are provided. Some examples relate to matching subsets of users from a first device to subsets of users from a second device, based on a first set of indications and a second set of indications, respectively, to calculate how long it takes for a user to travel to a location of interest, after the user is provided with directed content. A conversion rate may be determined based on how many instances at which a user travels to the location of interest, within a conversion window, after the user is provided with the directed content. The conversion rate may be compared to a baseline conversion rate to determine a change in conversion rate. The change in conversion rate may correspond to an impact of the directed content in causing the user to travel to the location of interest. User data is not shared from the first device to the second device, and vice-versa.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing conversion path performance measures and reports. In one aspect, user interaction data are obtained, were the user interaction data specify user interactions for a plurality of conversions. User interactions that are associated with each conversion are selected from the user interaction data, where the associated user interactions for each conversion are user interactions with a converting user during the conversion cycle for the conversion. Using the user interaction data for the selected user interactions, a quantity of user interactions that are associated with each conversion and occurred during the conversion cycle for the conversion are determined. In turn, conversion path performance measures are computed and reports specifying the conversion path performance measures are generated.