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: The present disclosure relates to systems and methods for automatic attribution modeling and measurement. In aspects, a system may receive identification information associated with profiles and electronic devices that were exposed to a certain piece of targeted content. The demographic and device data associated with the individuals who were exposed to the targeted content are used to create a control group of individuals who were not exposed to that targeted content. The real-world visit rates of the exposed group and the control group to one or more locations may be monitored over a period of time (or campaign) and evaluated to assess the effectiveness of the targeted content.

Abstract: The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

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: In non-limiting examples, passive information is used to verify the location of a client computing device and to create segment categories for directed information using location-based services and/or applications. In aspects, passive information is received from a client computing device. The client computing device is detected as being located at a predicted venue using the passive information. It may be determined whether directed information associated with the predicted venue has been displayed at an earlier time on a predetermined portion of the client computing device. In response to determining directed information associated with the predicted venue has been displayed on the predetermined portion of the client computing device, a statistical model may be applied to calculate a confidence score that the client computing device is located at the predicted venue. When the confidence score meets a predetermined threshold, a directed information conversion record may be stored in a data store.

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: Examples of the present disclosure describe systems and methods for venue detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from in response to a detected movement event or user interaction data. The sensor data may be used to generate a set of candidate venues corresponding to the location of the mobile device. The candidate venues may be provided to a venue detection system. The venue detection system may process the candidate venues to generate a set of features. The set of features may be applied to, and/or used to generate, one or more probabilistic models. The probabilistic models may generate confidence metrics for each of the candidate venues. In some aspects, the top ‘N’ venues may be selected from the set. The top ‘N’ venues may then be presented to the user and/or used to effect one or more actions.

Abstract: The present disclosure relates to apparatus, systems, and methods for analyzing characteristics of entities of interest. In particular, the present disclosure provides a mechanism for analyzing information about entities of interest and for rating or scoring the entities of interest based on the analyzed information. The rating or the score of an entity of interest can sometimes be referred to as a placerank value of an entity of interest.

Abstract: The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., Jesse Hate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.

Abstract: Examples of the present disclosure describe systems and methods for passive visit detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from the set of sensors in response to detecting a movement event or user interaction data. The collected sensor data may be processed and provided as input to one or more predictive or statistical models. The model(s) may evaluate the sensor data to detect mobile device location, movement events and visit events. The model(s) may also be used to determine correlations between features of the sensor data and movement- or location-based events, optimize the types of data collected by the set of sensors, extend localized predictions to large-scale ecosystems, and generate battery-efficient state predictions. In aspects, the model(s) may be trained using labeled and/or unlabeled data sets of sensor data.

Abstract: This disclosure relates to systems and methods for passive visit detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from the set of sensors in response to detecting a movement event or user interaction data. The collected sensor data may be processed and provided as input to one or more predictive or statistical models. The model(s) may evaluate the sensor data to detect mobile device location, movement events and visit events. The model(s) may also be used to determine correlations between features of the sensor data and movement- or location-based events, optimize the types of data collected by the set of sensors, extend localized predictions to large-scale ecosystems, and generate battery-efficient state predictions. In aspects, the model(s) may be trained using labeled and/or unlabeled data sets of sensor data.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.

Abstract: In non-limiting examples of the present disclosure, taste data is generated and usable within one or more applications. A taste is one or more elements that describe an entity. Information of an application may be processed to extract entity data that corresponds to a plurality of candidates to be designated as tastes (taste data). The candidates for the tastes are curated. In examples, the curating comprises filtering the candidates for tastes to remove extracted candidates. Extraction rules for managing structured taste data may be applied in the filtering. A status of a remaining candidate may be determined as approved or rejected based on processing of received user feedback. Taste data may be generated for an approved candidate. The generating of the taste data may comprise assigning parameters that include a descriptor type and a recommendation type. Taste data may be used within an application.

Abstract: In non-limiting examples of the present disclosure, taste data is generated and usable within one or more applications. A taste is one or more elements that describe an entity. Information of an application may be processed to extract entity data that corresponds to a plurality of candidates to be designated as tastes (taste data). The candidates for the tastes are curated. In examples, the curating comprises filtering the candidates for tastes to remove extracted candidates. Extraction rules for managing structured taste data may be applied in the filtering. A status of a remaining candidate may be determined as approved or rejected based on processing of received user feedback. Taste data may be generated for an approved candidate. The generating of the taste data may comprise assigning parameters that include a descriptor type and a recommendation type. Taste data may be used within an application.

Abstract: In non-limiting examples, passive information is used to verify the location of a client computing device and to create segment categories for directed information using location-based services and/or applications. In aspects, passive information is received from a client computing device. The client computing device is detected as being located at a predicted venue using the passive information. It may be determined whether directed information associated with the predicted venue has been displayed at an earlier time on a predetermined portion of the client computing device. In response to determining directed information associated with the predicted venue has been displayed on the predetermined portion of the client computing device, a statistical model may be applied to calculate a confidence score that the client computing device is located at the predicted venue. When the confidence score meets a predetermined threshold, a directed information conversion record may be stored in a data store.

Abstract: The present disclosure relates to systems and methods for automatic attribution modeling and measurement. In aspects, a system may receive identification information associated with profiles and electronic devices that were exposed to a certain piece of targeted content. The demographic and device data associated with the individuals who were exposed to the targeted content are used to create a control group of individuals who were not exposed to that targeted content. The real-world visit rates of the exposed group and the control group to one or more locations may be monitored over a period of time (or campaign) and evaluated to assess the effectiveness of the targeted content.

Abstract: In non-limiting examples, passive information is used to verify the location of a client computing device and to create segment categories for directed information using location-based services and/or applications. In aspects, passive information is received from a client computing device. The client computing device is detected as being located at a predicted venue using the passive information. It may be determined whether directed information associated with the predicted venue has been displayed at an earlier time on a predetermined portion of the client computing device. In response to determining directed information associated with the predicted venue has been displayed on the predetermined portion of the client computing device, a statistical model may be applied to calculate a confidence score that the client computing device is located at the predicted venue. When the confidence score meets a predetermined threshold, a directed information conversion record may be stored in a data store.

Abstract: Examples of the present disclosure describe systems and methods for passive visit detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from the set of sensors in response to detecting a movement event or user interaction data. The collected sensor data may be processed and provided as input to one or more predictive or statistical models. The model(s) may evaluate the sensor data to detect mobile device location, movement events and visit events. The model(s) may also be used to determine correlations between features of the sensor data and movement- or location-based events, optimize the types of data collected by the set of sensors, extend localized predictions to large-scale ecosystems, and generate battery-efficient state predictions. In aspects, the model(s) may be trained using labeled and/or unlabeled data sets of sensor data.