Retail Markdown optimization &#8211; Startegy and Solutions
Background Tiger Analytics supported SAS Institute in a deployment of their industry leading markdown optimization solution at a Top 10 retailer in the US. Business Problem Over the past decade, our client had seen a consistent reduction on gross margin, especially on the inventory that is permanently marked down. The client wanted to get more precise with their markdown strategy, and control the depth of markdown, timing of markdown and react better to store-level inventory. In addition, they wanted to reduce the frequency of markdowns as this has a significant impact on store labor. Challenges With more than 1000 stores spread across the USA, and each store carrying more than 100,000 stylecolors, marking down items at a store level intelligently poses a significant problem. Solving this problem presents many challenges for Forecasting and Optimization. Forecasting for this problem is marked by short-life cycles for fashion products, high volume of data at a store level, high levels of promotion etc. Optimization needs to be performed considering store level inventory, price elasticity and base forecast. In addition to all these challenges, data (especially for merchandise attributes) is generally not of the highest quality. Solution Tiger Analytics worked with SAS on implementing the SAS Markdown Optimization solution. The SAS solution is built to address this complex problem. The SAS Markdown Optimization solution was integrated with the client’s data warehouse and planning system. The solution estimated the demand for the diverse product types supported by the retailer through its multiple channels. Tiger Analytics supported the development of the demand models for a large number of merchandise categories. We supported the integration of these models into daily and weekly processes. The price recommendations were made while accommodating for multiple complex business rules involved in making markdown decisions. The solution provided information not only on how much the markdown should be but also when and at which stores. Additionally, processes to continuously tune the model based on new data and to monitor markdown performance were implemented. Results The solution is expected to provide both financial and operational benefits to the customer. The solution is on track to deliver the following benefits: Increase in Gross Margin Dollars: $90 Mil/ Year Increase in sell-Through on first markdown : 20-30% About Tiger Analytics We are a boutique consulting firm that provides services to enable organizations create value using Advanced Analytics. We strive to create business value through applying innovative analytical approaches to business problems. We specialize in Social and E-Commerce Analytics, Customer Analytics, Merchandising, Demand and Supply Optimization. For more information visit www.TigerAnalytics.com Source: Tiger Analytics
Background Tiger Analytics supported SAS Institute in a deployment of their industry leading markdown optimization solution at a Top 10 retailer in the US. Business Problem Over the past decade, our client had seen a consistent reduction on gross margin, especially on the inventory that is permanently marked down. The client wanted to get more […]
Background Tiger Analytics supported SAS Institute in a deployment of their industry leading markdown optimization solution at a Top 10 retailer in the
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Dr. Kumar started Tiger Analytics with a desire to bring his experience in management science to helping organizations achieve superior performance through the application of advanced analytics. As the CEO, he works closely with our customers to define the analytical approaches for business problems. In addition, he's focused on building an organization that attracts high quality talent that can help us deliver superior results.

He has previously done consulting work for McKinsey & Co, Intel, SAS Institute, a Top 10 US Department Store, a Large Apparel Vendor, Railroads and several start-ups. His consulting work has been in the areas of Supply Chain Optimization, Merchandise Optimization, Online Ad-Targeting and Forecasting. 

Prior to founding Tiger, Mahesh was an Assistant Professor at the University of Maryland's Smith School of Business and Rutgers University. He has a PhD in Operations Research and Marketing from MIT and a B.Tech in Computer Science from IIT Bombay (India).