Abstract: Mobile usage data representing the access of one or more resources on a network by mobile devices is accessed. The mobile usage data includes information received from mobile devices as a result of beacon instructions included with the one or more resources. A first set of data representing information about accesses to the one or more resources by mobile devices with persistent beacon cookies is determined. A second set of data representing information about accesses to the one or more resources by mobile devices with non-persistent beacon cookies is determined. One or more adjustment factors are determined based on the first set of data. A count of unique visitors accessing the one or more resources from the mobile devices is determined based on a count of accesses by the mobile devices with persistent beacon cookies and a count of accesses by the mobile devices with non-persistent beacon cookies adjusted by the one or more adjustment factors.

Abstract: Systems and methods herein log traffic to and from a device on a network. Logging can occur using a metering device, router, proxy, or other elements. For example, a metering device operatively coupled to a routing device can log the traffic directed to and originating from a user device. Logged traffic can be analyzed to identify users, devices, and/or sessions. For example, an identifier unique to the user device in the session, a device type of the user device, and a specific user of the device during the session can be identified.

Abstract: Methods and systems are provided herein for calibrating subject data based on reference data, so that the calibrated subject data more closely represents a target population. The methods and systems include partitioning a reference data set into a plurality of reference data partitions using a data partitioning scheme, each reference data partition associated with a characteristic; and partitioning a subject data set into a plurality of subject data partitions using the data partitioning scheme, each subject data partition associated with a characteristic that corresponds to the characteristic associated with a reference data partition of the plurality of reference data partitions; identifying a variable present in the reference data set that is not present in the subject data set; and calculating a value of the variable for each reference data partition based on a rate of occurrence of the variable in each reference data partition.

Abstract: A method of classifying a product placement in a video using rule sets is disclosed. Each rule of the rule set includes a value and one or more defining rule elements. An attribute rule set is created with attribute values and attribute elements that define levels of audio visual prominence of a product in the video. An integration rule set is created with integration values and integration elements where the integration elements define levels of integration of the product with video continuity. The video is partitioned at product scene changes to create product blocks. For each product block, an attribute value is selected based on the attribute elements and an integration value is selected based on the integration elements. An impact parameter for the video is derived as a function of the selected attribute values and integration value.

Abstract: Measuring a networked audience is described. Initially, a first set of network usage data based on access of a resource by a first set of client systems is received. Next, a second set of network usage data based on access of the resource by a second set of client systems using a monitoring application installed on the second set of client systems is determined. Usage of the resource based on the first set of network usage data during a time period and one or more adjustment factors based on the second set of network usage data are determined. The determined usage is adjusted using the one or more adjustment factors. Finally, one or more audience reports for the resource using the adjusted usage are generated.

Abstract: Methods and systems for fraudulent traffic detection and estimation are disclosed. Initially, an empirical distribution of a plurality of features based on a first plurality of datapoints for the plurality of features is received. Next, a model distribution of the plurality of features based on a second plurality of datapoints for the plurality of features is received. Then, it is determined, a minimum number of datapoints to remove from the first plurality of datapoints to create a modified empirical distribution corresponding to the model distribution within a first significance level. Finally, an alert that the first plurality of web traffic includes at least one fraudulent instance of web traffic is generated in response to the determination that the minimum number of datapoints is greater than a first threshold.

Abstract: Aggregating viewership data is disclosed. Initially, household viewership data assigned to a tuning event in multiple households is accessed. Then, the household viewership data for the tuning event in the multiple households representing the same episode is aggregated to generate episode viewership data. Next, episode viewership data representing multiple episodes of the same program is aggregated to generate program viewership data. Next, the program viewership data for multiple programs of the same network is aggregated to generate network viewership data. Finally, the network viewership data for multiple commonly-owned networks is aggregated to generate entity viewership data.

Abstract: Systems and methods herein log traffic to and from a device on a network. Logging can occur using a metering device, router, proxy, or other elements. For example, a metering device operatively coupled to a routing device can log the traffic directed to and originating from a user device. Logged traffic can be analyzed to identify users, devices, and/or sessions. For example, an identifier unique to the user device in the session, a device type of the user device, and a specific user of the device during the session can be identified.

Abstract: A computerized method of transmitting content to a first device and a second device may include receiving a first identifier and first location data of the first device, and a second identifier and second location data of the second device. The method may include comparing the first location data with the second location data, and generating a co-location score in response to the comparison. The method may include determining that the co-location score is greater than a threshold, and responsively generating household data indicative of a relationship between the first device and the second device. The method may further include generating and transmitting a report indicating the relationship to a content provider that transmits content to the first device and the second device.

Abstract: A first image of a mobile device screen is recorded into memory of the mobile device. The first image includes at least one icon that represents an application installed on the mobile device. A second image of the mobile device screen is recorded into the memory of the mobile device. A graphical change in an area of the mobile device screen corresponding to a position of the icon is detected by comparing at least a portion of the second image to the first image. The graphical change results from a user selection of the icon to activate the application represented by the icon. In response to detecting the graphical change, determine an identifier of the application represented by the icon. Send a record of the user selection of the icon to a collection server. The record includes at least the identifier of the application.

Abstract: Providing analysis of exposure of users to content without the use of personally identifiable information (PII) is described. Initially, first user activity data is obtained from a server associated with a collection service. The first user activity data comprises a first unique identifier (ID) deterministically created based on an Internet protocol (IP) address of a first entity using a first one-way function. Next, second user activity data is obtained from a service provider. The second user activity data comprises a second unique ID deterministically created based on PII of a second entity using a second one-way function. It is determined whether the first unique ID is the same as the second unique ID, and responsive to that determination, the first and second user activity data are continuously aggregated such that a trend is determined in real-time and analysis of the aggregated user activity data is performed and reported in compliance with mandated legal or policy privacy provisions.

