Embodiments disclosed herein select a content message to present to a user on a page of an application based on paralinguistic features of audio input received from the user for the application. The audio input is received via a microphone associated with a computing device. A feature extractor extracts paralinguistic features from the audio input. A predictive model determines a label indicating a measure of receptiveness to product placement (e.g., a predicted marketing outcome) based on the paralinguistic features. A content-selection component selects a content message to present to the user based on the label and based on a profile of the user.

A digital brand asset system is provided enabling a brand owner to create, distribute, maintain, manage, merchandise and analyze smart brand assets. The system enables distribution and sharing of smart brand assets across the websites. The websites can host webpages containing codes representing the smart brand assets. When a user device retrieves a webpage from one of the websites and renders the webpage, it executes the codes and requests the content of the smart brand assets from a brand asset server. Through the brand asset server, a brand owner can control the content and the presentation of the smart brand asset hosted by the websites, based on various factors such as previous click through rates, aggregated shopper behaviors, geographical locations of the websites or website visitors, categorized types of websites, blacklist of websites.

A digital brand asset system is provided enabling a brand owner to create, distribute, maintain, manage, merchandise and analyze smart brand assets. Generally, the system enables distribution and sharing of smart brand assets across websites. The system performs the steps of presenting a web page containing code representing a smart brand asset that has a unique identifier, receiving a request for the smart brand asset from a search engine crawler which is indexing web pages of the web server, redirecting the request to a brand asset proxy server based on the unique identifier and satisfying the request by providing content of the smart brand asset. The unique identifier can include information of the location, user attributes, or the content of the smart brand asset. As a result, it is determined that the request is sufficiently satisfied to be indexed by the search engine.

The present disclosure is directed to associating computing devices with each other based on computer network activity for selection of content items as part of an online content item placement campaign. A first linking factor is identified based on a connection between a first device and the computer network via a first IP address during a first time period, and based on a connection between a second device and the computer network via the first IP address during the first time period. A number of devices that connect with the computer network via the first IP address is determined. A positive match probability is generated. A second and third linking factors are monitored. A negative match probability is determined based on the second and third linking factors. The first device is linked with the second device based on the positive and negative match probabilities.

A media management system that handles a plurality of deals for a plurality of advertisers and a plurality of promotional campaigns, receives input and/or parameters for each of the plurality of deals that corresponds to an upfront inventory utilization type and commercial operator break (COB) inventory utilization type, of a plurality of inventory utilization types. Reserve inventory units for each of the plurality of promotional campaigns that corresponds to a promotion inventory utilization type of the plurality of inventory utilization types, are determined for a specified upcoming time-frame. Inventory units from a defined amount of inventory units are dynamically allocated among each inventory utilization types of the plurality of inventory utilization types to meet a plurality of defined parameters for the defined amount of inventory units for one or more specified durations until end of the specified upcoming time-frame.

A method, apparatus, and computer program product are disclosed to improve generation of electronic communications. The method may provide a plurality of content slots each configured to receive content, the content comprising at least one of promotion content or non-promotion content. The method may also include maintaining a database comprising a plurality of promotion content generators and non-promotion content generators, and determining, using a processor, one of the plurality of promotion content generators or non-promotion content generators for respectively supplying corresponding promotion content or non-promotion content to each of the plurality of content slots. The determining the one of the plurality of promotion content generators or non-promotion content generators may include determining selection parameters, and scoring the plurality of promotion content generators and non-promotion content generators based at least in part on the selection parameters.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

A computer-implemented method that includes presenting an available interaction opportunity based on a geolocation of an interaction platform and receiving proposals from one or more users for the available interaction opportunity at the geolocation. The method includes determining a successful proposal from the proposals received by the one or more users and displaying a stored interaction associated with a profile of a user of the one or more users having the successful proposal at the geolocation and on the interaction platform.

Techniques for integrating third-party analytics with virtual-assistant enabled applications are disclosed. A third-party analytics service trains a machine learning model, using labeled training data including (a) phrases corresponding to sales offers made to consumers and (b) sales conversion outcomes associated with the phrases. The service receives, from a consumer-facing application, a user query submitted via a virtual assistant interface. The service applies the user query to the machine learning model, to obtain a recommended phrase for the consumer-facing application to use in response to the user query. The recommended phrase is: based on one or more of the phrases used to train the machine learning model; responsive to the user query; and based on a likelihood of achieving a sales objective associated with the consumer-facing application. The service transmits the recommended phrase to the consumer-facing application, to use when supplying a response to user query via the virtual assistant interface.

In an approach to providing a notification based on lack of search results, one or more computer processors monitor one or more queries for content in an online marketplace. One or more computer processors determine that a result of the one or more queries is content is not found. One or more computer processors determine that a history of the result of the one or more queries for the content exceeds a threshold for content not found. One or more computer processors send a notification of the history.