COMBINING MEASUREMENTS BASED ON BEACON DATA

• US 20120324101A1
• Filed: 05/25/2012
• Published: 12/20/2012
• Est. Priority Date: 05/25/2011
• Status: Active Grant

• Current Assignee: comScore, Inc.
• Original Assignee: comScore, Inc.
• Inventors: Pugh, Brian, Pecjack, Frank, Nasr, Ramzi, Nair, Balakrishnan Vinayak, Johnson, Kris
• Granted Patent: US 9,430,746 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/146 Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
• CPC Class Codes: H04W 72/535 based on resource usage policies

Abstract

Usage data representing the access of a set of resources on a network is accessed. The usage data is based at least in part on information received from client systems sent as a result of beacon instructions included with the set of resources. First and second sets of usage data representing access by client systems classified as a first type and a second type, respectively, are determined based on the accessed usage data. Counts of unique visitors accessing the network resources from each of the first and second types of client systems, based on the first and sets of usage data, respectively, are each determined. A total count of unique visitors accessing the network resources from the first and second types of client systems is determined based on data representing the usage overlap of devices of the first type with devices of the second type.

Claims

1. A computer-implemented method comprising:

accessing usage data representing the access of a set of resources on a network, the usage data based at least in part on information received from client systems sent as a result of beacon instructions included with the set of resources;

determining, based on the accessed usage data, a first set of usage data representing access by client systems classified as a first type;

determining a count of unique visitors accessing the network resources from the first type of client systems based on the first set of usage data;

determining, based on the accessed usage data, a second set of usage data representing access by client systems classified as a second type;

determining a count of unique visitors accessing the network resources from the second type of client systems based on the second set of usage data; and

determining a total count of unique visitors accessing the network resources from the first and second types of client systems based on data representing the usage overlap of devices of the first type with devices of the second type.

2. The method of claim 1, wherein the first type of client system is a non-mobile, non-shared use personal computing device type.

3. The method of claim 2, wherein the second type of client system is a mobile computing device type.

4. The method of claim 3, further comprising:

determining, based on the accessed usage data, a third set of usage data representing access by client systems classified as a third type; and

determining a count of unique visitors accessing the network resources from the third type of client systems based on the third set of usage data;

wherein determining the total count of unique visitors accessing the network resources comprises determining a total count of unique visitors accessing the network resources from the first, second, and third types of client systems based on data representing the usage overlap of devices of the first, second, and third types.

5. The method of claim 4, wherein the third type of client system is a shared use computing device type.

6. The method of claim 2, wherein the second type of client system is a shared use computing device type.

7. The method of claim 1, wherein:

the second type of client system is a mobile computing device type, and

determining a count of unique visitors accessing the network resources from the second type of client systems comprises;

determining, based on the second set of usage data, a second subset of data representing information about accesses to the resources by mobile devices with non-persistent beacon cookies, wherein the non-persistent beacon cookies were present for a duration that does not meet the persistence threshold;

determining one or more adjustment factors based on the first subset of data; and

determining a count of unique visitors accessing the set of resources from mobile devices based, at least in part, on a count of accesses by mobile devices with persistent cookies and a count of accesses by mobile devices with non-persistent beacon cookies adjusted by the adjustment factors.

8. The method of claim 1, wherein:

the second type of client system is a shared use computing device type, and

determining a count of unique visitors accessing the network resources from the second type of client systems comprises;

determining, based on the second set of usage data, a subset of shared use data representing access of the network resources by shared use devices sharing a common location;

estimating a number of shared use devices and a number of persons at the common location based on the subset of shared use data; and

determining a count of unique visitors to the network resources from shared use devices at the common location based on the estimated number of shared use devices and number of persons at the common location.

9. The method of claim 1, further comprising:

accessing sample data representing the access of the network resources, the sample data including information about the overlap in use of client systems classified as the first type and client systems classified as the second type; and

generating an overlap factor based on the accessed sample data, the overlap factor representing the usage overlap of devices of the first type with devices of the second type;

wherein determining the total count comprises determining the total count of unique visitors accessing the network resources based on the generated overlap factor.

10. The method of claim 9, wherein the sample data represents survey data for users accessing one or more of the network resources, the survey data classifying each user'"'"'s current client system used to access one or more of the network resources and further classifying one or more other client systems regularly used by the user.

11. The method of claim 9, wherein the sample data is panel data.

12. A computer readable storage medium storing instructions that, when executed by one or more processing devices, cause the one or more processing devices to perform the following operations:

accessing usage data representing the access of a set of resources on a network, the usage data based at least in part on information received from client systems sent as a result of beacon instructions included with the set of resources;

determining, based on the accessed usage data, a first set of usage data representing access by client systems classified as a first type;

determining a count of unique visitors accessing the network resources from the first type of client systems based on the first set of usage data;

determining, based on the accessed usage data, a second set of usage data representing access by client systems classified as a second type;

determining a count of unique visitors accessing the network resources from the second type of client systems based on the second set of usage data; and

determining a total count of unique visitors accessing the network resources from the first and second types of client systems based on data representing the usage overlap of devices of the first type with devices of the second type.

13. The medium of claim 12, wherein the first type of client system is a non-mobile, non-shared use personal computing device type.

