Research Paper, led by MIT prof. David Simchi-Levi, Accepted by Manufacturing & Service Operations Management (M&SOM)

Analytics for an Online Retailer: Demand Forecasting and Price Optimization

 

Kris Johnson Ferreira, Technology and Operations Management Unit, Harvard Business School, krisdj12@gmail.com

Bin Hong Alex Lee, Engineering Systems Division, Massachusetts Institute of Technology, binhong@mit.edu

David Simchi-Levi, Engineering Systems Division, Department of Civil & Environmental Engineering and the Operations Research Center, Massachusetts Institute of Technology, dslevi@mit.edu

 

We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer’s main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product’s demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multi-product price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La’s daily use. We conduct a field experiment and find that sales does not decrease due to implementing tool recommended price increases for medium and high price point products.

Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].

 

Introduction

We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (“flash sales”) on designer apparel and accessories. According to McKitterick (2015), this industry emerged in the mid-2000s and by 2015 was worth approximately 3.8 billion USD, benefiting from an annual industry growth of approximately 17% over the last 5 years. Rue La La has approximately 14% market share in this industry, which is third largest to Zulily (39%) and Gilt Groupe (18%). Several of its smaller competitors also have brick-and-mortar stores, whereas others like Rue La La only sell products online. For an overview of the online fashion sample sales and broader “daily deal” industries, see Wolverson (2012), LON (2011), and Ostapenko (2013).

Upon visiting Rue La La’s website (www.ruelala.com), the customer sees several “events”, each representing a collection of for-sale products that are similar in some way. For example, one event might represent a collection of products from the same designer, whereas another event might represent a collection of men’s sweaters. Figure 1 shows a snapshot of three events that have appeared on their website. At the bottom of each event, there is a countdown timer informing the customer of the time remaining until the event is no longer available; events typically last between 1-4 days.

 

Screenshot 2015-07-06 14.31.58

 

to read the full paper please follow the link Analytics for an Online Retailer – Demand Forecasting and Price Optimization at Rue La La-2