Abstract: A system and method for attributing in-store visits to exposure to advertisement (“ad”) impressions associated with an ad campaign are disclosed. The system gathers impression data and uses that data to identify users who were exposed to the ad impressions. The system then uses location data, activity information and in some instances beacon data points reported by mobile devices of the impression users to determine if the impression users visited a target place during a conversion window. Based on the impression users who were exposed to the ad impressions, the system establishes a control group of users who were not exposed to the ad impressions to perform quasi-experimental analyses to assess whether the ad impressions had any impact on changing the impression users' physical in-store visitation behavior.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: A method comprises receiving a set of data from at least one interaction, calculating, an audio score for any audio data received in the set of data, and calculating a video score for any video data received in the set of data. The audio score is joined with the video score to create an audiovisual score, and some subset of measurement, alert or recommendation is sent to a user, the set being based on the audiovisual score and configured to be displayed on a user device.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: A method comprises receiving at least one of a transcript, a video recording, an audio recording, or an audiovisual recording of at least a portion of an interaction, and receiving an audiovisual score for a relevant portion of the interaction, the received audiovisual score being based on data received from at least a subset of participants in the interaction. A reaction metric is calculated based on the received audiovisual score. The at least one transcript, video recording, audio recording, or audiovisual recording is then displayed proximate to the reaction metric.

Abstract: A method comprises receiving a set of data from at least one interaction, calculating, an audio score for any audio data received in the set of data, and calculating a video score for any video data received in the set of data. The audio score is joined with the video score to create an audiovisual score, and some subset of measurement, alert or recommendation is sent to a user, the set being based on the audiovisual score and configured to be displayed on a user device.

Abstract: A system and method for attributing in-store visits to exposure to advertisement (“ad”) impressions associated with an ad campaign are disclosed. The system gathers impression data and uses that data to identify users who were exposed to the ad impressions. The system then uses location data, activity information and in some instances beacon data points reported by mobile devices of the impression users to determine if the impression users visited a target place during a conversion window. Based on the impression users who were exposed to the ad impressions, the system establishes a control group of users who were not exposed to the ad impressions to perform quasi-experimental analyses to assess whether the ad impressions had any impact on changing the impression users' physical in-store visitation behavior.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: A system and method for attributing in-store visits to exposure to advertisement (“ad”) impressions associated with an ad campaign are disclosed. The system gathers impression data and uses that data to identify users who were exposed to the ad impressions. The system then uses location data, activity information and in some instances beacon data points reported by mobile devices of the impression users to determine if the impression users visited a target place during a conversion window. Based on the impression users who were exposed to the ad impressions, the system establishes a control group of users who were not exposed to the ad impressions to perform quasi-experimental analyses to assess whether the ad impressions had any impact on changing the impression users' physical in-store visitation behavior.