14. The medium of claim 13, wherein the second type of client system is a mobile computing device type.

15. The medium of claim 14, wherein the instructions include instructions that, when executed, cause the one or more processing devices to perform the following operations:

determining, based on the accessed usage data, a third set of usage data representing access by client systems classified as a third type; and

determining a count of unique visitors accessing the network resources from the third type of client systems based on the third set of usage data;

wherein determining the total count of unique visitors accessing the network resources comprises determining a total count of unique visitors accessing the network resources from the first, second, and third types of client systems based on data representing the usage overlap of devices of the first, second, and third types.

16. The medium of claim 15, wherein the third type of client system is a shared use computing device type.

17. The medium of claim 13, wherein the second type of client system is a shared use computing device type.

18. The medium of claim 12, wherein:

the second type of client system is a mobile computing device type, and

to determine a count of unique visitors accessing the network resources from the second type of client systems, the instructions include instructions that cause the one or more processing devices to perform the following operations;

determining, based on the second set of usage data, a first subset of data representing information about accesses to the resources by mobile devices with persistent beacon cookies, wherein the persistent beacon cookies were present for a duration that meets a persistence threshold;

determining, based on the second set of usage data, a second subset of data representing information about accesses to the resources by mobile devices with non-persistent beacon cookies, wherein the non-persistent beacon cookies were present for a duration that does not meet the persistence threshold;

determining one or more adjustment factors based on the first subset of data; and

determining a count of unique visitors accessing the set of resources from mobile devices based, at least in part, on a count of accesses by mobile devices with persistent cookies and a count of accesses by mobile devices with non-persistent beacon cookies adjusted by the adjustment factors.

19. The medium of claim 12, wherein:

the second type of client system is a shared use computing device type, and

to determine a count of unique visitors accessing the network resources from the second type of client systems, the instructions include instructions that cause the one or more processing devices to perform the following operations;

determining, based on the second set of usage data, a subset of shared use data representing access of the network resources by shared use devices sharing a common location;

estimating a number of shared use devices and a number of persons at the common location based on the subset of shared use data; and

determining a count of unique visitors to the network resources from shared use devices at the common location based on the estimated number of shared use devices and number of persons at the common location.

20. The medium of claim 12, wherein the instructions include instructions that cause the one or more processing devices to perform the following operations:

accessing sample data representing the access of the network resources, the sample data including information about the overlap in use of client systems classified as the first type and client systems classified as the second type; and

generating an overlap factor based on the accessed sample data, the overlap factor representing the usage overlap of devices of the first type with devices of the second type;

wherein determining the total count comprises determining the total count of unique visitors accessing the network resources based on the generated overlap factor.

21. The medium of claim 20, wherein the sample data represents survey data for users accessing one or more of the network resources, the survey data classifying each user'"'"'s current client system used to access one or more of the network resources and further classifying one or more other client systems regularly used by the user.

22. The medium of claim 20, wherein the sample data is panel data.

23. A computer-implemented method, comprising:

accessing mobile usage data representing the access of one or more resources on a network by client devices classified as mobile devices, the mobile usage data including information received from client devices sent as a result of beacon instructions included with the set of resources;

determining, based on the accessed mobile usage data, a first set of data representing information about accesses to the resources by mobile devices with persistent beacon cookies, wherein the persistent beacon cookies were present for a duration that meets a persistence threshold;

determining, based on the accessed mobile usage data, a second set of data representing information about accesses to the resources by mobile devices with non-persistent beacon cookies, wherein the non-persistent beacon cookies were present for a duration that does not meet the persistence threshold;

determining one or more adjustment factors based on the first set of data;

determining a count of unique visitors accessing the set of resources from mobile devices based, at least in part, on a count of accesses by mobile devices with persistent beacon cookies and a count of accesses by mobile devices with non-persistent beacon cookies adjusted by the adjustment factors.

24. A computer-implemented method, comprising:

accessing mobile usage data representing the access of a set of resources on a network by client devices classified as mobile devices, the mobile usage data including information received from client systems sent as a result of beacon instructions included with the set of resources;

identifying a set of cookie usage data representing access by mobile devices with beacon cookies associated with the beacon instructions;

identifying a set of non-cookie usage data representing access by mobile devices without the beacon cookies;

determining a cookie count of unique visitors based on the cookie usage data;

determining a non-cookie count of unique visitors based on the non-cookie usage data and the cookie usage data; and

aggregating the cookie count and the non-cookie count to determine a total count of unique visitors for mobile devices.

25. A computer-implemented method, comprising:

accessing usage data representing the access of a set of resources on a network by client devices, the usage data including information received from client systems sent as a result of beacon instructions included with the set of resources;

determining, based on the accessed usage data, a set of shared use data representing access of the network resources by client devices classified as shared use devices;

determining, based on the set of shared use data, a first set of shared use data representing access of the network resources by shared use devices sharing a first common location;

determining, based on the set of shared use data, a second set of shared use data representing access of the network resources by shared use devices sharing a second common location;

estimating a number of shared use devices and a number of persons at the first common location based on the first set of shared use data;

determining a first count of unique visitors to the network resources from shared use devices at the first common location based on the estimated number of shared use devices and number of persons at the first common location;

estimating a number of shared use devices and a number of persons at the second common location based on the second set of shared use data;

determining a second count of unique visitors to the network resources from shared use devices at the second common location based on the estimated number of shared use devices and number of persons at the second common location; and

determining a count of unique visitors accessing the network resources by shared use devices based, at least in part, on the aggregation of the first and second counts.