Measuring retail visitation amounts based on locations sensed by mobile devices

• US 10,679,231 B2
• Filed: 09/01/2018
• Issued: 06/09/2020
• Est. Priority Date: 02/18/2013
• Status: Active Grant

• Current Assignee: PlaceIQ, Inc.
• Original Assignee: PlaceIQ, Inc.
• Inventors: Milton, Stephen, McCall, Duncan
• Primary Examiner(s): Rotaru, Octavian
• Application Number:
  US16/120,250
• Publication Number:
  US 20190130426A1
• Time in Patent Office: 647 Days
• Field of Search: None
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements

Abstract

Provided is a computer-implemented method that includes obtaining a user location dataset indicative of a location visited by a user, the user location dataset being based at least in part on a location signal received from a mobile computing device associated with the user, determining that the location visited corresponds to a geographic location of a place of interest, determining, using a computer, a visit count corresponding to the location dataset, adjusting a visit count for the place of interest to include the visit count corresponding to the location dataset, and storing the adjusted visit count for the place of interest in a visitation rate datastore.

Claims

1. A computer implemented method utilizing a global positioning system and cellular towers to indicate locations of mobile devices that use the indicated locations to provide location histories for estimating visitation rates at a brick-and-mortar store, the computer implemented method comprising:

receiving, with the mobile devices, from the global positioning system and from the cellular towers, signals indicative of the locations of the mobile devices;

forming, based on the indicated locations, the location histories of the mobile devices, the location histories including;

a specific date and time, a type of each of the mobile devices, a specific operating system employed by each of the mobile devices, and an identifier of a specific mobile application of the brick-and-mortar store employed by each of the mobile devices;

assigning a higher confidence score to each of at least some of the locations based on signals from the global positioning system;

assigning a lower confidence score to each of at least some other of the locations based on signals from the cellular towers;

identifying a boundary for the brick-and-mortar store by defining a polygonal boundary area associated with the brick-and-mortar store;

determining, based on the formed location histories, a number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store during a day of a week corresponding to an advertisement of the brick-and-mortar store in an advertising region of the brick-and-mortar store;

obtaining, from a population profile, a ratio of undetected and detected visitors of a specific gender, a specific age, using the specific mobile device type, the specific operating system, and the specific mobile application of the brick-and-mortar store; and

estimating, by one or more processors, the visitation rates for the brick-and-mortar store, by mathematically multiplying the number of the mobile devices from the location histories by the ratio; and

storing the estimated visitation rates in a memory.

2. The method of claim 1, further comprising:

identifying a location history of a given one of the mobile devices by indicating a plurality of time-stamped geographic locations sensed by the given mobile device associated with a given user;

wherein each of the respective ones of the plurality of time-stamped geographic locations indicates a location of the mobile device and a given date and time associated with the location of the mobile device.

3. The method of claim 2, further comprising:

identifying for the given mobile device having the location history;

a given type of the given mobile device,

a given operating system of the given mobile device, and

the identifier of the specific mobile application of the brick-and-mortar store employed by the given mobile device.

4. The method of claim 1, wherein identifying a boundary comprises:

identifying a plurality of centroids corresponding to a plurality of different places, the plurality of different places including the brick-and-mortar store;

identifying boundary polygons for the plurality of different places based at least in part on the plurality of centroids; and

storing, in the memory, for each of the plurality of different places, a corresponding polygonal boundary that bounds a respective geographic area.

5. The method of claim 1, wherein determining, based on the formed location histories, a number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store comprises:

determining whether a location history of a given one of the mobile devices determined to be in the polygonal boundary area is associated with a location characteristic; and

in response to determining that the location history of the given mobile device is associated with the location characteristic, identifying a user profile based on the location characteristic, wherein the user profile is indicative of a specific gender of a user, a specific age of the user, a type of mobile device of the user, an operating system of the mobile device of the user, and the specific mobile application of the brick-and-mortar store employed by the mobile device of the user.

