Target Brands, Inc.

Luyen Le

Saibal Bhattacharya

James Gaynor

Elif Tokar-Erdemir

Haema Nilakanta

Chad Morgan

Brian Wongchaowart

Anick Saha

Hamidreza Badri

Kusumakumari Vanteru

Xiao Pu

Koel Ghosh

In general, this disclosure is directed to presenting eligible promotional items on an offer description page. In some embodiments, the eligible promotional items are personalized based on customer relevancy. One aspect is a method for dynamically presenting items eligible for a promotion on an e-commerce application of a retailer, the method comprising receiving, from a customer user computing device associated with a customer user, a request for an offer description page of the promotion, retrieving the items eligible for the promotion and user interaction data associated with the customer user, ranking the items eligible for the promotion based as least in part on the user interaction data, and generating a user interface for the offer description page presenting the items eligible for the promotion according to the ranking, wherein the offer description page initially displays a subset of the highest ranked items eligible for the promotion.

Personalized ranking of promotional items

Product demand forecasting accuracy utilizes partitional clustering of time series data with dynamic time warping. The product demand forecasting disclosed herein is particularly suited to forecasting product demand for products with limited sales data. Time-series sales data of a producs (or group of products) with limited sales data (e.g. a sparse or no time series of sales data) are dynamically time warped with sales data of products, or groups of products, having extensive sales data (e.g., an extensive time series of sales data) to determine a clustering model with an optimal number of clusters and a prototype time series for each cluster in the model. The prototype time series for the cluster in which the product (or group of products) with limited sales data lies is utilized as its product demand forecast.

Time series clustering analysis for forecasting demand

Methods and systems are described for optimizing markdown schedules for clearance items at physical retail stores. For example, price sensitivity of a current inventory item at a physical retail store may be modeled using techniques that account for differences in operating behavior and data availability at physical stores versus online channels for selling items. When the current item is placed on clearance at the physical retail store, a request for a markdown schedule, including goals for the clearance, may be received. The forecasted price sensitivity of the current item may be adjusted based on actual price sensitivity of one or more past clearance items (e.g., based on actual clearance sales data) determined to match the current item. An optimal markdown schedule for the item may then be determined based, at least in part, on the adjusted price sensitivity of the item and clearance goals.

Optimization of markdown schedules for clearance items at physical retail stores

Methods and systems for generating an optimal clearance schedule for inventory items in a retail enterprise. A method includes accessing historical sales data for a plurality of inventory items in a department of a retailer. A selection of an item is received and a set of possible discount schedules for the item are generated. Each schedule includes a set of prices and a price duration. A forecasting tool provides a sales performance of the item based on the historical sales data. An optimal clearance schedule is selected from the possible discount schedules.

Method and system for managing clearance items

Growing community of inventors

Minneapolis, MN, United States of America

Luyen Le