ANALYZING CONSUMER BEHAVIOR USING ELECTRONICALLY-CAPTURED CONSUMER LOCATION DATA

• US 20110099046A1
• Filed: 10/22/2010
• Published: 04/28/2011
• Est. Priority Date: 10/23/2009
• Status: Active Grant

• Current Assignee: Service Management Group Incorporated
• Original Assignee: Cadio, Inc.
• Inventors: Fulford-Jones, Thaddeus R.F., Volpe, Andrew, Weiss, Eric H.
• Granted Patent: US 9,165,304 B2
• Time in Patent Office: Days
• US Class Current: 705/7.31
• 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 a 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;

analyzing the location data to identify a plurality of paths, each path being a path followed by a consumer of the plurality of consumers and comprising at least two settings visited by the consumer during a trip;

based at least in part on the at least two settings in each of the plurality of paths, producing inferences and/or predictions regarding the plurality of consumers.

2. The method of claim 1, wherein, for each of the plurality of paths, a setting of the at least two settings comprises an origin of the trip, the origin being one of a home or an office of the consumer.

3. The method of claim 1, wherein, for each of the plurality of paths, a first setting of the at least two settings comprises a first retail establishment visited during the trip and a second setting of the at least two settings comprises a second retail establishment visited during the trip.

4. The method of claim 1, wherein determining at least two settings visited by the consumer comprises comparing the at least two pieces of location data to information regarding locations of settings and, when a match is detected between a piece of location data and a location of a setting, determining that the consumer visited the setting.

5. The method of claim 4, wherein receiving the at least two pieces of location data comprises receiving a physical location and a time the physical location was determined for each piece of location data, and

wherein comparing the at least two pieces of location data to information regarding locations of settings comprises comparing times at which the at least two pieces of location data were obtained to schedules associated with a physical location to determine a setting visited by the consumer.

6. The method of claim 5, wherein when a piece of location data matches a location of a first setting and a second setting, matching the piece of location data to the first setting when the piece of location data is obtained at a first time and matching the piece of location data to the second setting when the piece of location data is obtained at a second time.

7. The method of claim 4, wherein comparing the at least two pieces of location data to information regarding locations of settings comprises:

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; and

when a match is detected between the single location and the location of the setting, determining that the consumer visited the setting.

8. The method of claim 7, wherein the set of acts further comprises:

calculating properties for a cluster based at least in part on the multiple pieces of location data included in the cluster, wherein the calculating comprises performing a mathematical operation on the multiple pieces of location data to determine a cluster location for the cluster,

wherein a match is detected between the single location and the location of the point of interest when the cluster location matches the location of the point of interest.

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

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 a set of personally-relevant settings.

10. The method of claim 9, 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.

11. The method of claim 9, 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.

12. The method of claim 9, wherein identifying the path comprises identifying the at least two endpoints of the path by comparing the at least two pieces of location data to the set of personally-relevant settings and, when at least one personally-relevant setting is identified in the at least two pieces of location data, identifying the at least one personally-relevant setting as an endpoint of the path and identifying the path as a sequence of settings visited by the consumer between the at least two endpoints.

13. The method of claim 1, wherein identifying the path comprises identifying types of settings in the path and, based at least in part on the types, determining a purpose of the consumer for the path.

14. The method of claim 1, wherein producing the inferences and/or predictions comprises producing the inferences and/or predictions based at least in part on profile data for the plurality of consumers.

15. The method of claim 14, wherein the method further comprises, for each first consumer of the plurality of consumers for which a first path is identified, generating the profile data for the first consumer by analyzing the first path.

16. The method of claim 14, wherein the method further comprises generating results of a study relating to an organization, wherein the results comprise the inferences and/or predictions, and

wherein producing the inferences and/or predictions comprises identifying behaviors of the plurality of consumers relating to the organization.

17. At least one storage medium encoded with computer-executable instructions that, when executed by a computer, cause the computer to carry out a method for performing consumer analytics for a plurality of consumers, the method comprising:

for each consumer of a plurality of consumers;

receiving at least two pieces of location data, each piece of location data identifying a physical location for the consumer,

determining, from the at least two pieces of location data, at least one setting visited by the consumer,

determining profile data comprising at least one characteristic for the consumer based on the at least one setting; and

based at least in part on profile data for each of the plurality of consumers, producing inferences and/or predictions regarding the plurality of consumers.

18. The at least one storage medium of claim 17, wherein determining at least one characteristic for a first consumer of the plurality of consumers based on the at least one setting comprises determining at least one pattern in information regarding the at least one setting.

19. The at least one storage medium of claim 18, wherein the at least one pattern is a pattern from a group consisting of patterns in repeated visits to a particular setting separated by at least one visit to another setting, lengths of visits to the particular setting, frequency of visits to the particular setting, frequency of visits to a particular type of setting, settings that are visited by the first consumer close together in time, and the first consumer'"'"'s daily routine based at least in part on the places visited by the consumer.

20. The at least one storage medium of claim 18, wherein the method further comprises generating profile data for the first consumer by analyzing the at least one setting, the profile data comprising the at least one characteristic for the first consumer.

21. The at least one storage medium of claim 20, wherein the method further comprises generating results of a study relating to an organization, wherein the results comprise the inferences and/or predictions, and

wherein producing the inferences and/or predictions comprises determining characteristics of the plurality of consumers with respect to the organization.

22. The at least one storage medium of claim 18, wherein determining the at least one characteristic for the first consumer comprises determining, based at least in part on the at least one setting, a behavior group for the first consumer.

23. The at least one storage medium of claim 22, wherein determining the behavior group comprises comparing the at least one characteristic for the first consumer to at least one condition for the behavior group and, if the at least one characteristic satisfies the at least one condition, identifying the first consumer as a part of the behavior group.

24. The at least one storage medium of claim 17, wherein the method further comprises generating results of a study relating to an organization, wherein the results comprise the inferences and/or predictions, and

wherein producing the inferences and/or predictions comprises determining how consumers will react given a set of conditions for the study.