Systems and methods for operating placebo-based experiments are described for online advertisements. One or more embodiments of the disclosed systems and methods utilizes an ad swapping approach to offer placebo media exposures, at no additional cost to an advertiser. One or more embodiments further provide a native experimentation platform that allows users to run tests of ad placements to measure the effectiveness of ads and view results displayed on a user interface. The disclosed systems and methods can assign viewers into a test group if shown the test ad or a control group if shown a control ad. The control ad can be provided at no cost to the advertiser for embodiments where the placebo ad belongs to an alternative advertiser. Effectiveness of third party attribution can also be evaluated. The disclosed systems and methods can define experiment parameters, including control frequency, test viewer groups, and control viewer groups.

Systems and methods are disclosed herein for attributing credit to online consumer touchpoints for a consumer performing an action. The systems and methods involve determining whether a consumer is in a particular environment for an online consumer touchpoint by detecting an external viewing condition for the consumer for the online consumer touchpoint. The systems and methods determine that the consumer performed an action, such as a conversion, following the online consumer touchpoint and additional online consumer touchpoints. An effectiveness of the online consumer touchpoint in the particular environment is determined and used to attribute relative credit to the online consumer touchpoint and the additional online consumer touchpoints for the consumer performing the action.

A method is disclosed for enabling a network location to provide an ordering process for data relevant to connected network devices' activities. The method includes assembling the data, utilizing the activity data, and associating the data, such that information is derived to enable a desired expansion of at least one designated activity. Another method is disclosed for managing an object assignment broadcast operations for a network location based on a network device's previous activities. This second method includes tracing a network device's conduct to determine that a network device prefers a particular class of content. The method also includes tagging a network device's profile with the respective observation and deciding by a network location as to the classification factor for a network device to be targeted for an object assignment broadcast.

An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management(DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (AND) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Disclosed are methods, systems, and non-transitory computer-readable medium for targeting bid and position for a keyword. For instance, the method may include obtaining information about the keyword, the information about the keyword including observations of value with respect to position for the keyword. The method may further include applying a Gaussian Process Model on the observations to obtain a prediction function and associated uncertainties, the prediction function and the associated uncertainties relating positions to expected values; applying a Thompson sampling reinforcement learning model on the expected values and the positions to obtain a target position; and applying a bid model to the target position to obtain bid information for the keyword. The method may also include transmitting a bid message to a search engine, the bid message including the bid information.

A determination device according to the present application includes an acquisition unit, a calculation unit, and a determination unit. The acquisition unit acquires user information that is information regarding a user who uses a terminal device that becomes a providing destination of content. The calculation unit calculates scores regarding a probability of the user performing a predetermined behavior for a plurality of pieces of the content on the basis of the user information acquired by the acquisition unit. The determination unit determines distribution content to be distributed to the terminal device on the basis of the scores of the plurality of pieces of content calculated by the calculation unit.

A computer-implemented method for optimizing publisher profits in online advertising based on real market movements is provided. The method may include dividing an advertisement (ad) placement for an ad unit into a plurality of levels. The method may also include generating a plurality of variables that are configured to test for the real market movements between levels. The method may further include processing ad impressions based on the plurality of variables within a time period to evaluate the real market movements for the ad unit, and determining whether to adjust one or more of the minimum floor prices based on a score for each variable calculated within the time period.

Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving a selection of a first objective and a second objective, the first objective and second objective comprising goals associated with distribution of a plurality of data elements; receiving an indication that the first objective has a higher priority than the second objective; receiving a first goal metric associated with the first objective and a second goal metric associated with the second objective; determining a first forecasted metric based on the first goal metric associated with the first objective; determining a second forecasted metric based on the second goal metric associated with the second objective; and allocating resources for the distribution of a plurality of data elements based on the first goal metric, the second goal metric, the first forecasted metric, the second forecasted metric, and the indication that the first objective has a higher priority than the second objective.

A digital advertising system includes at least one processor configured to execute a plurality of functional modules including an analytics module to receive and analyze client attributes associated with a website visitor and a requested website to define an analytics event. The analytics module ingests and enriches data within the analytics event and provides it to a machine learning module that generates prediction models for potential bids. A management platform receives the bidding prediction and generates candidate configs. An optimization module receives the candidate configs and applies weights and additional features to select a config and generate an optimized script for the selected config. A deployment module receives the optimized script and delivers the script to the website visitor.

A system and method for dynamically adjusting an online marketing campaign, in various embodiments, is configured to increase and/or decrease one or more keyword bids that make up part of an online mark'seting campaign for a particular product from a particular retailer based on whether: (1) the particular product is or is not competitively priced relative to one or more competing retailers; and/or (2) an advertisement for the particular product from the particular retailer on a search engine results page or in an online marketplace is in a relatively desirable position.