System and method for forecasting price optimization benefits in retail stores utilizing back-casting and decomposition analysis

A system for merchandising decomposition analysis, useful in association with stores, the apparatus comprising:

• an optimization engine configured to set initial prices of products, wherein the products are products sold by the stores;
• a point of sales data collector configured to compile initial point of sales data from the stores;
• an econometric engine configured to generate a demand forecast model for product sales of the products using the initial point of sales data;
• the optimization engine further configured to optimize prices using the demand forecast model;
• an econometric engine configured to generate a profit and sales lift prediction based upon the optimized prices;
• the optimization engine further configured to reset prices of the products to the optimized prices;
• the point of sales data collector further configured to transmit subsequent point of sales data from the stores, wherein the subsequent point of sales data reflects transactions that include the optimized prices;
• a forecasting engine, including a processor, configured to generate back-casts of volume, revenue and profit by applying causal data in the subsequent point of sales data to equations of the demand forecast model, wherein the back-casts predict volume, revenue and profits had the product maintained the initial prices, the forecasting engine further configured to adjust the back-casts to account for effects unknown when the profit and sales lift prediction was generated, and the forecasting engine further configured to compare the adjusted back-casts to the profit and sales lift prediction to generate a benefits analysis wherein the benefits analysis includes a differential benefits analysis generated by a differential benefit analyzer configured to:
• detecting pre-optimization price of the products;
• detecting pre-optimization cost of the products;
• generate pre-optimization forecasted units, wherein the pre-optimization forecasted units are predictions of volume of sales of the products at the pre-optimization price, and wherein the pre-optimization forecasted units is generated by utilizing the pre-optimization price and the demand forecast model;
• calculate forecasted pre-optimization profit by subtracting pre-optimization cost from pre-optimization price and multiplying the result by the pre-optimization forecasted units;
• detecting post-optimization price of the products;
• detecting post-optimization cost of the products;
• generate post-optimization forecasted units, wherein the post-optimization forecasted units are predictions of volume of sales of the products at the post-optimization price, and wherein the post-optimization forecasted units is generated by utilizing the post-optimization price and the demand forecast model;
• calculate forecasted post-optimization profit by subtracting post-optimization cost from post-optimization price and multiplying the result by the post-optimization forecasted units;
• generate raw profit benefit by subtracting forecasted pre-optimization profit from the forecasted post-optimization profit;
• and
• calibrate raw profit benefit by iteratively excluding effects, wherein the demand forecast model include the effects, and further wherein the effects include at least one of promotion effect, ad effect, discount effect, halo effect, cannibalism effect, seasonality effect, holiday effect and event effects;
• and
• an interface configured to display generated benefit forecasts.