Deal scheduling based on user location predictions

• US 9,009,093 B1
• Filed: 10/04/2012
• Issued: 04/14/2015
• Est. Priority Date: 10/04/2012
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Omoigui, Nosakhare D.
• Primary Examiner(s): CHEN, ALAN S
• Application Number:
  US13/645,268
• Time in Patent Office: 922 Days
• Field of Search: None
• US Class Current: 706/45
• CPC Class Codes: G06K 15/002 Interacting with the operator
• 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: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/0272 Period of advertisement exposure
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal

Abstract

Techniques for scheduling deals based on predicted user locations are described herein. In particular implementations, a service provider may aggregate location information for multiple users. In one example, the location information comprises check-in information shared by a user through a location-based service. The service provider may utilize the aggregated location information to predict future user traffic for a geographical region associated with a deal or a merchant of the deal. In some instance, the deal comprises an offer of an item for acquisition at a discounted value for a period of time, such as a period of hours, days, or weeks. The predicted user traffic may be used by the service provider, merchant, or others to identify the time period and discounted value at which to offer the deal for acquisition.

Claims

1. A method comprising:

under control of one or more computing systems configured with specific executable instructions,

obtaining location information for each of a plurality of users, each piece of the location information indicating a location shared by one of the plurality of users through a location-based service;

generating user traffic information from the location information of the plurality of users, the user traffic information indicating locations where the plurality of users were previously located;

identifying a deal to be offered for acquisition in a geographical region, the deal comprising an offer of an item for acquisition at a discounted value;

predicting future user traffic for the geographical region based on the user traffic information;

determining that the future user traffic satisfies one or more criteria during a time period;

determining an amount of discount to apply to the deal when offering the deal for acquisition based at least in part on the future user traffic; and

offering the deal for acquisition in the geographical region during the time period.

2. The method of claim 1, wherein each piece of the location information comprises check-in information indicating a venue at which one or more of the plurality of users is located.

3. The method of claim 1, wherein each piece of the location information comprises real-time data indicating a current location of one or more of the plurality of users.

4. The method of claim 1, wherein:

the future user traffic indicates an estimated number of users that will be located within the geographical region; and

determining that the future user traffic satisfies one or more criteria comprises determining that the estimated number of users is greater than a threshold.

5. The method of claim 4, wherein the amount of discount to apply to the deal is based at least in part on the estimated number of users indicated by the future user traffic.

6. The method of claim 5, wherein determining the amount of discount comprises determining a first amount of discount when the estimated number of individuals is less than another threshold and determining a second amount of discount when the estimated number of individuals is greater than or equal to the other threshold, the second amount of discount being less than the first amount of discount.

7. A system comprising:

one or more processors;

memory communicatively coupled to the one or more processors;

a location information module maintained in the memory and executable by the one or more processors to obtain location information for each of a plurality of users, each piece of the location information indicating a location of one of the plurality of users;

a user traffic generation module maintained in the memory and executable by the one or more processors to generate user traffic information from the location information of the plurality of users;

a prediction module maintained in the memory and executable by the one or more processors to predict, based at least in part on the user traffic information, future user traffic for one or more geographical regions associated with a deal or a merchant of the deal, the deal comprising an offer of an item provided by the merchant, the future user traffic indicating a predicted number of users that will be located within the one or more geographical regions; and

a deal scheduling module maintained in the memory and executable by the one or more processors to schedule the deal for acquisition in the one or more geographical regions based at least in part on the future user traffic.

8. The system of claim 7, wherein each piece of the location information comprises check-in information shared one of the plurality of users through a location-based service.

9. The system of claim 7, wherein each piece of the location information comprises one or more of traffic report data indicating a location through which one of the plurality of users has traveled, acquisition history data indicating a location at which one of the plurality of users acquired an item, mobile device data indicating a location at which a mobile device associated with one of the plurality of users was located, or call history data indicating a location at which one of the plurality of users made a call.

10. The system of claim 7, wherein each piece of the location information is free of user identification information that identifies one of the plurality of users.

