PREDICTIVE AND PROFILE LEARNING SALES AUTOMATION ANALYTICS SYSTEM AND METHOD

• US 20140067470A1
• Filed: 08/07/2013
• Published: 03/06/2014
• Est. Priority Date: 12/28/2006
• Status: Abandoned Application

• Current Assignee: Oracle OTC Subsidiary LLC (Oracle Corporation)
• Original Assignee: Oracle OTC Subsidiary LLC (Oracle Corporation)
• Inventors: RICHTER, JAMES NEAL
• Application Number:
  US13/961,586
• Publication Number:
  US 20140067470A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A sales automation system and method, namely a system and method for scoring sales representative performance and forecasting future sales representative performance. These scoring and forecasting techniques can apply to a sales representative monitoring his own performance, comparing himself to others within the organization (or even between organizations using methods described in application), contemplating which job duties are falling behind and which are ahead of schedule, and numerous other related activities. Similarly, with the sales representative providing a full set of performance data, the system is in a position to aid a sales manager identify which sales representatives are behind others and why, as well as help with resource planning should requirements, such as quotas or staffing, change.

Claims

1. A sales automation method comprising:

providing a repository of ideal and learned sales data profiles and a repository of raw sales data;

extracting raw sales data and extracting ideal and learned sales data;

defining a sales pipeline having a construct of a series of stages thereby defining a sales strategy where each stage is assigned a conversion rate and assumed value potential;

correlating the raw sales data among the series of stages based on the respective task;

correlating the ideal and learned data among the series of stages based on respective task;

developing a predictive model of a conversion rate of the sales strategy base on the correlating of the ideal and learned data; and

applying the predictive model to the correlated raw sales data and developing a raw predictive model to a row conversion rate.

2. A method comprising:

determining at least one first revenue forecasting parameter based on at least one performance characteristic of at least one person supporting generation of revenue for the organization; and

determining at least one second revenue forecasting parameter to forecast the revenue of an organization, wherein the at least second forecasting parameter is determined at least in part from sales opportunities in a sales pipeline; and

forecasting revenue of the organization using at least in part the first and second forecasting parameters.

3. The method according to claim 2 further wherein the at least one performance characteristic is based on historical performance of the person.

4. The method according to claim 3 further wherein the performance characteristic is based on the classification of the person'"'"'s capability to generate revenue for the organization.

5. The method according to claim 2 further including maintaining a profile for at least one of the persons, wherein the profile includes at least one parameter indicative of the capability to generate revenue.

6. The method according to claim 2 further comprising:

maintaining a central data repository including time stamped cached and sales records, idealized and learned sales data and performance metrics;

maintaining a repository of raw sales records including raw sales data;

retrieving data from said central repository and said repository of raw sales records;

producing metric scores, derived sales data, and learned sales data; and

storing said produced data in the central data repository.

7. The method according to claim 6 further comprising:

receiving a user initiated query;

querying based on the user initiated query the central data repository to transmit the cached sales records, idealized and learned sales data, and performance metrics; and

applying transforms, idealized and learned functions, and data models to the transmitted data.

8. A system comprising:

a processor; and

a memory coupled with and readable by the processor and storing therein a set of instructions which, when executed by the processor, causes the processor to perform a method comprising;

determining at least one first revenue forecasting parameter based on at least one performance characteristic of at least one person supporting generation of revenue for the organization; and

determining at least one second revenue forecasting parameter to forecast the revenue of an organization, wherein the at least second forecasting parameter is determined at least in part from sales opportunities in a sales pipeline; and

forecasting revenue of the organization using at least in part the first and second forecasting parameters.

9. The system according to claim 8 further wherein the at least one performance characteristic is based on historical performance of the person.

10. The system according to claim 9 further wherein the performance characteristic is based on the classification of the person'"'"'s capability to generate revenue for the organization.

11. The system according to claim 8 further including maintaining a profile for at least one of the persons, wherein the profile includes at least one parameter indicative of the capability to generate revenue.

12. The system according to claim 8 further comprising:

maintaining a central data repository including time stamped cached and sales records, idealized and learned sales data and performance metrics;

maintaining a repository of raw sales records including raw sales data;

retrieving data from said central repository and said repository of raw sales records;

producing metric scores, derived sales data, and learned sales data; and

storing said produced data in the central data repository.

13. The system according to claim 12 further comprising:

receiving a user initiated query;

querying based on the user initiated query the central data repository to transmit the cached sales records, idealized and learned sales data, and performance metrics; and

applying transforms, idealized and learned functions, and data models to the transmitted data.

14. A computer-readable memory comprising a set of instructions stored therein which, when executed by a processor, causes the processor to perform a method comprising:

determining at least one first revenue forecasting parameter based on at least one performance characteristic of at least one person supporting generation of revenue for the organization; and

determining at least one second revenue forecasting parameter to forecast the revenue of an organization, wherein the at least second forecasting parameter is determined at least in part from sales opportunities in a sales pipeline; and

forecasting revenue of the organization using at least in part the first and second forecasting parameters.

15. The computer-readable memory according to claim 14 further wherein the at least one performance characteristic is based on historical performance of the person.

16. The computer-readable memory according to claim 15 further wherein the performance characteristic is based on the classification of the person'"'"'s capability to generate revenue for the organization.

17. The computer-readable memory according to claim 14 further including maintaining a profile for at least one of the persons, wherein the profile includes at least one parameter indicative of the capability to generate revenue.

18. The computer-readable memory according to claim 14 further comprising:

maintaining a central data repository including time stamped cached and sales records, idealized and learned sales data and performance metrics;

maintaining a repository of raw sales records including raw sales data;

retrieving data from said central repository and said repository of raw sales records;

producing metric scores, derived sales data, and learned sales data; and

storing said produced data in the central data repository.

19. The computer-readable memory according to claim 18 further comprising:

receiving a user initiated query;

querying based on the user initiated query the central data repository to transmit the cached sales records, idealized and learned sales data, and performance metrics; and

applying transforms, idealized and learned functions, and data models to the transmitted data.