Analyzing consumer behavior using electronically-captured consumer location data

• US 9,165,304 B2
• Filed: 10/22/2010
• Issued: 10/20/2015
• Est. Priority Date: 10/23/2009
• Status: Active Grant

• Current Assignee: Service Management Group Incorporated
• Original Assignee: Service Management Group, Inc.
• Inventors: Weiss, Eric H., Volpe, Andrew, Fulford-Jones, Thaddeus R. F.
• Primary Examiner(s): Choi, Peter
• Assistant Examiner(s): HATCHER, DEIRDRE D
• Application Number:
  US12/910,311
• Publication Number:
  US 20110099046A1
• Time in Patent Office: 1,824 Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

In embodiments, methods and systems for consumer behavior analysis using electronically-captured consumer location data may be provided. The location data may be gathered for one or more consumers. The gathered data may be analyzed to determine behavior patterns or other characteristics of the one or more consumers. Further, inferences or predictions about consumers may be derived based on the characteristics. The inferences and predictions may be the basis of consumer analytics supplied to a business or other entity.

Claims

1. A method for performing consumer analytics based on location data for consumers of a plurality of consumers, wherein the location data comprises geographic location data determined using Global Positioning System (GPS) and/or Assisted GPS (AGPS) techniques implemented by mobile devices operated by the plurality of consumers, the method comprising:

operating at least one programmed processor to perform a set of acts, the at least one programmed processor being programmed with executable instructions identifying the set of acts, the set of acts comprising;

identifying, in the location data, a plurality of paths taken by the plurality of consumers, each path comprising a route traveled by a consumer of the plurality of consumers and comprising at least two settings visited by the consumer during the path, wherein the at least two settings of each path comprise a starting endpoint and a finishing endpoint of the path and wherein the starting endpoint and finishing endpoint for each path are personally-relevant locations for the consumer who traveled the path, and wherein identifying the plurality of paths in the location data comprises identifying in the location data visits by the plurality of consumers to personally-relevant locations for those consumers and identifying the plurality of paths based on the identified personally-relevant locations;

analyzing the plurality of paths, wherein the analyzing comprises;

identifying, in the plurality of paths, a first path followed by a first consumer that comprises a first route that passes a first location of a first business that the first consumer passed by but did not visit during the first path and comprises a competitor to the first business that the consumer visited during the first path;

generating context information indicating a context in which the first consumer passed by but did not visit the first business, the context including that the first consumer visited the competitor to the first business during the first path in which the first consumer passed by but did not visit the first business, the context information comprising a distance along the first route from a first starting endpoint of the first path to an anchor identified in the first path and including an identification of the at least two settings visited by the first consumer in the first path that included the visit to the competitor of the first business; and

for a group of consumers who are potential customers of the first business, predicting one or more behaviors in which consumers of the group of consumers may engage when visiting the first business in the future, wherein the predicting comprises predicting the one or more behaviors based at least in part on the context information indicating the context in which the first consumer passed by but did not visit the first business during the first path;

receiving, from a mobile device operated by a second consumer, location data for the second consumer determined by the mobile device using GPS and/or AGPS techniques;

in response to determining from a real-time analysis of the location data that the second consumer is presently engaged in at least one of the one or more behaviors, distributing to the mobile device operated by the second consumer at least one message relating to the first business,

wherein identifying, in the location data, the plurality of paths further comprises identifying, from the location data, settings visited by the plurality of consumers, wherein determining each one setting of the settings comprises, for first location data for a consumer who visited the one setting;

determining, for each one candidate setting of one or more candidate settings, a first probability of a match between the first location data and the one candidate setting based at least in part on comparing the first location data for the consumer to information regarding a location of the one candidate setting;

determining, for each one candidate setting of the one or more candidate settings, a second probability of a match based on previously-detected behaviors of the consumer who visited the one setting and/or of other consumers; and

identifying the one setting as one of the one or more candidate settings based on the first probability and the second probability,

wherein the set of acts further comprises receiving the first location data, the receiving comprising receiving, for each piece of location data of the first location data, a physical location and a time the physical location was determined, and

wherein determining the first probability based at least in part on comparing the first location data to information regarding the location of the one candidate setting comprises determining the first probability based at least in part on comparing times at which each piece of the first location data was determined to schedules associated with a location of each one candidate setting of the one or more candidate settings.

2. The method of claim 1, wherein comparing the first location data to locations of one or more candidate settings comprises:

clustering multiple pieces of the first location data that are close in time and space to identify a cluster, the cluster comprising a single location to which the multiple pieces of first location data relates; and

comparing the single location and the location of each one candidate setting of the one or more candidate settings.

