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This solution is powered by a series of sophisticated economic and statistical models designed to develop optimized pricing based upon up-to-the-minute information on price elasticity and supply/demand.  Every time either a Sales Proposal or a Purchase Agreement is executed, specific pricing and related data are captured and stored in a database.  The multiple-regression and pricing elasticity models then use this information to develop optimal pricing based on the likelihood of proposals turning into sales at each price point that has historically  been proposed.  Each of these calculated probabilities of turning a proposal into a Purchase Agreement is then weighted by the proposed price point to ultimately arrive at an expected revenue per proposal.  The price point with the maximum expected revenue is thereby identified as the optimal price point that should be chosen to maximize revenue.  This process, which updates on a real-time basis, utilizes this concept to calculate optimized pricing across a full spectrum of both discrete and interactive variables.  Pricing is thereby optimized on a product, channel, customer, geographic, seasonal (or other calendar-related variable, such as end of quarter), and salesperson basis - both discretely and interactively.  The result is that pricing is able to be fine tuned and revenue-optimized at a micro level, according to every possible variable, and every possible combination of variables, that has been statistically proven to influence expected revenue.

Pricing Optimization Model

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