Abstract: Techniques for projecting person-level viewership from household-level tuning events are described. Initially, panelist viewing data are accessed and a plurality of state values based on the panelist viewing data are determined. Then, tuning data representing tuning events associated with particular households are accessed. For at least one tuning event represented by the tuning data, household member data is accessed, a portion of the panelist viewing data whose panelist information matches at least a portion of the member data is determined, a total number of watched minutes of the program by an individual member and a number of continuous series of watched states of the program by the individual member is determined, and an output representative of a probability that the particular portion of the program was watched by one or more of the individual members is generated.

Abstract: Measuring a networked audience is described. Initially, a first set of network usage data is determined based on a first set of signaling information obtained from a first subset of the client systems as a result of the first subset accessing common resources. In addition, a second set of network usage data is determined based on a second set of information obtained from a second subset of the client systems as a result of an application installed thereon monitoring accessing of the common resources. Usage is measured based on the first set of network usage data by determining an initial count of unique visitors that accessed the common resources by determining a count related to signaling of the first set of signaling information. Adjustment factors are determined based on the second set of network usage data. Finally, audience reports are generated by adjusting the measured usage using the one or more adjustment factors.

Abstract: An identifier associated with a client machine is accessed, and parameters that define a control group of client machines for a particular advertising campaign are accessed. The advertising campaign is associated with one or more standard advertisements. Whether the client machine is a member of the control group based on the identifier associated with the client machine and the parameters that define the control group is determined. If the determination of whether the client machine is a member of the control group indicates that the client machine is a member of the control group, a control advertisement is caused to be presented on the client machine. The control advertisement is an advertisement not associated with the advertising campaign.

Abstract: The systems and techniques described herein measure advertisement effectiveness of behavior-based outcomes (e.g., site visit, number of pages consumed, searches, online and offline transactions). The system implemented an automated model to measure the impact of exposures and impressions on outcomes using uses panel data, cookie-based data, and combinations thereof. The techniques use test and control approach to calculate effectiveness, where the test group are those exposed to a campaign and a control group who is not exposed. For those exposed, a running analysis of impressions (and other variables) in a pre period is used to determine behavior based outcomes over a set time period after that exposure. As a result, the automated model is able to generate metrics that show absolute and relative impacts on future behavior.

Abstract: Methods and systems are provided herein for collecting web browser click events across a plurality of web sites from a data collection agent (DCA), as a click-stream, at a data collection server (DCS) to record and provide user on-line activity, filtering the user online activity to include activity from a time period prior to a sale from the sales transaction data and identifying one or more shopping touch-points based on the filtered user online activity and the sales transaction data. Further, an engagement index, an influence index, and an opportunity index is calculated. A digital touch-points facility may perform the identifying and calculating.

Abstract: Systems, methods, and computer-readable medium are provided for identifying content displayed by a media device and associating the content with demographics of one or more users. An indication to capture an image of the content displayed by the media device is initially received. The image of the content is then captured and stored. One or more image fingerprints in the image of the content are identified. The monitoring device transmits the one or more image fingerprints, a timestamp of the time at which the image was captured, and an identifier of the monitoring device to a server. The server compares the one or more image fingerprints to fingerprints in a database to identify the content displayed by the media device. The identity of the content is then associated with demographic information of the one or more users. A report of the content viewership of the content displayed by the media device is then generated.

Abstract: A computer system may include at least one processor and at least one memory storing instructions that, when executed, cause the at least one processor to perform a process. The process may include receiving audio data from a user device, and accessing content data including at least one audio signature associated with video content. The process may also include correlating the audio data with the at least one audio signature and identifying recognized video content based on the correlation of the audio data with the at least one audio. The process may also include receiving tuning data including content being presented on a display component. The process may further include correlating the recognized video content with the tuning data, determining viewed video content based on the correlation of the recognized video content with the tuning data, and storing the viewed video content in a user array.

Abstract: Usage data representing the access of a network entity by a plurality of client devices each associated with one or more Internet Protocol (IP) addresses of a plurality of IP addresses is initially accessed. Then a set of static IP addresses of the plurality of IP addresses is determined based on the usage data. A first count of static IP addresses of the set of static IP addresses that are all associated with mobile traffic or that are all associated with non-mobile traffic is determined. A second count of static IP addresses of the set of static IP addresses that are associated with the mobile traffic and the non-mobile traffic is also determined. An overlap factor is determined based on a ratio between the first count of static IP addresses and the second count of static IP addresses. Based on the overlap factor, a total count of unique static IP addresses via which the network entity was accessed is finally determined.

Abstract: Several ways of identifying users and collecting demographic information and market information are disclosed, including branding a browser with a unique identification in each user request, identifying a user by his key strokes or mouse clicks, gathering demographic information using multiple data sets and by monitoring network traffic. Additionally, user requested content is distinguished from other, non-user content, and the performance of a server can be monitor and analyzed from a client a client perspective. Further, an Internet user's Internet data is routed to a known domain on the Internet, from which it is routed on to the intended recipient. The domain includes proxy servers which proxy the user's data requests to the domain, and database servers, which filter and build a database of the user's Internet usage. Particular data concerning certain behaviors of interest, such as purchasing data, is filtered into the database, and can form the basis for numerous market measures.