3. The method of claim 1, wherein the set of acts further comprises:

determining a set of personally-relevant settings for a consumer of the plurality of consumers at least in part by;

clustering multiple pieces of location data that are close in time and space to identify a single location to which the multiple pieces of location data relate;

evaluating at least times that the multiple pieces of location data were obtained and lengths of time the consumer spent at the single location to determine whether the single location is a personally-relevant setting for the consumer; and

when the single location is a personally-relevant setting for the consumer, adding the single location to the set of personally-relevant settings.

4. The method of claim 3, wherein the evaluating comprises determining whether the consumer spends more than a threshold period of time in a location during night hours to determine whether the single location is a place of residence for the consumer.

5. The method of claim 3, wherein the evaluating comprises determining whether the consumer spends more than a threshold period of time in a location during day hours to determine whether the single location is a place of employment for the consumer.

6. The method of claim 1, wherein:

analyzing the plurality of paths comprises, for each one path of at least some paths of the plurality of paths, analyzing information regarding the at least two settings visited during the one path to infer a purpose of a consumer, who traveled the one path, in traveling the one path,

wherein generating the context information indicating the context in which the consumer passed by but did not visit the first business comprises generating context information comprising a purpose of the first consumer for the first path inferred as a result of the analyzing to infer a purpose, and

predicting the one or more behaviors of the group of potential customers of the first business comprises predicting one or more purposes for paths that the group of potential customers may take that include a visit to the first business, based at least in part on the purpose of the first consumer for the first path.

7. The method of claim 6, wherein predicting the one or more behaviors in which consumers of the group of consumers may engage when visiting the first business in the future comprises determining a likelihood that consumers of the group of consumers will visit the first business during paths that have the inferred purpose for the first path.

8. The method of claim 7, wherein:

the method further comprises adjusting, based on the context information, a stored likelihood that a consumer who travels a path having the inferred purpose for the first path would also visit a first type of setting, the first business and the competitor being of the first type of setting; and

determining the likelihood that consumers of the group of consumers will visit the first business during paths that have the inferred purpose for the first path comprises determining the likelihood based at least in part on the stored likelihood.

9. The method of claim 7, wherein:

generating the context information indicating the context in which the first consumer passed by but did not visit the first business comprises identifying at least one setting visited by the first consumer during the first path; and

the method further comprises adjusting, based on the context information, a likelihood that a consumer who visits the at least one setting would also visit a first type of setting, the first business and the competitor being of the first type of setting.

10. The method of claim 9, wherein:

adjusting the likelihood comprises increasing the likelihood; and

determining the likelihood that consumers of the group of consumers will visit the first business during paths that have the inferred purpose for the first path comprises determining the likelihood based at least in part on the probability that a consumer who visits the at least one setting would also visit the first type of setting.

11. The method of claim 1, wherein predicting the one or more behaviors for the group comprises predicting based at least in part on profile data for the plurality of consumers.

12. The method of claim 11, wherein the method further comprises, for each third consumer of the plurality of consumers for which a second path is identified, generating the profile data for the third consumer by analyzing the second path.

13. The method of claim 11, wherein the method further comprises generating results of a study relating to the first business, wherein the results comprise the one or more behaviors for the group produced in the predicting.

14. The method of claim 1, wherein:

analyzing the plurality of paths further comprises;

identifying a first set of paths, of the plurality of paths, where each path of the first set of paths does not include a visit to the first business, comprises a route that passes the location of the first business, and includes to the competitor; and

identifying a pattern in the first set of paths of the plurality of paths, the pattern relating to a similarity in context in which consumers who traveled the first set of paths passed by but did not visit the first business and visited the competitor; and

predicting the one or more behaviors for the group based at least in part on the information indicating the context in which the consumer passed by but did not visit the first business comprises predicting that consumers of the group will visit the first business in the future in accordance with the pattern identified in the first set of paths.

15. The method of claim 14, wherein:

identifying the pattern comprises identifying a frequency with which consumers who traveled the first set of paths passed by but did not visit the first business and visited the competitor; and

predicting that consumers of the group will visit the first business in the future in accordance with the pattern comprises predicting that consumers of the group will visit the first business with the frequency.

16. The method of claim 1, wherein:

analyzing the plurality of paths further comprises;

identifying a first set of paths, of the plurality of paths, where each path of the first set of paths does not include a visit to the first business and comprises a route that passes the location of the first business, the first set of paths including the first path; and

identifying, in the first set of paths of the plurality of paths, one or more settings and/or one or more types of setting visited by consumers in the first set of paths, the one or more settings including the at least two settings visited by the consumer in the first path and/or the one or more types of settings including a type of the at least two settings visited by the consumer in the first path; and

predicting the one or more behaviors for the group based at least in part on the context information indicating the context in which the consumer passed by but did not visit the first business comprises predicting that consumers of the group will visit the first business in the future in paths that include visits to the one or more settings and/or one or more types of setting.

