CUSTOMER REFERENCE GENERATOR

• US 20100114665A1
• Filed: 09/17/2009
• Published: 05/06/2010
• Est. Priority Date: 11/03/2008
• Status: Abandoned Application

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: CAMPOS, Marcos, MOZES, Ari, DANKO, Krisztian Z., STENGARD, Peter J., KIM, Jooyoung John, CASAS, Francisco V.
• Application Number:
  US12/561,653
• Publication Number:
  US 20100114665A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/214 Database migration support
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/902 using more than one table in sequence, i.e. systems with three or more layers
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

A system generates a customer reference recommendation based on similarity to other customers. The system includes a customer data file including demographic data and purchasing pattern data for a plurality of customers, a first cluster model trained on the demographic data, and a second cluster model trained on the purchasing pattern data. A customer reference generator produces a customer reference recommendation based on cluster membership in the first and second cluster models in response to a query from a user interface.

Claims

1. A computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to generate a customer reference recommendation by:

uploading customer data including past purchasing pattern data and demographic data for a plurality of customers;

requesting a customer reference recommendation; and

receiving at least one customer reference recommendation from a prediction model trained by the purchasing pattern data and the demographic data.

2. The computer-readable medium of claim 1, wherein demographic data includes at least one of:

a customer location, industry, headquarter country, public corporation status, importer status, exporter status, number of locations, number of employees, and annual revenue.

3. The computer-readable medium of claim 1, wherein the at least one customer reference recommendation includes a similarity score to another customer.

4. The computer-readable medium of claim 1, wherein the prediction model comprises a first cluster model and a second cluster model.

5. The computer-readable medium of claim 4, wherein the first cluster model is built on the demographic data.

6. The computer-readable medium of claim 4, wherein the second cluster model is built on the past purchasing pattern data.

7. The computer-readable medium of claim 4, wherein the first and second cluster models are used to calculate a similarity score based on cluster membership.

8. The computer-readable medium of claim 7, wherein a first customer and a second customer both sharing membership in a first cluster in the first cluster model and a second cluster in the second cluster model generates a maximum similarity score.

18. The computer-readable medium of claim 1, further comprising generating a buying pattern prediction for a new customer.

19. The computer-readable medium of claim 1, wherein the customer reference recommendation comprises a list of customer references in a ranked order.

20. The computer-readable medium of claim 1, further comprising receiving feedback for at least one of the customer reference recommendation.

9. A computer-implemented method for generating a customer reference recommendation, comprising:

receiving customer data including purchasing pattern data and demographic data for a plurality of customers;

generating a prediction model trained by the purchasing pattern data and the demographic data;

receiving a query for a customer reference recommendation; and

responding to the query with at least one customer reference recommended by the prediction model.

10. The method of claim 9, wherein demographic data includes at least one of:

a customer location, industry, headquarter country, public corporation status, importer status, exporter status, number of locations, number of employees, and annual revenue.

11. The method of claim 9, wherein the at least one customer reference includes a similarity score to another customer.

12. The method of claim 9, wherein the prediction model comprises a first cluster model and a second cluster model.

13. The method of claim 12 wherein the first cluster model is built on the demographic data.

14. The method of claim 12, wherein the second cluster model is built on the purchasing pattern data.

15. The method of claim 12, wherein the first and second cluster models are used to calculate a similarity score based on cluster membership.

16. A system for generating a customer reference recommendation, comprising:

a customer data file including demographic data and purchasing pattern data for a plurality of customers;

a first cluster model trained on the demographic data;

a second cluster model trained on the purchasing pattern data;

a customer reference generator that produces a customer reference recommendation based on cluster membership in the first and second cluster models; and

a user interface for querying the customer reference generator.

17. The system of claim 16, wherein the customer reference recommendation is based on a similarity score generated from cluster memberships in the first cluster model and the second cluster model.