Online retailers and advertisers typically attempt to glean information about a potential customer from his or her web browser click history. However, relying on only a potential customer's click history is not an effective means for tailoring and personalizing web content for the user in the moment of customer interaction with the web content. Obtaining information about the user's engagement with web content is critical to successful personalization of web content in real-time. The present invention provides solutions for monitoring, tracking and calculating user engagement with web content that enables successful personalization of web content in the moment of interaction. The features of the present invention improve the customer experience of shopping online for products by providing web content that is personalized and targeted to the customer, as well as providing online retailers with a way of customizing the customer's shopping experience such that prospective customers convert to buyers at a high rate.

Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree

Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold. The server determines, for each different channel type, a distance value between the engagement probability value and the corresponding threshold and communicates with a remote computing device via a channel that is associated with an optimal distance value.

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. The system further enables the brand partners to adjust the content of the smart brand assets based on their needs.

A workflow manager can be configured to present a graphical interface to an entity and display a current status of a workflow via the graphical interface. Via the graphical interface, the entity can provide one or more indications that indicate an entry condition for the workflow, one or more message communications, and one or more filter conditions. The entity can activate the workflow and cause the workflow manager to identify a user population that satisfies the entry condition and cause the user population to be presented with the one or more message communications and the one or more filter conditions of the workflow. Different user segments of the user population may be assigned to different user paths, where each user segment receives different communications based on their corresponding user paths.

A system and method for multi-channel dynamic advertisement testing. The system comprises a multi-platform adaptive ad campaign manager, a dynamic advertisement engine, a campaign database, and an omnichannel text-based communicator. The system receives customer interactions with two advertisement test variants, establishes a real-time media stream between a customer device and a second user device, and monitors the media stream to collect data related to effectiveness of the advertisement variants. The system may analyze media stream data together with a plurality of other data types to statistically determine which of the two advertisement variants resulted in better performance based on a variety of advertisement metrics. The system may use the plurality of data and the statistical analysis to suggest an advertisement element to be altered in the next round of advertisement variant testing. This system can combine data collection and analytics for an ad campaign together into one system.

A charging station for an electric vehicle determines that a first person is in proximity to the charging station and identifies whether the first person is a user of the charging station. In accordance with a determination that the first person is a user of the charging station, the charging station provides, using a first mode for selecting content for users of the charging station, first content for display. The charging station determines that a second person is in proximity to the charging station and identifies whether the second person is a user of the charging station. In accordance with the determination that the second person is not a user of the charging station, the charging station provides, using a second mode for selecting content distinct from the first mode for selecting content, second content for display. The second mode provides content for non-users of the charging station.

Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include identifying content assets including one or more images that are combined to create different digital components distributed to one or more client devices. A quality of each of the one or more images is evaluated using one or more machine learning models trained to evaluate one or more visual aspects that are deemed indicative of visual quality. An aggregate quality for the content assets is determined based, at least in part, on an output of the one or more machine learning models indicating the visual quality of each of the one or more images. A graphical user interface of a first computing device is updated to present a visual indication of the aggregate quality of the content assets.

One or more computing devices, systems, and/or methods for implementing a model for serving exploration traffic are provided. An amount of spend by a content provider to provide content items of the content provider through a content serving platform to client devices of users is determined. A number of exploration impressions of users viewing exploration content items of the content provider over a timespan is determined. A return on exploration impression metric is determined for the content provider based upon a ratio of the amount of spend to the number of exploration impressions. The return on exploration metric is used to rank available exploration content items of content providers for serving exploration traffic.

Methods and apparatus to incorporate saturation effects into marketing mix models are disclosed. An example apparatus includes means for converting adstock data associated with an advertising campaign into effective reached realized (ERR) data based on a first saturation curve, the adstock data corresponding to adstocked gross rating points generated from marketing mix input data. The apparatus further including means for performing regression analysis to: identify the first saturation curve from among a plurality of plausible curves based on a fit of different ones of the plurality of plausible curves to the marketing mix input data, the first saturation curve to define a relationship indicative of saturation effects of the advertising campaign on a target audience of the advertising campaign; and determine an impact of the advertising campaign on sales during a period of interest based on a regression analysis of the ERR data relative to sales data.