17. The method of claim 1, wherein:

analyzing the plurality of paths further comprises;

identifying a first set of paths, of the plurality of paths, where each path of the first set of paths does not include a visit to the first business and comprises a route that passes the location of the first business; and

identifying, in the first set of paths of the plurality of paths, one or more settings and/or one or more types of setting not visited by consumers in the first set of paths; and

predicting the one or more behaviors for the group based at least in part on the context information indicating the context in which the consumer passed by but did not visit the first business during the first path in which the consumer visited the competitor comprises predicting that consumers of the group will visit the first business in the future in paths that do not include visits to the one or more settings and/or one or more types of setting.

18. The method of claim 1, wherein:

generating the context information indicating the context in which the first consumer passed by but did not visit the first business comprises identifying a personally-relevant location to which the first consumer was traveling during the first path in which the first consumer passed by the first business and visited the competitor; and

predicting the one or more behaviors for the group based at least in part on the context information indicating the context in which the consumer passed by but did not visit the first business comprises predicting the one or more behaviors for the group based at least in part on the personally-relevant location to which the first consumer was traveling during the first path in which the consumer passed by the first business and visited the competitor.

19. The method of claim 1, wherein:

generating the context information indicating the context in which the first consumer passed by but did not visit the first business comprises identifying a behavior in which the first consumer was engaged during the first path in which the first consumer passed by the first business and visited the competitor; and

based at least in part on the context information indicating the context in which the consumer passed by but did not visit the first business comprises producing the inferences and/or predictions based at least in part on the behavior in which the first consumer was engaged during the first path in which the consumer passed by the first business and visited the competitor.

20. The method of claim 1, wherein determining the purpose of the first consumer for the first path comprises identifying that the first consumer was shopping for a particular product or that the first consumer was on a general shopping trip when the first consumer passed by but did not visit the first location of the first business.

21. The method of claim 1, wherein predicting the one or more behaviors of the group of consumers who are potential customers of the first business comprises predicting one or more behaviors of potential new customers of the first business.

22. The method of claim 1, wherein predicting the one or more behaviors in which consumers of the group of consumers may engage when visiting the first business in the future comprises determining a likelihood that consumers of the group of consumers will travel at least the distance to visit the first business.

23. The method of claim 1, wherein:

the anchor in the first path is the competitor; and

generating the context information comprising the distance along the first route from the endpoint to the anchor comprises generating the context information comprises the distance along the first route from the endpoint to the competitor.

24. The method of claim 1, wherein the second consumer is one of the plurality of consumers.

25. At least one non-transitory computer-readable storage medium having encoded thereon executable instructions that, when executed by at least one processor, cause the at least one processor carry out a method for performing consumer analytics based on location data for consumers of a plurality of consumers, wherein the location data comprises geographic location data determined using Global Positioning System (GPS) and/or Assisted GPS (AGPS) techniques implemented by mobile devices operated by the plurality of consumers, the method comprising:

identifying, in the location data, a plurality of paths taken by the plurality of consumers, each path comprising a route traveled by a consumer of the plurality of consumers and comprising at least two settings visited by the consumer during the path, wherein the at least two settings of each path comprise a starting endpoint and a finishing endpoint of the path and wherein the starting endpoint and finishing endpoint for each path are personally-relevant locations for the consumer who traveled the path, and wherein identifying the plurality of paths in the location data comprises identifying in the location data visits by the plurality of consumers to personally-relevant locations for those consumers and identifying the plurality of paths based on the identified personally-relevant locations;

analyzing the plurality of paths, wherein the analyzing comprises;

identifying, in the plurality of paths, a first path followed by a first consumer that comprises a first route that passes a first location of a first business that the first consumer passed by but did not visit during the first path and comprises a competitor to the first business that the consumer visited during the first path;

generating context information indicating a context in which the first consumer passed by but did not visit the first business, the context including that the first consumer visited the competitor to the first business during the first path in which the first consumer passed by but did not visit the first business, the context information comprising a distance along the first route from a first starting endpoint of the first path to an anchor identified in the first path and including an identification of the at least two settings visited by the first consumer in the first path that included the visit to the competitor of the first business; and

for a group of consumers who are potential customers of the first business, predicting one or more behaviors in which consumers of the group of consumers may engage when visiting the first business in the future, wherein the predicting comprises predicting the one or more behaviors based at least in part on the context information indicating the context in which the first consumer passed by but did not visit the first business during the first path,

