Abstract: 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.

Abstract: 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.

Abstract: An attribution system aggregates and merges online data and offline chronologically. The attribution system examines merged data for unique visitor (UV) sessions initiated at an online medium (e.g., a website) within an attribution window for a spot that aired on an offline medium (e.g., a television network) and, for each conversion event that occurred in a UV session, assigns a session timestamp to it so that the conversion event is correlated to the spot. The attribution system then determines an overall conversion rate of UVs to the online medium in the attribution window and the attribution by the spot that aired on the offline medium to the overall conversion rate of UVs to the online medium in the attribution window. Results of the offline attribution to the online conversions can be visualized and presented on a client device communicatively connected to the attribution system.

Abstract: Embodiments of the present disclosure relate to data correlation of data pertaining to content distributed through distinct content delivery networks including offline networks and data related to user interaction with an online presence on the distributed computer network and uses for such correlated data, including to measure and quantify latent effects of the content distributed through a distinct content delivery network, such as an offline network, on user's interactions with the online presence on the distributed computer network.

Abstract: A self-consistent inception architecture includes a process that integrates online data and offline data to determine an estimated lift (“prior lift”) in the number of unique visitors (UVs) to a website caused by a television (TV) spot airing on an offline medium. The prior lift is used to adjust a UV profile of the website. A baseline thus produced is fitted through an inception process in which a locally weighted scatterplot smoothing algorithm is applied iteratively until a final baseline converges. The baseline from the inception process is used to determine a calculated lift. If the prior lift and the calculated lift are not consistent (e.g., within a threshold), the process is run iteratively until the prior lift and the calculated lift are consistent. The calculated lifts can be used to determine and visualize performance metric(s) relating to media creatives such as TV spots airing in the physical world.

Abstract: An attribution system aggregates and merges online data and offline chronologically. The attribution system examines merged data for unique visitor (UV) sessions initiated at an online medium (e.g., a website) within an attribution window for a spot that aired on an offline medium (e.g., a television network) and, for each conversion event that occurred in a UV session, assigns a session timestamp to it so that the conversion event is correlated to the spot. The attribution system then determines an overall conversion rate of UVs to the online medium in the attribution window and the attribution by the spot that aired on the offline medium to the overall conversion rate of UVs to the online medium in the attribution window. Results of the offline attribution to the online conversions can be visualized and presented on a client device communicatively connected to the attribution system.

Abstract: Embodiments of the present disclosure relate to data correlation of data pertaining to content distributed through distinct content delivery networks including offline networks and data related to user interaction with an online presence on the distributed computer network and uses for such correlated data, including to measure and quantify latent effects of the content distributed through a distinct content delivery network, such as an offline network, on user's interactions with the online presence on the distributed computer network.

Abstract: 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.

Abstract: 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.

Abstract: An attribution system aggregates and merges online data and offline chronologically. The attribution system examines merged data for unique visitor (UV) sessions initiated at an online medium (e.g., a website) within an attribution window for a spot that aired on an offline medium (e.g., a television network) and, for each conversion event that occurred in a UV session, assigns a session timestamp to it so that the conversion event is correlated to the spot. The attribution system then determines an overall conversion rate of UVs to the online medium in the attribution window and the attribution by the spot that aired on the offline medium to the overall conversion rate of UVs to the online medium in the attribution window. Results of the offline attribution to the online conversions can be visualized and presented on a client device communicatively connected to the attribution system.

Abstract: Embodiments of the present disclosure relate to data correlation of data pertaining to content distributed through distinct content delivery networks including offline networks and data related to user interaction with an online presence on the distributed computer network and uses for such correlated data, including to measure and quantify latent effects of the content distributed through a distinct content delivery network, such as an offline network, on user's interactions with the online presence on the distributed computer network.