Systems and Methods for Continually Scoring and Segmenting Open Opportunities Using Client Data and Product Predictors

A machine learning and adaptive optimization method, the method comprising:

• continually receiving client data for a plurality of closed opportunities, the client data comprising client behaviors and respective outcomes of software trials of a product maintained in a database;
• determining, based on the client data, predictors comprising one or more attributes that correlate to successful opportunities of the plurality of closed opportunities that resulted in a purchase of the product, the predictors further comprising one or more attributes that correlate to unsuccessful opportunities of the plurality of closed opportunities that did not result in purchases of the product;
• continually segmenting open opportunities using the predictors, the client behaviors, and respective outcomes;
• continually scoring the open opportunities using the predictors, the client behaviors, and respective outcomes;
• tailoring a targeted proposal to clients associated with the open opportunities that have client behaviors that are associated with closed won opportunities;
• and
• applying a feedback loop that utilizes, for the continually segmenting and continually scoring the open opportunities, client behaviors of the clients receiving the targeted proposal if the clients result in closed opportunities, the feedback loop placing the client behaviors into the database and updating the predictors based on client data received from the clients resulting in the closed opportunities.

A machine learning and adaptive optimization system, the system comprising:

• a processor;
• and
• a memory for storing executable instructions, the processor executing the instructions to:
• continually receive client data for a plurality of closed opportunities, the client data comprising client behaviors and respective outcomes of software trials of a product maintained in a database;
• determine, based on the client data, predictors that include weighted client behaviors and respective outcomes from closed opportunities of the plurality of closed opportunities that resulted in a purchase of the product;
• continually segment open opportunities using the weighted client behaviors and respective outcomes relative to the predictors for the product;
• continually score the open opportunities using the weighted client behaviors and respective outcomes relative to the predictors for the product;
• update a product roadmap of the product using the open opportunities and the predictors, the product roadmap comprising technical specifications for the product;
• and
• update the predictors based on client data received for opportunities of the open opportunities that result in successfully closed opportunities.