AGGREGATE MOBILE ANALYTICS-BASED INVENTORY ACTIVITY IDENTIFICATION SYSTEMS AND METHODS

• US 20180107971A1
• Filed: 10/12/2017
• Published: 04/19/2018
• Est. Priority Date: 10/19/2016
• Status: Active Application

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Wal-Mart Stores Texas LLC (WalMart Inc.)
• Inventors: Vukin, Greg N., Mattingly, Todd D., Wilkinson, Bruce W.
• Application Number:
  US15/782,509
• Publication Number:
  US 20180107971A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

Some embodiments provide retail product inventory distribution systems, comprising: an inventory tracking system; an inventory management control circuit configured to couple with a source of multiple different types of mobile analytics information, and to: electronically access aggregated layers of multiple different types of mobile analytics information corresponding to activities associated with multiple different electronic user devices; identify, based on at least a first pattern of activity determined from the aggregated multiple different types of mobile analytics information, an inventory adjustment activity to be implemented as a function of the first pattern of activity relative to retail services; and communicate instructions to cause the inventory adjustment activity to be implemented.

Claims

1. A retail product inventory distribution system, comprising:

an inventory tracking system receiving signals comprising inventory information and configured to maintain inventory count information of tens of thousands of products across multiple different retail shopping facilities;

an inventory management control circuit coupled with the inventory tracking system and configured to couple with a source of multiple different types of mobile analytics information;

wherein the inventory management control circuit is further configured to;

electronically access aggregated layers of multiple different types of mobile analytics information corresponding to activities associated with multiple different electronic user devices relative to a first geographic area of interest, wherein the aggregated mobile analytics information does not identify individual user devices of the multiple user devices and from which the individual user devices cannot be identified solely through the aggregated mobile analytics information;

identify, based on at least a first pattern of activity determined from the aggregated multiple different types of mobile analytics information, an inventory adjustment activity to be implemented as a function of the first pattern of activity relative to retail services; and

communicate instructions to cause the inventory adjustment activity to be implemented.

2. The system of claim 1, wherein the aggregate mobile analytics information comprises at least two of cellular mobile analytics information, wireless network access mobile analytics information, and social media analytics information.

3. The system of claim 2, wherein the aggregate mobile analytics information comprises mobile analytics information collected over time and represents sequences of activity and movement by at least a subset of the multiple user devices.

4. The system of claim 1, wherein the inventory management control circuit is configured to identify the inventory adjustment activity to cause a modification of inventory of one or more products at a retail shopping facility within a threshold distance of an origin area of the first pattern of activity.

5. The system of claim 1, wherein the inventory management control circuit is configured to identify the inventory adjustment activity as a function of clustered movement patterns corresponding to multiple different mobile devices, wherein each of the clustered movement patterns, including the first pattern of activity, have a common origin area and common destination area.

6. The system of claim 5, wherein the inventory management control circuit is configured to identify the inventory adjustment activity to cause a modification of inventory of a product at a location along a first clustered movement pattern of the clustered movement patterns.

7. The system of claim 1, wherein the inventory management control circuit is configured to identify retail customers that are associated with a pattern location corresponding to the occurrence of activities of the first pattern of activity, and identify an aggregate partiality vector corresponding to the identified retail customers based on sets of partiality vectors that are each associated with one of the identified customers;

wherein the inventory management control circuit in identifying the inventory adjustment activity is configured identify a product consistent with the aggregate partiality vector and identify the inventory adjustment activity that affects inventory of the product at an adjustment location proximate the pattern location.

8. The system of claim 1, further comprising:

a resource allocation system configured to identify third party services that are predicted to benefit from the use of the aggregate mobile analytics information, and cause the aggregated mobile analytics information to be distributed to at least one of the third party services.

9. A method of distributing retail product inventory based in part on analytics information, comprising:

electronically accessing aggregated layers of multiple different types of mobile analytics information corresponding to activities associated with multiple different electronic user devices relative to a first geographic area of interest, wherein the aggregated mobile analytics information does not identify individual user devices of the multiple user devices and from which the individual user devices cannot be identified solely through the aggregated mobile analytics information;

identifying, based on at least a first pattern of activity determined from the aggregated multiple different types of mobile analytics information, an inventory adjustment activity to be implemented as a function of the first pattern of activity relative to retail services; and

communicating instructions to cause the inventory adjustment activity to be implemented.

10. The method of claim 9, wherein the aggregate mobile analytics information comprises at least two of cellular mobile analytics information, wireless network access mobile analytics information, and social media analytics information.

11. The method of claim 10, wherein the aggregate mobile analytics information comprises mobile analytics information collected over time and represents sequences of activity and movement by at least a subset of the multiple user devices.

12. The method of claim 9, wherein the identifying the inventory adjustment activity comprises identifying the inventory adjustment activity to cause a modification of inventory of one or more products at a retail shopping facility within a threshold distance of an origin area of the first pattern of activity.

13. The method of claim 9, wherein the identifying the inventory adjustment activity comprises identifying the inventory adjustment activity as a function of clustered movement patterns corresponding to multiple different mobile devices, wherein each of the clustered movement patterns, including the first pattern of activity, have a common origin area and common destination area.

14. The method of claim 13, wherein the identifying the inventory adjustment activity comprises identifying the inventory adjustment activity to cause a modification of inventory of a product at a location along a first clustered movement pattern of the clustered movement patterns.

15. The method of claim 9, further comprising:

identifying retail customers that are associated with a pattern location corresponding to the occurrence of activities of the first pattern of activity; and

identifying an aggregate partiality vector corresponding to the identified retail customers based on sets of partiality vectors that are each associated with one of the identified customers;

wherein the identifying the inventory adjustment activity comprises identifying a product consistent with the aggregate partiality vector and identifying the inventory adjustment activity that affects inventory of the product at an adjustment location proximate the pattern location.

16. The method of claim 9, further comprising:

identifying third party services that are predicted to benefit from the use of the aggregate mobile analytics information, and distributing the aggregated mobile analytics information to at least one of the third party services.