Cinema-based content exposure attribution system
Systems and methods are disclosed for attributing advertisement exposure in a cinema environment. A ticket event is accessed, indicating a user's purchase of a movie ticket at a specific location, screening room, and showtime. Advertisement schedule data is accessed to identify ads shown before the movie. The system determines that the user was likely present during the advertisement window using ticket information and supplemental data such as geolocation or concession purchase records. Post-exposure events—such as transactions, store visits, or digital engagement—are analyzed to detect behavioral changes following ad exposure. A correlation engine links the exposure to user behavior, enabling ad attribution, effectiveness scoring, and campaign optimization. The system supports privacy-preserving data handling and applies statistical or machine learning models to improve attribution accuracy. This enables advertisers to measure the impact of cinema-based advertisements and make data-driven decisions across physical and digital channels.
1 . A method performed by one or more hardware processors, the method comprising:
accessing ticket event data associated with a user, the ticket event data indicating a purchase of a cinema ticket for a specific theater location;
accessing media content schedule data indicating a schedule for displaying media content at the specific theater location, the media content associated with a good or service;
determining a media content exposure indicating that the user was likely exposed to the media content shown at the specific theater location by inputting the ticket event data associated with movie showing schedule data and the media content schedule data into a machine learning model and the machine learning model being trained to determine the media content exposure based on the inputted ticket event data and the media content schedule data, the media content schedule data being different than the movie showing schedule data;
accessing a post-exposure event associated with the user, the post-exposure event comprising data indicating a transaction associated with the good or service;
correlating the media content exposure with the post-exposure event to determine an influence factor of the media content in a behavior of the user; and
optimizing an electronic media content bidding campaign based on the influence factor of the media content in the behavior of the user.
2 . The method of claim 1 , wherein the ticket event data comprises a receipt from a digital ticketing platform or point-of-sale system that is scanned by a device of the user.
3 . The method of claim 1 , wherein determining the media content exposure comprises comparing a scheduled showtime of a movie associated with the ticket event data to a time period of a scheduled display for the media content that includes an advertisement based on the media content schedule data, wherein the movie is different than the media content.
4 . The method of claim 1 , wherein determining that the user was likely exposed to the media content comprises accessing location data from a mobile device associated with the user, the location data indicating that the mobile device was within a geofence of the specific theater location during a scheduled display for the media content based on the media content schedule data.
5 . The method of claim 1 , wherein determining that the user was likely exposed to the media content comprises accessing entry log data from a theater device, the entry log data indicating a digital ticket scan or in-app check-in associated with the user at the specific theater location and during a scheduled display for the media content based on the media content schedule data.
6 . The method of claim 1 , wherein determining that the user was likely exposed to the media content further comprises validating a presence of the user at the specific theater location and during the scheduled display for the media content based on a transaction associated with the user occurring (1) within the specific theater location and (2) occurring within a time window corresponding to a scheduled display for the media content based on the media content schedule data, the scheduled display for the media content item being different than a movie corresponding to the cinema ticket.
7 . The method of claim 1 , wherein determining that the user was likely exposed to the media content further comprises determining a probability of exposure based on (1) linking the ticket event data to indicating an auditorium identifier within the theater and (2) a scheduled display for the media content to a particular auditorium identifier associated with the user.
8 . The method of claim 1 , wherein the post-exposure event comprises receipt data indicating a purchase of the good or service associated with the media content, the receipt data recorded on a date subsequent to a scheduled showing of the media content.
9 . The method of claim 1 , wherein the post-exposure event comprises receipt data indicating that the user purchased the good or service at a third-party merchant location external to a location for the theater.
10 . The method of claim 1 , wherein the post-exposure event comprises geolocation data of the user indicating that the user visited a physical retail location associated with the good or service within a predefined time window after the media content was shown.
11 . The method of claim 1 , wherein the post-exposure event comprises digital activity data, including browser history or app interaction logs, indicating that the user searched for, viewed, or engaged with information related to the good or service after a scheduled showing of the media content, the media content was displayed in an environment lacking pixel, cookie, or embedded tracking features to directly confirm a user's exposure.
12 . The method of claim 1 , wherein optimizing the media content campaign comprises initiating a new media content campaign for the user based on the determined influence factor.
13 . The method of claim 1 , wherein optimizing the media content campaign comprises modifying a retargeting strategy for the user, the retargeting strategy comprising displaying follow-up media content via a mobile application, a television platform, or a web browser based on the prior media content exposure.
14 . The method of claim 1 , wherein optimizing the media content campaign comprises initiating an order for additional advertisements to be displayed at the specific theater location associated with the media content based on the determined influence factor.
15 . The method of claim 1 , wherein the media content exposure is determined using a machine learning model to determine that the user was likely exposed to the media content based on inputting the ticket event data and the media content schedule data into the machine learning model.
16 . The method of claim 1 , wherein the post-exposure event is correlated using a machine learning model to determine the influence factor based on inputting the media content exposure and the post-exposure event into the machine learning model.
17 . The method of claim 1 , wherein the media content campaign is optimized using a machine learning model to based on inputting the influence factor into the machine learning model.
18 . The method of claim 1 , wherein the media content schedule data includes dynamic targeting parameters associated with an expected audience profile, and wherein optimizing the media content campaign comprises adjusting future media content blocks based on a correlation between audience profile and post-exposure behavior.
19 . A system comprising:
at least one processor; and
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
accessing ticket event data associated with a user, the ticket event data indicating a purchase of a cinema ticket for a specific theater location;
accessing media content schedule data indicating a schedule for displaying media content at the specific theater location, the media content associated with a good or service;
determining a media content exposure indicating that the user was likely exposed to the media content shown at the specific theater location by inputting the ticket event data associated with movie showing schedule data and the media content schedule data into a machine learning model and the machine learning model being trained to determine the media content exposure based on the inputted ticket event data and the media content schedule data, the media content schedule data being different than the movie showing schedule data;
accessing a post-exposure event associated with the user, the post-exposure event comprising data indicating a transaction associated with the good or service;
correlating the media content exposure with the post-exposure event to determine an influence factor of the media content in a behavior of the user; and
optimizing an electronic media content bidding campaign based on the influence factor of the media content in the behavior of the user.
20 . A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
accessing ticket event data associated with a user, the ticket event data indicating a purchase of a cinema ticket for a specific theater location;
accessing media content schedule data indicating a schedule for displaying media content at the specific theater location, the media content associated with a good or service;
determining a media content exposure indicating that the user was likely exposed to the media content shown at the specific theater location by inputting the ticket event data associated with movie showing schedule data and the media content schedule data into a machine learning model and the machine learning model being trained to determine the media content exposure based on the inputted ticket event data and the media content schedule data, the media content schedule data being different than the movie showing schedule data;
accessing a post-exposure event associated with the user, the post-exposure event comprising data indicating a transaction associated with the good or service;
correlating the media content exposure with the post-exposure event to determine an influence factor of the media content in a behavior of the user; and
optimizing an electronic media content bidding campaign based on the influence factor of the media content in the behavior of the user.