Customer attribute exemption

• US 10,565,538 B1
• Filed: 03/14/2014
• Issued: 02/18/2020
• Est. Priority Date: 03/14/2014
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Inventors: Bailey, Brian Paul, Bannon, Richard
• Primary Examiner(s): Loftis, Johnna R
• Assistant Examiner(s): Henry, Matthew D
• Application Number:
  US14/214,170
• Time in Patent Office: 2,167 Days
• Field of Search: 705 734
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

The present disclosure extends to methods, systems, and computer program products for generating attribute tables for holding attributes while a corresponding business plan is in an approval process. The present disclosure also extends to methods, systems and computer program products for approving proposed business plans and automatically generating workflow for establishing data tables for aggregating customer profile data in those data tables. The present disclosure also extends to methods, systems and computer programs for excluding, hiding or removing certain items of information or data from data contained in one or more attribute tables.

Claims

1. A method for making rapid aggregated data studies for determining buying patterns related to a business plan based on attribute data retrieved from a database repository over a computer network, the method comprising:

storing a plurality of transaction records, each including a store location, in a distributed memory, the distributed memory comprising;

a plurality of nodes, each node comprising a and a processor, each memory configured to store a portion of the plurality of transaction records, and each processor configured to perform an operation using the portion of the plurality of transaction records in the memory upon request;

receiving over a network a request for approval of a business plan;

capturing, from the business plan, user inputs that describe features of the business plan;

generating attributes aggregatable to values corresponding to the user inputs that will be used in the business plan, the attributes including purchase attributes;

generating a flattened and transposed attribute table for storing aggregated attribute data therein, the attribute table including fields corresponding to the generated attributes for use by the business plan;

determining the identity of a first node of the plurality of nodes having a memory storing a first transaction record corresponding to a field of the attribute table and the identity of a second node of the plurality of nodes having a memory storing a second transaction record corresponding to a field of the attribute table, and retrieving the attribute data from only a selected node by transmitting a request to only the selected node to return the attribute data corresponding to the field of the attribute table, the selected node selected from the group consisting of;

the first node and the second node;

receiving the requested attribute data from the selected node;

automatically executing one or more database operations to aggregate the requested attribute data into the fields of the attribute table;

further aggregating purchase attribute data for members of a cluster formed by customers that share predetermined attributes, at least one of the predetermined attributes being a physical address, such that the members of the cluster reside at the same residence;

generating an initial buying pattern for a customer based on the aggregated attribute data, the initial buying pattern including at least one purchase attribute and a percentage likelihood of the customer making a purchase having an attribute that is the same as the at least one purchase attribute in the future;

determining one or more preferred store locations for the customer based on the aggregated attribute data;

generating a refined buying pattern by excluding each transaction record for the customer that does not include one of the one or more preferred store locations; and

generating a report conveying the refined buying pattern patterns relative to the business plan.

2. The method of claim 1, further comprising generating recommendations based on the refined buying pattern.

3. The method of claim 1, wherein the one or more preferred store locations are determined based on at least one of a number of visits, a total spend, or a distance from a physical address of the customer.

4. The method of claim 1, further comprising aggregating purchase attribute data for members of a cluster formed by purchasers that share predetermined attributes.

5. The method of claim 4, wherein one of the predetermined shared attributes for members of a cluster is a physical address, such that the members of the cluster reside at a same residence.

6. The method of claim 1, wherein purchases for shared household items by one member of the cluster are accounted for in a purchase history of at least one other member in the cluster.

7. The method of claim 1, further comprising determining seasonal attributes for products and customers and excluding the seasonal attributes from a non-seasonal purchase history.

8. The method of claim 1, further comprising generating purchasing clusters from within the aggregated attribute data that may be used to exclude attributes used for making recommendations and generating reports.

9. The method of claim 8, wherein each purchasing cluster is in part determined based on a time range of the purchase and a predetermined time before a purchase is expected to be made.

10. The method claim 8, wherein each purchasing cluster is in part determined based on events related to an age of the customer.

11. The method of claim 1, further comprising providing instant savings offerings based in part on an amount of time since the purchase of products by the customer.

12. A system for making rapid aggregated data studies for determining buying patterns related to approving proposed business plans and automatically generating workflow for establishing data tables for aggregating customer profiles data therein, wherein the approval process and business plan development data is exchanged over a computer network comprising one or more processors and one or more memory devices operably coupled to the one or more processors and storing executable and operational data, the executable and operational data effective to cause the one or more processors to:

store a plurality of transaction records, each including a store location, in a distributed memory, the distributed memory comprising;

a plurality of nodes, each node comprising a memory and a processor, each memory configured to store a local portion of the plurality of transaction records, and each processor configured to perform an operation using the local portion of the plurality of transaction records in the memory upon request;

receive over a network a request for approval of a business plan;

capture from the business plan user inputs that describe features of the business plan;

generate attributes including purchase attributes, the attributes being aggregatable to values corresponding to the user inputs that will be used in the business plan;

generate a flattened and transposed attribute table for storing aggregated attribute data therein, the attribute table including fields corresponding to the generated attributes for use by the business plan;

determine the identity of a first node of the plurality of nodes having a memory storing a first transaction record corresponding to a field of the attribute table and the identity of a second node of the plurality of nodes having a memory storing a second transaction record corresponding to a field of the attribute table, and retrieve the attribute data from only a selected node by transmitting a request to only the selected node to return the attribute data corresponding to the field of the attribute table the selected node selected from the group consisting of;

the first node and the second node;

receive the requested attribute data from each node of the plurality of nodes;

automatically execute one or more database operations to aggregate the requested attribute data into the fields of the attribute table;

generate an initial buying pattern for a customer based on the aggregated attribute data, the initial buying pattern including at least one purchase attribute and a percentage likelihood of the customer making a purchase having an attribute that is the same as the at least one purchase attribute in the future;

determine one or more preferred store locations for the customer based on the aggregated attribute data;

generate a refined buying pattern by excluding each transaction record for the customer that does not include one of the one or more preferred store locations; and

generate a report conveying the refined buying patterns relative to the business plan.

13. The system of claim 12, wherein the system further comprises generating recommendations based on the refined buying pattern.

14. The system of claim 12, wherein the one or more preferred store locations are determined based on at least one of a number of visits, a total spend, or a distance from a physical address of the customer.

15. The system of claim 12, wherein the system further comprises aggregating purchase attribute data for members of a cluster formed by purchasers that share predetermined attributes.

16. The system of claim 15, wherein one of the predetermined shared attributes for members of a cluster is a physical address such that the members of the cluster reside at a same residence.

17. The system of claim 12, wherein purchases for shared household items by one member of the cluster are accounted for in a purchase history of at least one other member in the cluster.

18. The system of claim 12 wherein the system further comprises determining seasonal attributes for products and customers and excluding the seasonal attributes from a non-seasonal purchase history.

19. The system of claim 12, wherein the system further comprises generating purchasing clusters from within the aggregated attribute data that may be used to exclude attributes used for making recommendations and generating reports.

20. The system of claim 19, wherein each purchasing cluster is in part determined based on a time range of the purchase and predetermined time before a purchase is expected to be made.

21. The system claim 19, wherein each purchasing cluster is in part determined based on events related to an age of the customer.

22. The system of claim 12, wherein the system further comprises providing instant savings offerings based in part on an amount of time since the purchase of products by the customer.