HYBRID PREDICTION MODEL FOR A SALES PROSPECTOR

• US 20100114663A1
• Filed: 06/02/2009
• Published: 05/06/2010
• Est. Priority Date: 11/03/2008
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Mozes, Ari, Stengard, Peter J., Kim, Jooyoung John, Campos, Marcos, Casas, Francisco V., Danko, Krisztian Z.
• Granted Patent: US 8,775,230 B2
• 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

Systems and methods provide a system for generating a sales prospect recommendation that uses demographic data to make a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.

Claims

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

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

requesting a sales prospect recommendation; and

receiving at least one sales prospect recommendation from a predictive model trained by the past purchasing data and the demographic data, wherein the at least one sales prospect recommendation includes a customer and product.

2. The computer-readable medium of claim 1, wherein demographic data comprises 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 sales prospect recommendation comprises at least one of:

an estimated time to close a sale, an estimated revenue from the sale, and a probability that the sale will occur.

4. The computer-readable medium of claim 1, wherein the predictive model comprises an association rule model, 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 data.

7. The computer-readable medium of claim 4, wherein the first and second cluster models are used to predict at least one of:

an estimated time to close a sale and an estimated revenue from the sale.

8. The computer-readable medium of claim 4, wherein the association rule model is used to predict a product/customer pair and a probability that a sale will occur.

9. A computer-implemented method for generating a sales prospect recommendation, comprising:

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

generating a predictive model trained by the past purchasing data and the demographic data;

receiving a query for a sales prospect recommendation; and

responding to the query with at least one sales prospect recommendation based on the predictive model.

10. The method of claim 9, wherein the predictive model includes an association rule model, a first cluster model, and a second cluster model.

11. The method of claim 10, wherein the first cluster model is built on the demographic data.

12. The method of claim 10, wherein the second cluster model is built on the past purchasing data.

13. The method of claim 10, wherein the first and second cluster models are used to predict at least one of:

an estimated time to close the sale and an estimated revenue from the sale.

14. The method of claim 10, wherein the association rule model comprises an Apriori algorithm.

15. A system for generating a sales prospect recommendation, comprising:

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

a sales modeler trained using the customer data file that receives a customer name as an input and outputs a product name and a probability of a sales transaction occurring; and

a user interface for querying the sales modeler.

16. The system of claim 15, wherein the sales modeler includes an association rule model, a first cluster model, and a second cluster model.

17. The system of claim 16, wherein the first cluster model is built on the demographic data.

18. The system of claim 16, wherein the second cluster model is built on the past purchasing data.

19. The system of claim 15, wherein the user interface comprises a bubble for each of a plurality of sales prospects.

20. A computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to generate a sales prospect recommendation by:

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

requesting a sales prospect recommendation; and

receiving at least one sales prospect from a predictive model trained by the past purchasing data and the demographic data, wherein the at least one sales prospect includes a customer and product, wherein the predictive model includes an association rule model, a first cluster model built on the demographic data, and a second cluster model the past purchasing data, and wherein the first and second cluster models are used to predict at least one of an estimated time to close the sale and an estimated revenue from the sale.