Embodiments of a system method and computer program product for selecting an advertisement and presenting it to a user are described. Products and services offered by various merchants are read using a merchant specific catalog and stored in a common format. Categories for such products and services are normalized and virtual categories are created using various product attributes. Visual creatives, termed as ad-templates are created to control the visual and interactive aspects of the ad, including ad-size, color, as well as product attributes that are displayed in the ad. Ad-templates may be constrained to specific products or product categories. A learning algorithm uses an adaptive sampling process to sample various products, product categories and ad-templates independently for different learning units such as individual users, groups of users determined by some demographics, individual web pages and groups of web pages grouped using various similarity criteria. The performance of the ad is measured using various learning statistics, such as the click-through-rate, conversion rate, etc. The learning algorithm uses the learning statistics to optimize the return for the advertiser by favoring the products or categories that perform better on one or more specified criteria.

The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign—according to user-input targeting criteria and expenditure constraints.

A method, apparatus and computer program product are provided for integrated sales management. A sales resource may use the system to track and modify promotion proposals for providers. Data defining the proposed promotion may include information relating to the provider, capacity, the promotion structure, return on investment, and/or an auto-approval status. Some data defining the proposed promotion may be automatically generated by the promotional system and/or modified by the provider. The proposal may be auto-approved or approved manually by a sales resource. Upon approval, the promotion may be made available for sale.

A non-limiting exemplary embodiment of a system includes metadata for a plurality of locations, metadata for a plurality of fixtures, metadata for a plurality of campaigns, rules for each location, an apparatus for producing signages for the plurality of locations, and filters for segregating the signages produced into a plurality of kits. Another non-limiting exemplary embodiment of a system includes metadata for a plurality of locations, metadata for a plurality of fixtures, metadata for a plurality of campaigns, rules for each location, filters for segregating the display graphics into a plurality of data packets, transmitting the data packet to the location for which it was prepared, and displaying the graphics contained in the data packet in the location to which it was transmitted.

A quantification system is configured for debiasing media creative efficiency. In some embodiments, the quantification system leverages a weighted generalized linear model (GLM) to determine the individual impacts of media creatives beyond network effects. To prepare input data for fitting the weighted GLM, the quantification system analyzes spot airing data, creates a specific data structure for storing observations (e.g., network-media creative combinations) that can be provided to the weight GLM as input, and computes additional input data points needed by the weighted GLM (e.g., network spend, media creative efficiency per network-media creative combination, etc.). The weighted GLM is then fitted to obtain coefficients representing the individual impacts of the media creatives. The quantification system utilizes the computed impacts to adjust the previously computed media creative efficiency for each media creative. In this way, relative performance of media creatives can be objectively quantified across networks without needing digital evidence.

A media creative attribution method includes determining a response profile within an attribution time window, the response profile being a portion of a unique visitor (UV) curve associated with a website. In some cases, a shadow baseline analysis is run on every media creative that aired within an extended time window to determine whether to adjust the response profile. A total lift within the attribution time window is determined utilizing a baseline of the UV curve. A weight for each media creative that aired within the attribution time window is determined. Utilizing the weight, the total lift is allocated to individual media creatives that aired within the attribution time window. The allocated attribution can be utilized to generate performance metrics relating to the individual media creatives that aired within the attribution time window. The performance metrics such as cost per visitor can be visualized through a user interface or dashboard.

Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. An example apparatus includes memory; instructions in the apparatus; and processor circuitry to execute the instruction to, based on impression requests from a plurality of client devices via a network, log a plurality of impressions corresponding to media accessed at the client devices; generate a product by multiplying a count of the impressions by a square of a count of registered users of a database proprietor exposed to the media; generate a quotient by dividing the product by a count of demographic impressions logged by the database proprietor; and determine a unique audience size based on a square root of the quotient.

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.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for polymer production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a polymer production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and providing a provable access to the instruction set. The method may further include providing commands to a production tool of the polymer production process and recording the transaction on the distributed ledger.

Value sharing for a social network is provided. One or more servers send, to a client device of a first user of the social network, a support me icon in association with a post on the social network, the post having been created by a second user of the social network, the support me icon configured to cause an advertisement to be presented to the first user responsive to selection of the support me icon by the first user. The one or more servers receive, from the client device, an indication that the advertisement was presented to the to the first user. The one or more servers allocate, to the second user, a first portion of value corresponding to the presentation of the advertisement to the first user, thereby compensating the second user for creating the post on the social network.