Abstract: Systems and methods are described which optimize a business process through adaptive learning. Incoming customers are profiled and based on the customer's profile, a corresponding set of actions may be generated. From this set of actions, an algorithm is used to select a discrete action which is then presented to the customer. The customer's response to the presented action may in turn be used to forecast future customer responses and update the selection algorithm. These methods and systems allow attainment of a specific objective through progressive optimization of a business process.

Abstract: A method and system can be used to determine an optimal price of a commodity on a spot market. Market state conditions can be generated using historical data, such as transactional and other data. The market state conditions may include nearly any number of attributes, and the forecast market state conditions may include a forecast price for the commodity during the next time period. The historical data and market state condition may be used in a clustering module that separates the data into clusters. The cluster with the market state condition is identified, and the data in that cluster is used to generate a price-demand curve for the next time period. The price for the commodity on the spot market during that time period can be determined consistent with the company's business rules.