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.

Systems and methods are provided herein for determining personalized timing for generating for display advertisements to users. Rather than an expert determining time segments of a media asset most suitable for presenting advertisements to users, the most suitable time segments in a media asset for presenting advertisements to users may be customized based on a user's profile information and/or the user's level of engagement in a media asset. The media guidance application may parse a media asset into multiple time segments and determine one or more time segments associated with metadata that matches content characteristics preferred by the user. One or more advertisements may be presented to the user in these time segments determined by the media guidance application instead of the time segments determined by the expert.

In some aspects, a system for enhancing performance of directed information delivery is provided. In one example, an advertising recommendation server receives an advertisement request including an advertisement for display on at least one of a plurality of webpages and a requested performance of the advertisement. The server extracts context data from the advertisement, and provides the extracted context data to a data model useable to generate predictions of performance of the advertisement when displayed on each of a plurality of webpages, trained based on performance data associated with previous displays of one or more previous advertisements on one or more of the plurality of webpages and context data from the one or more previous advertisements. A webpage is identified that meets expected performance, and a recommendation is provided to a decision platform.

A control apparatus includes a communication interface configured to transmit, to at least one bidding apparatus, range data indicating a geographical range through which at least one vehicle travels, and receive, from the at least one bidding apparatus, bidding data for bidding on an advertising slot associated with the geographical range indicated by the range data, and a controller configured to determine a bidder to whom the advertising slot is to be sold based on the bidding data received by the communication interface.

Systems and methods for creating advertisement campaigns are provided. In one embodiments, an advertisement planning program comprises a creating module enabling a user to create an advertisement to be displayed within an advertisement area on each of one or more media components, such as a ticket that entitles a bearer of the respective ticket to enter a place, travel by public transport, participate in an event, or enter a lottery or raffle. The advertisement planning program also comprises a run time module configured to enable the user to enter time periods during which the advertisement is initially displayed. A location selection module is configured to enable the user to select one or more remote locations where the advertisement is to be initially displayed. The advertisement creating module enables the user to electronically insert and spatially arrange graphical and/or textual elements in a window representing a predetermined size and shape of the advertisement area.

Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

Embodiments of system are disclosed in which selection strategies for a direct marketing campaign that identify consumers from a credit bureau or other consumer database can be planned, tested, and/or refined on a stable subset of the credit database. In some embodiments, once refined, consumer selection criteria may be used to execute the direct marketing campaign on the full consumer/credit database, which is preferably updated approximately twice weekly. In one preferred embodiment, the data for the test database represents a random sampling of approximately 10% of the full database and the sampling is regenerated approximately weekly in order to provide a stable set of data on which campaign developers may test their campaign. For each consumer in the sampling, the environment may allow a client to access and use both attributes calculated by the credit bureau and proprietary attributes and data owned by the client. The system allows for a plurality of clients to use the system substantially simultaneously while protecting the privacy and integrity of the client's proprietary data and results.

A promotion server may generate the promotion campaigns based on input from a merchant. The input may include a request to generate the promotion campaign, a merchant preference, or other information shared between the merchant and the promotion server. The promotion campaign may include promotions, such as coupons, discounts, or the like, to encourage transactions with a merchant. The promotions may be generated based on merchant specified criteria, a merchant transaction history, customer preferences, a customer transaction history, and/or other information processed by the promotion server. The promotions of the promotion campaign may be distributed via one or more channels, such as electronic mail, website publication, receipts, etc. Each promotion of the promotion campaign may be linked to a particular customer, thereby limiting the number of times a particular customer can take advantage of the promotion campaign.

A method and system for allocating displays to a plurality of stores grouped into a plurality of store configurations is presented. It includes receiving a plurality of counts, that specifies spaces available in the store configuration for one of a plurality of types of displays, a ranking of a plurality of displays, wherein the ranking is based on a benefit of each of the plurality of displays for the store configuration, and when the plurality of displays are to be allocated to the plurality of stores; determining a subset of the plurality of displays that meet one or more conditions related to at least one of a forecasted sell through or forecasted margin of the plurality of displays for the store, allocating at least some of the displays based on the ranking of the displays, and generating an indication of the allocated displays for output to a user.

A system and method for dynamically tracking and capturing content and displaying the content on public or semi-public non-personal digital displays. In exemplary embodiments, the content may include an urgent notification that is displayed within a slot of looped content with the manner in which the urgent notification is displayed depending on the level of urgency and attributes of the non-personal digital displays.