This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

The subject disclosure relates to employing a computer-implemented method that sources, by a system operatively coupled to a processor, a set of personalized data comprising at least one of biometric data, statistical data, or contextual data. The method also includes determining, by the system, predictive relationships based on an evaluation of the set of personalized data. In another aspect, the method includes generating, by the system, a personal dynamic decision grid comprising a set of decision data coupled to a set of scores based on the predictive relationships, wherein the set of scores represent a probability of performing respective decisions of the set of decisions.

A system is disclosed for presenting advertisements for products and related products for a consumer based on the products being purchased.

An apparatus for providing customized advertisements includes a database that stores a plurality of electronic advertisements, receives biometric information of a client from at least one biometric device of the client, and receives receptivity information of the client responding to the plurality of electronic advertisements, as well as a processor that accesses the database, and maps the biometric information and the receptivity information and analyzes the mapped information to generate customized marketing data. The processor also calculates a receptivity probability for each of the plurality of electronic advertisements based on the customized marketing data by using current biometric state of the client, selects an electronic advertisement from the plurality of electronic advertisements based on the calculated receptivity probabilities, and outputs to the client the selected electronic advertisement.

Machine learning techniques to optimize user interface template selection are provided. In one technique, a first set of feature values pertaining to a first entity is identified. Multiple sets of feature values are also identified, each set of feature values pertaining to a different user interface (UI) template for rendering content items on a computer screen. For each set of feature values of the multiple sets, the set of feature values and the first set of feature values are inserted into a machine-learned model to generate a score, which is added to a set of scores, which set of scores is initially empty. Based on the set of scores, a particular UI template is selected for a content item. The content item is transmitted over a computer network to be presented on a screen of a computing device of the first entity according to the particular UI template.

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.

Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

A system for selecting one or more advertisement items to be presented to a user may include a computing device configured to obtain anchor item data including anchor item title data and anchor item metadata identifying characteristics of an anchor item. The computing device can also obtain advertisement item data including advertisement item title data and advertisement item metadata identifying characteristics of a plurality of advertisement items. The computing device can also determine a match score for each advertisement item of the plurality of advertisement items based on the anchor item data and the advertisement item data, wherein the match score identifies a relevance of the advertisement item to the anchor item. The computing device can then select one or more advertisement items based on the match score of each advertisement item.

Methods and systems for managing advertisements in social networks are provided. An example method commences with receiving, from a user, a publishing time and a social network post to be published in one or more accounts of the user in one or more social networks. The method includes, prior to publishing the social network post, associating the social network post with a rule for enabling advertisement of content. The rule is based partially on statistics to be associated with the social network post. The method includes, after publishing the social network post in the one or more social networks at the publishing time, continuously monitoring the statistics associated with the published social network post to determine, based on the statistics, that the rule is satisfied. In response to the determination that the rule is satisfied, the advertisement of content is enabled in the one or more social networks.