A segmentation system utilizes a supervised learning method and a clustering analysis to identify clusters, thereby segmenting a population into groups, where the clusters are associated with various conversion potentials that indicate the probability of an event. The segmentation system employs the supervised learning method to train a model on training data comprising historical conversion data and features associated with members of the group. A subset of features is selected from a ranked order that is determined using weights generated by the supervised learning. A clustering analysis is performed for a population with respect to the subset to generate clusters. A superior cluster is identified based on it having a conversion potential greater than a conversion potential of another cluster. In a marketing context, the system can be employed to identify a superior cluster of users that have a higher conversion potential in response to an advertisement campaign.

A method for billboards in a data management platform (DMP) includes: receiving an indication from a user during an interaction between the user and an agent that the user would like to hear an offer; determining at least one offer to play to the customer; determining statistics regarding at least one of the characteristics, features, or aspects of the at least one offer that customers have indicated as liking or have acted on; determining measurements for KPIs regarding at least one of the interaction or the at least one offer, and other metrics; generating one or more billboards using the statistics and the measurements; and announcing the one or more billboards to one or more of clients, contact centers, or customers. The billboards comprise at least one of the characteristics, features, or aspects of the at least one offer that customers have indicated as liking or have acted on.

The present disclosure provides operations for determining an effectiveness of an advertisement. The operations may include determining a first rate of engagement with a product, service, or message by a target group that includes one or more subjects who had an opportunity to view an advertisement for the product, service, or message at a specified location during a first time period. The operations may further include determining a second rate of engagement with the product, service, or message by a control group that includes one or more subjects present at the specified location during a second time period disjoint from the first time period and who did not have an opportunity to view the advertisement for the product, service, or message. The operations may further include determining the effectiveness of the advertisement using the first rate of engagement and the second rate of engagement.

Methods and systems for classifying tabular data include clustering columns from one or more input tables into column groups. The column groups are processed using a neural network that has a set of input layers, each input layer accepting a respective one column group from the column groups as input, to generate a classification output. A classification task is performed on the one or more input tables using the classification output.

A system and methods are disclosed for creating and using a revenue value index to score impressions and users for purposes of determining fair pricing for advertisement (ad) placement or to provide analytics and information to publishers for future use. The revenue value index may be configured to evaluate data obtained or acquired from first-party, publisher platforms and/or third-party platforms or other data sources for the purpose of valuing either ad impressions or the users engaging the websites on which ads are likely to be placed. The revenue value index may be utilized for either providing value information to entities (for example, publishers) or for measuring the value of ad impressions or users for use in determining fair pricing in real-time auctions conducted for ad placement.

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.

A system and method for the presentation of advertisements is present. According to one embodiment, a number of impressions of an advertisement message are presented over a computer network such as the Internet to a variety of viewer computers. Depending on the actions taken by the viewers (e.g., whether the Viewer selects the advertising message and accesses a web-link to the advertiser's web-site), bonus exposure (e.g., an additional number of impressions provided to the viewers) of the advertising message is given.

A system and method for selecting portions of content, and media content sets to be delivered over a packet based network is provided. The selected portions of content (such as a web page) may be keywords such as a word or a group of words. The media content sets may include one or more of video content (that may include an audio portion), audio content, graphics, images, or other multimedia or single media content. The media content set may include advertising, informational, business, educational, governmental, institutional, customized (e.g., personalized) content and/or other content. According to an example embodiment, the present invention provides a heuristic model employing a piece-wise linear optimization function to select keywords and the media content sets for delivery by a media delivery provider.

A system and method of advertising for use on an internet and/or digital networking capable device, wherein the system allows advertisers to use a loading space generated during the initiation of a process on the device to post any media and/or advertising content during the time between when a program or web page is requested and when it actually loads.

This specification describes technologies relating to content presentation. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving ad information from a seller; generating, using one or more processors, a single conversion ad using the received ad information, where the single conversion ad has an available inventory of one such that only a single conversion of the ad can be performed; transmitting the single conversion ad to one or more potential buyers; receive an input from one of the one or more potential buyers; and notifying the seller of the user input. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.