Customer analytics data model

• US 10,235,649 B1
• Filed: 03/14/2014
• Issued: 03/19/2019
• Est. Priority Date: 03/14/2014
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Inventors: Marimuthu, Raja, Gopal, Radhakrishnan, Namperumal, Nandhakumar
• Primary Examiner(s): Stamber, Eric W
• Assistant Examiner(s): Waesco, Joseph M
• Application Number:
  US14/214,233
• Time in Patent Office: 1,831 Days
• Field of Search: None
• 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

Abstract

The present disclosure extends to methods, systems, and computer program products for generating attribute tables for holding attributes and a retail data model linking customer attributes to perform analytics on customer behavior that is optimized for the Hadoop platform.

Claims

1. A computer-implemented method for generating and optimizing attribute tables, comprising:

receiving a request over a network to generate an attribute table corresponding to a business plan, wherein the business plan comprises at least one parameter;

extracting without manual intervention from a user at least one attribute from the business plan that corresponds to the at least one parameter;

automatically generating without manual intervention from a user, an automated table generation process executing on the computer, the automated table generation process including creation in storage of an attribute table having at least one field corresponding to the at least one attribute;

automatically generating without manual intervention from a user, an automated request process executing on the computer, the automated request process including a plurality of requests for attribute data wherein each of the plurality of requests corresponds to a different source database;

wherein the attribute table is optimized for holding retail related attribute data comprising customer profiles, product characteristic information, and retail channel information;

wherein optimization of the attribute table comprises;

flattening a data structure;

partitioning related attributes together into subgroups;

determining a hierarchy for each of the subgroups and generating metadata comprising information regarding the hierarchy for each of the subgroups;

combining subgroups of attributes into a plurality of subgroup tables based on the hierarchy, wherein the flattening, partitioning, and determining the hierarchy minimize the number of subgroup tables according to the retail related attribute data by grouping at least two of;

the customer profile, the product characteristic information, and the retail channel information as related into one of the plurality of subgroup tables as a portion of the flattened data structure after receiving the attribute data from the plurality of requests for attribute data from the different source databases, and wherein the optimized attribute table is selectively generated for only the retail related attribute data based on the automatically generated plurality of requests for attribute data of the automated request process;

transposing the subgroups when combining the subgroups within an optimized table; and

automatically generating queries for each of the subgroup tables and aggregating the query into a common temporary table; and

automatically generating a report conveying the aggregated attribute information corresponding to the business plan.

2. The method of claim 1, further comprising maintaining the hierarchy of the subgroups through a plurality of levels of combinations during aggregation.

3. The method of claim 1, further comprising aggregating attribute data from a plurality of retail source databases representing a plurality of retail channels.

4. The method of claim 3, further comprising automatically generating a plurality of queries for attribute data wherein each request corresponds to one of the different retail source databases.

5. The method of claim 4, wherein the queries are generated to specifically correspond to each of the different retail channel databases such that the requests differ according to the partition within which corresponding subgroup data resides.

6. The method of claim 1, further comprising generating meta data comprising information regarding the combining of the subgroups stored within the attribute table.

7. The method of claim 1, wherein the attribute table is refreshed at a recurring interval.

8. The method of claim 7, wherein the recurring interval is monthly.

9. The method of claim 7, wherein the recurring interval is daily.

10. A system comprising one or more processors and a computer-readable media comprising instructions causing the one or more processors to:

receive a request over a network to generate an attribute table corresponding to a business plan, wherein the business plan comprises at least one parameter;

extract without manual intervention from a user at least one attribute from the business plan that corresponds to the at least one parameter;

automatically generate without manual intervention from a user, an automated table generation process executing on the processor, the automated table generation process including creation in storage of an attribute table having at least one field corresponding to the at least one attribute;

automatically generate without manual intervention from a user, an automated request process executing on the computer, the automated request process including a plurality of requests for attribute data, wherein each of the plurality of requests corresponds to a different source database;

wherein the attribute table is optimized for holding retail related attribute data comprising customer profiles, product characteristic information, and retail channel information;

wherein optimization of the attribute table comprises;

flattening a data structure;

partitioning related attributes together into subgroups;

determining a hierarchy for each of the subgroups and generating metadata comprising information regarding the hierarchy for each of the subgroups;

combining subgroups of attributes into a plurality of subgroup tables based on the hierarchy, wherein the flattening, partitioning, and determining the hierarchy minimize the number of subgroup tables according to the retail related attribute data by grouping at least two of;

the customer profile, the product characteristic information, and the retail channel information as related into one of the plurality of subgroup tables as a portion of the flattened data structure after receiving the attribute data from the plurality of requests for attribute data from the different source databases, and wherein the optimized attribute table is selectively generated for only the retail related attribute data based on the automatically generated plurality of requests for attribute data of the automated request process;

transposing the subgroups when combining the subgroups within an optimized table; and

generate queries for each of the subgroup tables and aggregating the query into common temporary tables; and

automatically generate a report conveying the aggregated attribute information corresponding to the business plan.

11. The system of claim 10, further comprising maintaining the hierarchy of the subgroups through a plurality of levels of combinations during aggregation.

12. The system of claim 10, further comprising aggregating attribute data from a plurality of retail source databases representing a plurality of retail channels.

13. The system of claim 12, further comprising generating a plurality of queries for attribute data wherein each request corresponds to one of the different retail source databases.

14. The system of claim 13, wherein the queries are generated to specifically correspond to each of the different retail channel databases such that the requests differ according to the partition within which corresponding subgroup data resides.

15. The system of claim 10, further comprising generating meta data comprising information regarding the combining of the subgroups stored within the attribute table.

16. The system of claim 10, wherein the attribute table is refreshed at a recurring interval.

17. The system of claim 16, wherein the recurring interval is monthly.

18. The system of claim 16, wherein the recurring interval is daily.