Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.

A campaign profile specifies products and/or content items associated with a campaign. A target group selection engine applies an affinity attribute model to user information of a user. The affinity attribute model is used to determine the user's affinity towards (a) product attributes of the products associated with the campaign and/or (b) content attributes of the content items associated with the campaign. The affinity attribute model may be generated using machine learning. A user interface accepts target user tuning parameters that specify weights to be applied to the affinity attributes determined by the affinity attribute model. Based at least on applying the weights to the affinity attributes, an inclusion score and/or exclusion score for the user is determined. The user is included in a target group, for engaging with the campaign, based on the inclusion score and/or exclusion score.

Systems and methods are described for a causal marketing attribution process that includes the receiving of a plurality of marketing events associated with a customer and computing a sum of a plurality of channel-specific terms corresponding to the plurality of marketing events, wherein each of the plurality of channel-specific terms comprises a channel-specific base parameter and a channel-specific decay parameter. Additionally, the causal marketing attribution process computes a sum of a plurality of interaction terms, wherein each interaction term comprises a product of a pair of channel-specific terms, and determines a probability of a target outcome for the customer based on the sum of the plurality of channel-specific terms and the sum of the plurality of interaction terms.

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 predictive marketing includes: receiving information that an event is expected to happen or is happening; predicting which attributes of customers will be associated with calls or chats directed to the event; predicting which customers will contact a contact center based on the predicted attributes; and taking action based on the predictions. A system for predictive marketing includes: an intelligent database for storing interaction data of customers; and a data management platform (DMP) configured to receive customer data from the intelligent database, analyze the customer data, determine which of the customers will be affected by an event, determine how the customers will be affected by the event, and take action. A predictive marketing platform includes: data sources; and a marketing and ad platform configured to receive data from the data sources, make predictions based on the data; and take action based on the predictions.

Multitask learning is applied to predict a customer's propensity to purchase an item within a particular category of items. Then, the network is tuned using transfer learning for a specific promotional campaign. Retail revenue and promotional revenue are jointly optimized, conditioned on customer trust. Accordingly, a particular promotional program may be selected that is specific to the user.

A system determines a constraint associated with a pending deal for an advertiser based on target cost per thousand (CPM) reduction goal, demographics CPM cap, or established parameter. Rates are generated for each selling title for a week for a duration and a network of the pending deal. In certain time period for first channel of first network, buckets are determined based on sum of program attributes and time attributes for each second channel and weighing factor. Target audience rating estimates are acquired based on a predictive model, the buckets, the target CPM reduction goal, and the demographics CPM cap for plurality of networks. First proposal information is generated based on first distribution information of an audience spot and modified target CPM of a proposal associated with the pending deal based on target audience rating estimates. Audience spot is scheduled across the network for a selling title and week combination.

Targeting information (also referred to as ad serving constraints) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

An ad is placed in a movie, by analyzing inherent characteristics of the movie, analyzing viewed characteristics of the movie, analyzing viewer characteristics of a viewer of the movie, obtaining advertiser preferences for placement of the ad in the movie, determining costs of placing the ad in the movie based on the inherent characteristics of the movie, the viewed characteristics of the movie, the viewer characteristics and the advertiser preferences, and placing the ad in the movie in accordance with the inherent characteristics of the movie, the viewed characteristics of the movie, the viewer characteristics, the advertiser preferences and the determined costs.

Methods and systems for handling online requests based on information known to a service provider. One method may comprise: obtaining first information, the first information relating to an online request made using a communication apparatus; using a logical identifier assigned to the communication apparatus to obtain second information, the second information pertaining to a profile associated with the logical identifier, comparing the first information to the second information; and performing an action related to handling of the online request based on a result of the comparing.