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.

Example methods and systems for ad insertion decision-making comprise: identifying an ad insertion opportunity in which an underlying advertisement is available for replacement by an addressable advertisement; determining a first expected revenue corresponding to presentation of the addressable advertisement and a first expected cost corresponding to presentation of the addressable advertisement, wherein the first expected cost is based on a current pacing of an advertisement campaign of the addressable advertisement; determining a second expected revenue corresponding to presentation of the underlying advertisement and a second expected cost corresponding to presentation of the underlying advertisement, wherein the second expected cost is based on a current pacing of an advertisement campaign of the underlying advertisement; determining an expected value of the ad insertion based on the first and second expected revenues and the first and second expected costs; and causing performance of the ad insertion based on the expected value.

Systems and methods relate to targeted display of media content, including a system that includes at least one processor configured to execute program instructions to implement a device and a content management system (CMS) and control the system to: send, by the device and to the CMS, a plurality of signals each triggering the CMS to play on a first display that is local to and separate from the device, a respective one of a plurality of media content of a playlist including a first media content, each of the plurality of signals sent at a different time while the first media content of the playlist is played on the first display, and ignore, by the CMS, each of the plurality of signals received prior to a first predetermined time of the played first media content of the playlist.

A multi-booth traffic allocation scheme for multiple users if provided. In an implementation, an exposure probability of each booth and a unit traffic revenue and corresponding unit resource consumption of each candidate display object for each user are obtained. An optimization objective and a resource consumption constraint corresponding to a multi-booth traffic allocation scheme are constructed. The constructed optimization objective includes a sum value of traffic revenues for each user under the multi-booth traffic allocation scheme. The traffic revenues for each user are determined based on the exposure probability of each booth, a booth allocation scheme of candidate display objects, and the unit traffic revenue of each candidate display object. A multi-booth traffic allocation scheme when the resource consumption constraint is satisfied and the optimization objective is maximized is determined.

An online concierge system facilitates procurement and delivery of items for customers using a network of shoppers. The online concierge system includes a promotion management engine that paces delivery of promotions for content campaigns based in part on predicted item availability and a paced spending model that operates to pace spending of a content campaign over a budget period. The system paces the delivery by determining whether to enter a bid for the impression opportunity by comparing an observed cumulative spend for the content campaign during a portion of the budget period prior to the impression time and a desired cumulative spend for the content campaign during the portion of the budget period prior to the impression time based on the distribution of impression opportunities and a budget for the content campaign during the budget period.

Computer implemented sales prediction system collects data relating to events of visitors showing an interest in a client company from plural data sources, an organization module which organizes collected data into different event types and separates collected event counts in each event type between non-recent and recent events occurring within a predetermined time period, a first processing module which periodically calculates weighting for each event type based on recent and non-recent events for the event type compared to totals for other selected event types, a second processing module which periodically calculates sales prediction scores for each visitor and companies with which visitors are associated based on accumulated event data and weighting, and a reporting and data extract module which is configured to detect variation in sales prediction scores over time to identify spikes which can predict upcoming sales and to provide predicted sales information and leads to the client company.

Devices, systems, and methods are provided for encapsulating machine learning in a clean room to generate a goal-based output. A method may include identifying, by a device operating within a clean room, an agreement between multiple parties to share data for use in machine learning to generate a goal-based output; retrieving the data; selecting the machine learning model based on a goal indicated by the agreement; generating, using the data as inputs to the selected machine learning model, a first set of probabilities indicative that a respective user may perform an action; generating, using the selected machine learning model and the first set of probabilities, a second set of probabilities indicative that a respective user may perform the action; generating the goal-based output based on the second set of probabilities; and sending the goal-based output from the clean room to a destination location.

An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Embodiments disclosed herein generally relate to a system and method for tracking virtual reality experiences. A computing system receives a plurality of location coordinates of a user during a VR simulation. The computing system uploads the plurality of location coordinates to a database. The computing system prompts a client device that an API linking the client device of the user to functionality of the database is available. The computing system receives a query via the API. The computing system translates the received query to a query compatible with the database. The computing system queries the database using the received query for according to criteria set forth in the received query to retrieve a set of location information. The computing system generates a heat map based on the retrieved location information. The computing system prompts the remote client device that the heat map is available for display.