System and method for identifying and presenting business-to-business sales opportunities

• US 8,775,231 B1
• Filed: 02/12/2013
• Issued: 07/08/2014
• Est. Priority Date: 09/25/2012
• Status: Active Grant

• Current Assignee: Zilliant, Inc.
• Original Assignee: Zilliant, Inc.
• Inventors: Nipko, Joseph C., Eppele, Peter J., Navaneethakrishnan, Sabarinath, Zan, Jing, Hills, Eric B., Kurak, David D., Thompson, Barrett M.
• Primary Examiner(s): LOFTIS, JOHNNA RONEE
• Application Number:
  US13/765,438
• Time in Patent Office: 511 Days
• Field of Search: 705/7.27, 705/7.33
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0254 based on statistics

Abstract

The present invention relates to a system and method for efficiently identifying the sales opportunities in a business-to-business market environment. It provides a computer-implemented predictive sales intelligence system and method for identifying sales opportunities. The present invention is a computer implemented system and method for efficiently identifying reliable purchase pattern profiles through scientific analysis of customer data. It includes a system and method for calculating a customer'"'"'s purchase profile, clustering customers based on similarity of their purchase profile, and efficiently providing a reliable set of opportunities including lost sales (retention) and cross-selling (wallet share expansion) opportunities. It uses this reliable estimate of sales opportunities to retain and expand wallet share for customers.

Claims

1. A computer-implemented method hosted on a server for identifying product cross sell opportunities for a target customer to be presented to a business, comprising carrying out the steps of the method by computer-executable instructions being executed by the server with at least a computer processor and a memory, comprising the computer-implemented steps of:

downloading target customer data attributes and target product data for a target customer stored in memory into the server;

downloading target customer sales transaction data comprising historical sales transactions for the target customer stored in the memory for one or more products into the server;

downloading customer data stored in the memory for a group of core customers into the server wherein the customer data is selected from the group consisting of core customer data attributes, core customer product data, core customer product affinity data and core customer sales transaction data;

computing a clustered customer group with a clustering algorithm by grouping together customers from the group of core customers that have similar product purchase patterns to form a clustered customer group and correlating spending by product for core customers within the clustered customer group;

computing a benchmark spending model by product for the core customers within the clustered customer group to determine a clustered customer group product purchase pattern profile;

downloading non-core customer data stored in the memory for a group of non-core customers into the server wherein the non-core customer data is selected from the group consisting of non-core customer data attributes, non-core customer product data and non-core customer sales transaction data;

computing a non-core customer spending model by product for a non-core customer using the non-core customer data downloaded into the server to determine a non-core customer product purchase pattern profile;

normalizing the non-core customer product purchase pattern profile by computing an expected non-core customer product spending based on the clustered customer group product purchase pattern profile, and adding the normalized non-core customer product purchase pattern profile to the clustered customer group product purchase pattern profile;

determining the differences between the clustered customer group product purchase pattern profile and the target customer sales transaction data to generate product cross-sell opportunities for the target customer; and

presenting the product cross-sell opportunities for the target customer to the business.

2. The method of claim 1 further comprising the computer-implemented steps of:

downloading business rules stored in a database into the server;

applying an opportunity generator function to the cross sell opportunities for the target customer wherein the opportunity generator function uses business rules to identify the product cross sell opportunities for the target customer.

3. The method of claim 2 further comprising the computer-implemented steps of:

generating an opportunity report comprising prioritized product cross sell opportunities for the target customer; and

presenting the opportunity report to a user.

4. The method of claim 3 wherein the computer-implemented step of presenting the opportunity report to the user comprises displaying the opportunity report to a user on a computer user interface.

5. The method of claim 3 wherein in the computer-implemented step of presenting the opportunity report to the user comprises emailing the opportunity report to the user.

6. The method of claim 3, wherein the opportunity report comprises product cross-sell opportunities by product and dollar amount.

7. The method of claim 3, wherein the opportunity generator function further comprises computer-implemented instructions for generating an opportunity report that visually depicts product cross-sell opportunities, said computer-implemented instructions comprising computer code for:

identifying normalized non-core customer and core customer spending for a product group as expected non-target customer spending for the product group;

identifying target customer spending for the product group;

calculating target customer cross-sell opportunity amounts for the target customer;

displaying the product group, normalized non-core customer and core customer spending for the product group, the target customer spending for the product group and the target customer cross sell opportunity amounts; and

repeating the steps of identifying normalized non-core customer and core customer spending, identifying target customer spending, calculating target customer cross sell opportunity amounts for the target customer.