receiving, from a mobile device operated by a second consumer, location data for the second consumer determined by the mobile device using GPS and/or AGPS techniques;

in response to determining from a real-time analysis of the location data that the second consumer is presently engaged in at least one of the one or more behaviors, distributing to the mobile device operated by the second consumer at least one message relating to the first business,

wherein identifying, in the location data, the plurality of paths further comprises identifying, from the location data, settings visited by the plurality of consumers, wherein determining each one setting of the settings comprises, for first location data for a consumer who visited the one setting;

determining, for each one candidate setting of one or more candidate settings, a first probability of a match between the first location data and the one candidate setting based at least in part on comparing the first location data for the consumer to information regarding a location of the one candidate setting;

determining, for each one candidate setting of the one or more candidate settings, a second probability of a match based on previously-detected behaviors of the consumer who visited the one setting and/or of other consumers; and

identifying the one setting as one of the one or more candidate settings based on the first probability and the second probability,

wherein the set of acts further comprises receiving the first location data, the receiving comprising receiving, for each piece of location data of the first location data, a physical location and a time the physical location was determined, and

wherein determining the first probability based at least in part on comparing the first location data to information regarding the location of the one candidate setting comprises determining the first probability based at least in part on comparing times at which each piece of the first location data was determined to schedules associated with a location of each one candidate setting of the one or more candidate settings.

26. An apparatus comprising:

at least one processor; and

at least one computer-readable storage medium having encoded thereon executable instructions that, when executed by the at least one processor, cause the at least one processor carry out a method for performing consumer analytics based on location data for consumers of a plurality of consumers, wherein the location data comprises geographic location data determined using Global Positioning System (GPS) and/or Assisted GPS (AGPS) techniques implemented by mobile devices operated by the plurality of consumers, the method comprising;

identifying, in the location data, a plurality of paths taken by the plurality of consumers, each path comprising a route traveled by a consumer of the plurality of consumers and comprising at least two settings visited by the consumer during the path, wherein the at least two settings of each path comprise a starting endpoint and a finishing endpoint of the path and wherein the starting endpoint and finishing endpoint for each path are personally-relevant locations for the consumer who traveled the path, and wherein identifying the plurality of paths in the location data comprises identifying in the location data visits by the plurality of consumers to personally-relevant locations for those consumers and identifying the plurality of paths based on the identified personally-relevant locations; and

analyzing the plurality of paths, wherein the analyzing comprises;

identifying, in the plurality of paths, a first path followed by a first consumer that comprises a first route that passes a first location of a first business that the first consumer passed by but did not visit during the first path and comprises a competitor to the first business that the consumer visited during the first path;

generating context information indicating a context in which the first consumer passed by but did not visit the first business, the context including that the first consumer visited the competitor to the first business during the first path in which the first consumer passed by but did not visit the first business, the context information comprising a distance along the first route from a first starting endpoint of the first path to an anchor identified in the first path and including an identification of the at least two settings visited by the first consumer in the first path that included the visit to the competitor of the first business; and

for a group of consumers who are potential customers of the first business, predicting one or more behaviors in which consumers of the group of consumers may engage when visiting the first business in the future, wherein the predicting comprises predicting the one or more behaviors based at least in part on the context information indicating the context in which the first consumer passed by but did not visit the first business during the first path;

receiving, from a mobile device operated by a second consumer, location data for the second consumer determined by the mobile device using GPS and/or AGPS techniques;

in response to determining from a real-time analysis of the location data that the second consumer is presently engaged in at least one of the one or more behaviors, distributing to the mobile device operated by the second consumer at least one message relating to the first business,

wherein identifying, in the location data, the plurality of paths further comprises identifying, from the location data, settings visited by the plurality of consumers, wherein determining each one setting of the settings comprises, for first location data for a consumer who visited the one setting;

determining, for each one candidate setting of one or more candidate settings, a first probability of a match between the first location data and the one candidate setting based at least in part on comparing the first location data for the consumer to information regarding a location of the one candidate setting;

determining, for each one candidate setting of the one or more candidate settings, a second probability of a match based on previously-detected behaviors of the consumer who visited the one setting and/or of other consumers; and

identifying the one setting as one of the one or more candidate settings based on the first probability and the second probability,

wherein the method further comprises;

collecting, with the mobile devices operated by the plurality of consumers, the location data, wherein collecting the location data comprises, with each mobile device of the mobile devices, sampling location according to a time interval with which the mobile device is configured, and wherein sampling location according to the time interval comprises adjusting the time interval based at least in part on a proximity of the mobile device to a setting.