Abstract: Systems and methods for predicting stock on hand for predefined markdown plans are provided. An example method can include retrieving retail item sales data; aggregating normal sales and markdown sales; converting normal sales and markdown sales to a weekly time series normal sales and a weekly time series markdown sales; creating a plurality of disruptive time series; receiving one or more markdown plans; performing prediction on each disruptive time series; obtaining an average of predictions from each disruptive time series to find a final sales prediction; calculating a predicted stock on hand; and rerunning the disruptive time series model to automatically recalculate the predicted stock on hand and the predicted incremental impact in real time when the processor receives a change made on the markdown plans.

Abstract: The system and method described herein provide a computationally efficient clearance markdown planning system that may quickly automate the calculations of many possible markdown plans to determine the impact of various pricing options on total sales volume and revenue. While considering business constraints and other parameters, the markdown planning system may determine an optimized markdown plan for a particular product, season, and locale. In some aspects, the markdown planning system may use scaling and a variation of dynamic programming (DP) to quickly calculate and compare different potential markdown plans.

Abstract: Embodiments of the present invention disclose a method and system for determining customer conversion propensity. According to one embodiment, a source registrant group having a plurality of registered customers is identified from a database. A probability for a conversion event is then computed for each of the plurality of customers within the source registrant group is then computed. The customers within the source registrant are than categorized based on the computed probability value and a timing for the conversion event.