A content influencer scoring system may include influencer computers each associated with a respective content influencer having influencer historical performance data and legacy influencer content associated therewith. A remote server may obtain advertisement campaign data associated with an advertisement campaign and parse the advertisement campaign data for advertisement keywords. The remote server may match content influencers to the advertisement campaign data based on the advertisement keywords and, for each content influencer, generate an advertisement campaign score. The score may be generated by determining whether the content influencer is suitable for the advertisement campaign based upon a term frequency of the advertisement keywords for each document from the legacy influencer content, and frequency of the advertisement keywords across the documents, and when suitable, determining whether the advertisement campaign score based upon the historical performance data to generate the advertisement campaign score.

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.

Methods, systems, and media for estimating the causal effect of different content exposure levels are provided.

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.

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.

Technology is described for enabling social and virtual interaction during online sessions while the user views or interacts with content infused with interactive advertising objects. (e.g., Product placement in a bait click styled environment.) The user interacts with interactive advertising objects to collect points for later redemption of goods, services, and sponsored prizes. The object of the process is that the user is never interrupted while going about moment-to-moment online activity and or watching a modified television network or cable broadcast of programming events, such but not limited to using a Search engine, watching a live or recorded sports or entertainment video, a music video, social media video and other online content. Alternatively, the system is complimentary to traditional advertising models and can be made to be interactive with and used with traditional television and interactive online media presentations of Sponsors brands and advertising messages.

An aircraft media projection system is provided with a deployment subsystem having an interface to supply an enablement signal with an identification code, in response to an aircraft maintaining a selected midair position in the atmosphere above the ground. A location subsystem determines the midair geographic location of the aircraft, and a communications subsystem, typically a cellular link, has an interface to receive verification information including the enablement signal, identification code, and geographic location, and an interface to transmit the verification information to a server. In one aspect, the system may include a WiFi hotspot that is available for use by the public at large. A targeting software application permits the selection of the midair position from a plurality of potential midair positions. Each potential midair stationary position may have a corresponding weighted value. In another aspect, the system may include a media projection subsystem to selectively project media.

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.

In one embodiment, a method includes accessing a current-visitor context of a current visitor to a web page in a current web-browsing session. The current-visitor context includes one or more data associated with or concerning the current visitor. The method includes selecting based on the current-visitor context a particular one of multiple possible instances of the web page for presentation to the current visitor. The particular one of the multiple possible instances of the web page is substantially most likely to generate a highest expected outcome from interaction with the web page by the current visitor as indicated by the current-visitor context.

Techniques for determining recommended allocations of resources among different platforms that sell a common type of inventory. Determining the allocations can include obtaining parameters of a campaign from a client. Determining the allocations can include combining current campaign parameters and scoring with historical campaign performance data to create recommendations for dividing resources among different media platforms.