11. The system of claim 7, wherein the deal scheduling module is further executable by the one or more processors to determine, based at least in part on the future user traffic, a time period to offer the deal for acquisition.

12. The system of claim 7, wherein the deal scheduling module is further executable by the one or more processors to determine, based at least in part on the future user traffic, an amount of discount to apply to the deal when offering the deal for acquisition.

13. The system of claim 7, wherein the deal scheduling module is executable by the one or more processors to schedule the deal by:

causing the future user traffic to be output to a merchant or a user of the system; and

receiving input from the merchant or the user specifying at least one of a time period to offer the deal for acquisition or an amount of discount to apply to the deal when offering the deal for acquisition.

14. The system of claim 7, wherein the deal to be offered for acquisition comprises an attribute that is related to a preference or attribute of the plurality of users.

15. The system of claim 7, wherein the deal scheduling module is executable by the one or more processors to schedule the deal by automatically scheduling the deal during a time period in which the future user traffic is greater than a threshold.

16. The system of claim 7, wherein:

the location information module is further executable by the one or more processors to obtain event information indicating a location of a scheduled event; and

the prediction module is executable by the one or more processors to predict the future user traffic based at least in part on the event information.

17. A method comprising:

under control of one or more computing systems configured with specific executable instructions,

aggregating location information for a plurality of users;

identifying one or more geographical regions associated with a deal or a merchant of the deal, the deal comprising an offer of an item for acquisition;

predicting a number of the plurality of users that will be located within the one or more geographical regions based at least in part on the aggregated location information of the plurality of users; and

scheduling the deal for acquisition in the one or more geographical regions based at least in part on the predicted number of users.

18. The method of claim 17, further comprising:

based at least in part on the predicted number of users, determining a time period to offer the deal for acquisition; and

scheduling the deal to be offered for acquisition during the time period.

19. The method of claim 18, wherein the time period corresponds to a time period during which the predicted number of users is greater than a threshold.

20. The method of claim 17, further comprising:

based at least in part on the predicted number of users, determining an amount of discount to apply to the deal when offering the deal for acquisition; and

scheduling the deal to be offered for acquisition with the determined amount of discount.

21. The method of claim 20, wherein the amount of discount comprises a first amount of discount when the predicted number of users is below a threshold and the amount of discount comprises a second amount of discount when the predicted number of users is greater than or equal to the threshold, the second amount of discount being less than the first amount of discount.

22. The method of claim 17, further comprising:

before causing the deal to be scheduled, determining a preference or attribute of a predicted user of the number of users that will be located within the one or more geographical regions,

wherein the deal to be offered for acquisition comprises at least one attribute that is related to the preference or attribute of the predicted user.

23. The method of claim 17, further comprising:

before predicting the number of the plurality of users that will be located within the one or more geographical regions, obtaining event information indicating a location of a scheduled event;

wherein the number of the plurality of users that will be located within the one or more geographical regions is predicted based at least in part on the event information.

24. One or more computer-readable storage media storing computer-readable instructions that, when executed, instruct one or more processors to perform operations comprising:

obtaining location information for each of a plurality of users;

generating user traffic information from the location information of the plurality of users;

predicting a number of users that will be located within one or more geographical regions based on the user traffic information;

identifying a deal to be offered for acquisition and one or more parameters for offering the deal for acquisition, the deal comprising an offer of an item for acquisition; and

scheduling the deal to be offered for acquisition based at least in part on the one or more parameters and the predicted number of users.

25. The one or more computer-readable storage media of claim 24, wherein the one or more parameters comprise at least one of an amount of discount to apply to the deal when offering the deal for acquisition, a time period during which to offer the deal for acquisition, or a geographical region in which to offer the deal for acquisition.

26. The one or more computer-readable storage media of claim 24, wherein each piece of the location information comprises check-in information shared by one of the plurality of users through a location-based service.

27. The one or more computer-readable storage media of claim 24, wherein the operations further comprise causing a graph of future user traffic to be displayed, the graph indicating the future user traffic over a time period.

28. The one or more computer-readable storage media of claim 27, wherein the future user traffic is specific to a particular deal or item category, merchant, user preference, or user attribute.