Predictive and profile learning salesperson performance system and method

A method for sales automation comprising:

• providing a central repository of machine learned and top performer sales data profiles and a repository of raw sales data;
• extracting raw sales data from the repository of raw sale data;
• extracting machine learned and top performer sales data profiles from the central repository;
• machine learning, by a processor, central sales data patterns based on a forecasting formulation of raw historical sales models, the forecasting of raw historical sales models based on raw sales data, wherein machine learning central sales data patterns comprises calculating a revenue goal attainment for a sales person during a sales period, classifying performance of the sales person based on the calculated revenue goal attainment, calculating a revenue variance for the sales person during the sales period, calculating a difference between the extracted learned sales pipeline models and sales transaction data for the sales person during the sales period, calculating a difference between an idealized model and the sales transaction data for the sales person during the sales period, and repeating said machine learning central sales data patterns for each sales period and for each sales person;
• machine learning new sale data profiles based on a formulation of learned sales models, the formulation of learned sales models based on machine learned and top performer data profiles, wherein machine learning new sales data profiles comprises selecting a random sales person, comparing transaction data for the random sales person to learned sales pipeline models and the idealized model, reinforcing the idealized model when the transaction data for the random sales person fits the idealized model, and reinforcing the learned sales pipeline models when the transaction data for the random sales person fits the learned sales pipeline models, and repeating said machine learning new sales data profiles for each learning algorithm and each time step;
• storing the new sales data profiles and central sales data patterns to the central repository;
• and
• scoring performance of central sales data patterns based on the machine learned new sales data profiles.

A system comprising:

• a processor;
• and
• a memory coupled with and readable by the processor and storing a set of instructions which, when executed by the processor, cause the processor to automate sales by:
• providing a central repository of machine learned and top performer sales data profiles and a repository of raw sales data;
• extracting raw sales data from the repository of raw sale data;
• extracting machine learned and top performer sales data profiles from the central repository;
• machine learning central sales data patterns based on a forecasting formulation of raw historical sales models, the forecasting of raw historical sales models based on raw sales data, wherein machine learning central sales data patterns comprises calculating a revenue goal attainment for a sales person during a sales period, classifying performance of the sales person based on the calculated revenue goal attainment, calculating a revenue variance for the sales person during the sales period, calculating a difference between the extracted learned sales pipeline models and sales transaction data for the sales person during the sales period, calculating a difference between an idealized model and the sales transaction data for the sales person during the sales period, and repeating said machine learning central sales data patterns for each sales period and for each sales person;
• machine learning new sale data profiles based on a formulation of learned sales models, the formulation of learned sales models based on machine learned and top performer data profiles, wherein machine learning new sales data profiles comprises selecting a random sales person, comparing transaction data for the random sales person to learned sales pipeline models and the idealized model, reinforcing the idealized model when the transaction data for the random sales person fits the idealized model, and reinforcing the learned sales pipeline models when the transaction data for the random sales person fits the learned sales pipeline models, and repeating said machine learning new sales data profiles for each learning algorithm and each time step;
• storing the new sales data profiles and central sales data patterns to the central repository;
• and
• scoring performance of central sales data patterns based on the machine learned new sales data profiles.