Predictive and profile learning sales automation analytics system and method

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.

What is claimed is: 
     
         1 . A sales automation method comprising:
 providing a central data repository having time stamped cached sales records, idealized and learned sales data, and performance metrics and further providing a repository of raw sales records including raw sales related data;   deriving sales data forecasts for a sales metric utilizing a machine learning and forecasting module based on idealized and learned and raw sales data;   defining a desired sales goal relative to the forecasted sales metric and defining an input scenario based on the desired sales goal; and   deriving alterations to sales parameters required to achieve the desire sales goal.   
     
     
         2 . The sales automation method as recited in  claim 1 , where deriving the sales data forecasts for a sales metric includes forecasting based on user input manipulations. 
     
     
         3 . The sales automation method as recited in  claim 2 , further comprising: displaying to a user the alterations to the sales parameters need to meet the desired sales goal. 
     
     
         4 . The sales automation method as recited in  claim 3 , further comprising: deriving alterations to the quantity of individual data generation sources to achieve the desired goal. 
     
     
         5 . 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 sales automation by:
 providing a central data repository having time stamped cached sales records, idealized and learned sales data, and performance metrics and further providing a repository of raw sales records including raw sales related data; 
 deriving sales data forecasts for a sales metric utilizing a machine learning and forecasting module based on idealized and learned and raw sales data; 
 defining a desired sales goal relative to the forecasted sales metric and defining an input scenario based on the desired sales goal; and 
 deriving alterations to sales parameters required to achieve the desire sales goal. 
   
     
     
         6 . The system as recited in  claim 5 , where deriving the sales data forecasts for a sales metric includes forecasting based on user input manipulations. 
     
     
         7 . The system as recited in  claim 6 , further comprising: displaying to a user the alterations to the sales parameters need to meet the desired sales goal. 
     
     
         8 . The system as recited in  claim 7 , further comprising: deriving alterations to the quantity of individual data generation sources to achieve the desired goal. 
     
     
         9 . A computer-readable memory comprising a set of instructions stored therein which, when executed by a processor, causes the processor to perform sales automation by:
 providing a central data repository having time stamped cached sales records, idealized and learned sales data, and performance metrics and further providing a repository of raw sales records including raw sales related data;   deriving sales data forecasts for a sales metric utilizing a machine learning and forecasting module based on idealized and learned and raw sales data;   defining a desired sales goal relative to the forecasted sales metric and defining an input scenario based on the desired sales goal; and   deriving alterations to sales parameters required to achieve the desire sales goal.   
     
     
         10 . The computer-readable memory as recited in  claim 9 , where deriving the sales data forecasts for a sales metric includes forecasting based on user input manipulations. 
     
     
         11 . The computer-readable memory as recited in  claim 10 , further comprising: displaying to a user the alterations to the sales parameters need to meet the desired sales goal. 
     
     
         12 . The computer-readable memory as recited in  claim 11 , further comprising: deriving alterations to the quantity of individual data generation sources to achieve the desired goal.