6. The method of claim 5, wherein the user profile is a personal profile that is unique to the user, and the population profile is associated with demographics of a plurality of different persons including users of the specific mobile application, or both the users and non-users of the specific mobile application.

7. The method of claim 5, wherein estimating the visitation rates for the brick-and-mortar store comprises:

determining a visitation count for the brick-and-mortar store during the day of the week; and

adjusting the visitation count to include a current visit count corresponding to the brick-and-mortar store determined based on other locations histories or other timeframes.

8. The method of claim 1, further comprising:

indicating, with the location histories, a type of a given mobile device among other types of mobile devices;

indicating, with the location histories, a specific operating system or version thereof employed by the given mobile device among other operating systems or versions thereof;

identifying, with the location histories, the specific mobile application of the brick and mortar stored employed by the given mobile device; and

assigning, amongst the locations of the given mobile device, at least one of the higher confidence scores and at least one of the lower confidence scores by determining respective confidence scores for the respective ones of the locations.

9. The method of claim 1, wherein:

the ratio of undetected and detected visitors is based on numbers of mobile devices of users reporting respective location histories relative to other users or persons for which their respective mobile devices are not reporting locations histories to estimate numbers of persons having visited the brick-and-mortar store based on numbers of mobile devices detected in the polygonal boundary area associated with the brick-and-mortar store.

10. The method of claim 1, wherein an indicated location is a time-stamped geographic location indicative of a physical location of a mobile device, as determined by the mobile device based on one or more signals indicative of respective locations, at a specific time identified by a time-stamp.

11. The method of claim 10, wherein the time-stamped geographic location is associated with a specific date indicative of a given day of a given calendar year.

12. The method of claim 10, wherein the geographic location comprises a geographic area.

13. The method of claim 1, further comprising:

transmitting one or more content items corresponding to the advertisement of the brick-and-mortar store to a given mobile device associated with the advertising region for presentation to a user of the given mobile device;

identifying a received location of the given mobile device at a specific time or over a period of time when at least one of the content items is displayed by the mobile device; and

determining a visitation effectiveness for a geographic area within the advertising region based at least in part on the received location.

14. The method of claim 13, wherein the visitation effectiveness is based at least in part on identification of the given mobile device of the user within the number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store after the display of at least one of the content items by the given mobile device.

15. The method of claim 13 further comprising:

transmitting at least one of the content items to other mobile devices of other users in the geographic area or advertising region based at least in part on identification of the given mobile device of the user within the number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store.

16. The method of claim 13, wherein the geographic area is associated with a given radius from the brick-and-mortar store that includes the received location.

17. The method of claim 13, wherein the geographic area comprises a geographic tile including the received location or is a geographic tile including the received location.

18. The method of claim 17, further comprising:

identifying a demographic for the tile;

identifying a different geographic area having a same or similar demographic as the tile; and

transmitting at least one of the content items to other mobile devices of other users within the different geographic area based at least in part on identification of the given mobile device of the user within the number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store.

19. A non-transitory computer-readable medium storing computer program instructions for processing indicated locations of mobile devices configured to utilize a global positioning system and cellular towers to provide location histories for estimating visitation rates at a brick-and-mortar store, the computer program instructions, when executed by one or more processors, effectuating operations comprising:

receiving, with the mobile devices, from the global positioning system and from the cellular towers, signals indicative of the locations of the mobile devices;

forming, based on the indicated locations, the location histories of the mobile devices, the location histories including;

a specific date and time, a type of each of the mobile devices, a specific operating system employed by each of the mobile devices, and an identifier of a specific mobile application of the brick-and-mortar store employed by each of the mobile devices;

assigning a higher confidence score to each of at least some of the locations based on signals from the global positioning system;

assigning a lower confidence score to each of at least some other of the locations based on signals from the cellular towers;

identifying a boundary for the brick-and-mortar store by defining a polygonal boundary area associated with the brick-and-mortar store;

determining, based on the formed location histories, a number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store during a day of a week corresponding to an advertisement of the brick-and-mortar store in an advertising region of the brick-and-mortar store;

obtaining, from a population profile, a ratio of undetected and detected visitors of a specific gender, a specific age, using the specific mobile device type, the specific operating system, and the specific mobile application of the brick-and-mortar store; and

estimating the visitation rates for the brick-and-mortar store, by mathematically multiplying the number of the mobile devices from the location histories by the ratio; and

storing the estimated visitation rates in a memory.

