1. A computer-implemented method for analyzing forecasting model accuracy, comprising: generating a baseline forecast, wherein generating the baseline forecast is performed using a forecasting model and one or more processors; receiving an input that specifies a storage time duration; storing the baseline forecast, wherein the baseline forecast is stored throughout a time period that lasts for the specified storage time duration; receiving marketing mix data associated with promotions of one or more retail products, wherein the promotions occur during the time period, and wherein the marketing mix data includes a price of the one or more retail products during the time period; retrieving the baseline forecast subsequent to storing the baseline forecast throughout the time period; generating a revised sales forecast using the baseline forecast and the marketing mix data, wherein the revised forecast provides forecasted sales of the one or more retail products during the time period; comparing the forecasted sales to a pool of production data, wherein the pool of production data includes sales data that represents actual sales of the one or more retail products during the time period; determining an accuracy metric for the forecasting model based on the comparison of the revised sales forecast to the pool of production data; receiving an accuracy criteria corresponding to the accuracy metric; determining, using one or more data processors, that the accuracy metric fails to satisfy the accuracy criteria, wherein determining includes providing an indication that the accuracy metric fails to satisfy the accuracy criteria; copying data from the pool of production data, wherein the data is copied to a separate data store, and wherein copying is performed in response to determining that the accuracy metric fails to satisfy the accuracy criteria; designating a first portion of the data copied to the separate data store, wherein the first portion is designated as a training data sample; designating a second portion of the data copied to the separate data store, wherein the second portion is designated as an accuracy testing data sample, wherein the data in the training data sample is from an earlier period than the data in the accuracy testing data sample; rebuilding the forecasting model, wherein the forecasting model is rebuilt based on the training data sample; evaluating forecasting performance of the rebuilt forecasting model, wherein evaluating is performed based on the accuracy testing data sample and includes representing the forecasting performance by generating a rebuilt model accuracy metric; and generating an additional forecast, wherein the additional forecast is generated using the rebuilt forecasting model.