8. The method of claim 7 wherein a display that is visually depicted comprises a plurality of bars representing normalized expected non-target spending for the product group, the target customer spending for the product group and the target customer cross sell opportunity amounts.

9. The method of claim 8 wherein the target customer cross sell opportunity amounts are identified as the difference between an expected spend for the target customer calculated from the normalized expected non-target customer spending and actual spending for the target customer.

10. The method of claim 1 wherein the target customer sales transaction data includes the amount of money the target customer spends on a product, and product cross-sell opportunities for the target customer are computed as the difference between target customer spending on the product and the benchmark spending model by product for the core customers in the clustered customer group.

11. The method of claim 1, wherein the clustering algorithm comprises an affinity propagation clustering algorithm.

12. The method of claim 1, wherein the clustering algorithm is selected from the group consisting of:

affinity propagation algorithms, centroid models algorithms, hierarchical clustering algorithms and graph search algorithms.

13. The method of claim 1, wherein the clustered customer group product purchase pattern profile is stored in a parameter look up table for reuse in the step of generating product cross sell opportunities for the target customer.

14. The method of claim 13, wherein the parameter look up table comprises data selected from the group consisting of:

customer minimum revenue, product, product group, date, time period and customer definition parameters.

15. The method of claim 13, wherein the parameter lookup table comprises opportunity identification parameters selected from the group consisting of:

minimum opportunity amount and opportunity definition parameters.

16. The method of claim 13, wherein the parameter lookup table comprises sales campaign parameters.

17. A computer system comprising:

a processor;

a memory coupled to the processor;

a display device;

wherein the memory stores a program, that identifies product cross sell opportunities for a target customer to be presented to a business, when executed by the processor causes the processor to;

download target customer data attributes and target product data for a target customer stored in memory;

download target customer sales transaction data comprising historical sales transactions stored in memory for one or more products;

download customer data stored in memory for a group of core customers wherein the customer data is selected from the group consisting of core customer data attributes, core customer product data, core customer product affinity data and core customer sales transaction data;

compute a clustered customer group by grouping together customers from the group of core customers that have similar customer product purchase patterns;

compute a benchmark spending model by product for the core customers within the clustered customer group to determine a clustered customer group purchase pattern profile;

download non-core customer data stored in memory for a group of non-core customers wherein the non-core customer data is selected from the group consisting of non-core customer data attributes, non-core customer product data and non-core customer sales transaction data;

compute a spending model by product for a non-core customer to determine a non-core customer product purchase pattern profile based on non-core customer data;

normalize the non-core customer product purchase pattern profile by computing an expected non-core customer product spending based on the clustered customer group purchase pattern profile and adding the normalized non-core product purchase pattern profile to the clustered customer group purchase pattern profile;

determine the differences between the clustered customer product purchase pattern profile and the target customer sales transaction data to compute cross sell opportunities for the target customer; and

present product cross-sell opportunities for the target customer to the business.

18. The method of claim 17, wherein the product group comprises one or more products.

19. The method of claim 18 wherein a display that is visually depicted comprises a plurality of bars representing normalized expected non-target spending for the product, target customer spending for the product and cross sell opportunity amounts for the target customer.

20. A computer system comprising:

a server with a computer processor;

a memory coupled to the computer processor;

a display device;

wherein the memory stores a program, that identifies product cross sell opportunities for a target customer to be presented to a business, that when executed by the processor causes the processor to;

download target customer data attributes and target product data for a target customer stored in memory into the server;

download target customer sales transaction data comprising historical sales transactions for the target customer stored in the memory for one or more products into the server;

download customer data stored in the memory for a group of core customers into the server wherein the customer data is selected from the group consisting of core customer data attributes, core customer product data, core customer product affinity data and core customer sales transaction data;

group together customers from the group of core customers that have similar product purchase patterns to form a clustered customer group and correlating spending by product for core customers within the clustered customer group;

compute a benchmark spending model by product for the core customers within the clustered customer group to determine a clustered customer group product purchase pattern profile;

download non-core customer data stored in the memory for a group of non-core customers into the server wherein the non-core customer data is selected from the group consisting of non-core customer data attributes, non-core customer product data and non-core customer sales transaction data;

compute a non-core customer spending model by product for a non-core customer using the non-core customer data downloaded into the server to determine a non-core customer product purchase pattern profile;

normalize the non-core customer product purchase pattern profile by computing an expected non-core customer product spending based on the clustered customer group product purchase pattern profile, and adding the normalized non-core customer product purchase pattern profile to the clustered customer group purchase pattern profile; and

determine the differences between the clustered customer group product purchase pattern profile and the target customer sales transaction data to generate product cross sell opportunities for the target customer.