20. The non-transitory computer-readable medium of claim 19, wherein the computer program instructions, when executed by the one or more processors, further effectuate operations comprising:

identifying a location history of a given one of the mobile devices by indicating a plurality of time-stamped geographic locations sensed by the given mobile device associated with a given user;

wherein each of the respective ones of the plurality of time-stamped geographic locations indicates a location of the mobile device and a given date and time associated with the location of the mobile device.

21. The non-transitory computer-readable medium of claim 20, wherein the computer program instructions, when executed the by one or more processors, further effectuate operations comprising:

identifying for the given mobile device having the location history;

a given type of the given mobile device,

a given operating system of the given mobile device, and

the identifier of the specific mobile application of the brick-and-mortar store employed by the given mobile device.

22. The non-transitory computer-readable medium of claim 19, wherein identifying a boundary comprises:

identifying a plurality of centroids corresponding to a plurality of different places, the plurality of different places including the brick-and-mortar store;

identifying boundary polygons for the plurality of different places based at least in part on the plurality of centroids; and

storing, in the memory, for each of the plurality of different places, a corresponding polygonal boundary that bounds a respective geographic area.

23. The non-transitory computer-readable medium of claim 19, wherein determining, based on the formed location histories, a number of the mobile devices in the polygonal boundary area associated with the brick-and-mortar store comprises:

determining whether a location history of a given one of the mobile devices determined to be in the polygonal boundary area is associated with a location characteristic; and

in response to determining that the location history of the given mobile device is associated with the location characteristic, identifying a user profile based on the location characteristic, wherein the user profile is indicative of a specific gender of a user, a specific age of the user, a type of mobile device of the user, an operating system of the mobile device of the user, and the specific mobile application of the brick-and-mortar store employed by the mobile device of the user.

24. The non-transitory computer-readable medium of claim 23, wherein the user profile is a personal profile that is unique to the user, and the population profile is associated with demographics of a plurality of different persons including users of the specific mobile application, or both the users and non-users of the specific mobile application.

25. The non-transitory computer-readable medium of claim 19, wherein:

the ratio of undetected and detected visitors is based on numbers of mobile devices of users reporting respective location histories relative to other users or persons for which their respective mobile devices are not reporting locations histories to estimate numbers of persons having visited the brick-and-mortar store based on numbers of mobile devices detected in the polygonal boundary area associated with the brick-and-mortar store.

26. The non-transitory computer-readable medium of claim 19, wherein:

an indicated location is a time-stamped geographic location indicative of a physical location of a mobile device, as determined by the mobile device based on one or more signals indicative of respective locations, at a specific time identified by a time-stamp, and wherein, the time-stamped geographic location is associated with a specific date indicative of a given day of a given calendar year.

27. The non-transitory computer-readable medium of claim 26, wherein the geographic location comprises a geographic area.

28. The non-transitory computer-readable medium of claim 19, wherein the computer program instructions, when executed the by one or more processors, further effectuate operations comprising:

indicating, with the location histories, a type of a given mobile device among other types of mobile devices;

indicating, with the location histories, a specific operating system or version thereof employed by the given mobile device among other operating systems or versions thereof;

identifying, with the location histories, the specific mobile application of the brick and mortar stored employed by the given mobile device; and

assigning, amongst the locations of the given mobile device, at least one of the higher confidence scores and at least one of the lower confidence scores by determining respective confidence scores for the respective ones of the locations.