Optimizing cashback rates

In a computer system having a processor, memory and data storage subsystems, a computer-implemented optimal sales rebate system, comprising:

• a data store comprising historical sales data, historical price data, and historical rebate data from one or more merchants;
• a demand prediction computing component, wherein the demand prediction computing component is configured to determine a relationship between prices, rebates offered, and quantity of products sold utilizing the historical sales data, the historical price data, and the historical rebate data in the data store;
• and
• an optimization computing component, comprising a gross earnings determining component and a rebate budget determining component, wherein the optimization computing component is configured to determine an optimal sales rebate rate for each of the plurality of products.

A computer-implemented method for determining optimal sales rebate rates, said method comprising:

• receiving historical sales data, historical price data, and historical rebate data for a plurality of products advertised by a merchant;
• utilizing a first computing process, determining a relationship between prices, rebates offered, and quantity of products sold utilizing a regression analysis;
• utilizing a second computing process, estimating gross earnings to be received from the merchant advertising the plurality of products;
• utilizing a third computing process, calculating a rebate budget as a portion of the estimated gross earnings;
• and
• utilizing a fourth computing process, determining an optimal rebate rate for each of the plurality of products utilizing a second order cone programming, wherein one of sales and profits of the plurality of products is maximized, and wherein the rebate budget is not exceeded.