July 6, 2015
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, email@example.com
Bin Hong Alex Lee, Engineering Systems Division, Massachusetts Institute of Technology, firstname.lastname@example.org
David Simchi-Levi, Engineering Systems Division, Department of Civil & Environmental Engineering and the Operations Research Center, Massachusetts Institute of Technology, email@example.com
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%].
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.
to read the full paper please follow the link Analytics for an Online Retailer – Demand Forecasting and Price Optimization at Rue La La-2
July 1, 2015
Linking Analytics to Performance
High performers share a winning approach to analytics when it comes to investments, talent, technological tools, and their focus on linking decision-making with business outcomes.
Analytics Accelerates Smarter Business Decisions
For many companies, analytics is still a question.
For high performers, analytics is the answer. High-performing companies – those that deliver continuous above-average business performance as well as above-average analytics performance – do analytics differently. This distinctive approach contributes to measurable gains across their enterprises. In essence, the stronger a company’s commitment to analytics, the higher that company’s performance.
Analytics high performers are far ahead in the journey from data and analysis to insights, decisions and outcomes. High performers see better results when they adopt analytics because they then adapt their enterprises to leverage analytics’ full power. By embedding analytics into decision-making processes linked to desired business outcomes, high performers can make smarter decisions faster and with greater certainty. They also make decisions that are more likely to lead to tangible business results.
There is a strong consensus across all companies surveyed on the importance of analytics to an organization’s future: more than two-thirds agree that analytics is important. But major differences surface when companies are grouped by several parameters: their application of analytics, along with growth, profitability, consistent performance and future prospects. From this analysis, a new class of company emerges, termed “high performers.” These high performers deliver higher business and analytics performance by doing analytics differently. They…
1. Adopt analytics to support decision- making: Twice as many high performers are using analytics in key areas.
2. Adapt decision processes to embed analytics: Twice as many high performers are embedding analytics in decision- making that leverages machine learning. High performers embed predictive analytics insights into key business processes (79 percent vs. 34 percent) and keep monitoring decisions and course-correct (84 percent vs. 32 percent).
To read the full paper, please follow the link Linking Analytics to High Performance_Executive Summary Final_June 2015
January 16, 2015
2015 Accenture and MIT Alliance in Business Analytics Research Update
The Accenture and MIT Alliance in Business Analytics is an applied research collaboration focused on developing new business analytics solutions to help today’s global companies solve some of their most critical challenges. The alliance harnesses Accenture’s industry and analytics expertise and MIT’s scientific and technological leadership to address the challenge of applying leading edge analytical techniques to solve practical problems with tangible value.
To read the full research update, please follow the link:
November 20, 2014
Research will be conducted through the Accenture and MIT Alliance in Business Analytics.
Accenture and MIT have issued a call for research proposals from MIT faculty to further discover advanced analytics solutions that can help solve some of the most complex and challenging problems facing today’s global companies. Accenture and MIT are also providing seed funding for non-tenured professors to support their analytics research.
Through the Accenture and MIT Alliance in Business Analytics, Accenture analytics experts, MIT faculty and researchers, and a select group of participating companies have come together to research the use of analytics in areas such as social media causal monitoring, learning and revenue optimization, equipment failure prediction, and fraud detection and prevention. The call for proposals from MIT faculty is for the next round of research that will begin in early 2015.
“We are pleased to provide seed funding to support non-tenured faculty and their research. This also benefits the alliance by continuing to bring in new thinking and ideas to the alliance’s research portfolio,” says Professor David Simchi-Levi, co-lead of the Accenture and MIT Alliance in Business Analytics.
Narendra Mulani, senior managing director of Accenture Analytics and co-lead of the Accenture and MIT Alliance in Business Analytics, says: “The research results already obtained through the alliance have established new advanced analytics solutions that can help businesses improve customer loyalty, increase revenues, and improve their business operations. We’re excited to continue the analytics research journey with this next round of research projects and drive real business outcomes through analytics.”
The new research charters, which can be viewed by MIT faculty, are listed here
Call for proposals and submission instructions
Both tenured and non-tenured professors are invited to submit a proposal to participate in an upcoming Accenture and MIT Alliance in Business Analytics research project. The submission process and timing details are as follows:
Tenured faculty: Tenured faculty that would like to participate in a currently listed research charter or submit a new research idea, please submit a proposal to firstname.lastname@example.org that includes the following elements: the problem or challenge, the objective of the study, expected outcomes, and the type of data required. New research ideas not related to current charters will be considered pending identification of a participating company.
Non-tenured professors: Non-tenured professors who have not yet participated in alliance research may also submit research ideas and proposals for potential seed funding. To submit a research idea for seed funding, please contact Leslie Sheppard at email@example.com.
Both charter and seed funding proposals must be submitted by Nov. 30 and focus on business analytics applications that can solve challenging real-world problems. All proposals will be reviewed and approved by the alliance’s board in December, and successful candidates will be notified by email. Depending on the submission date, research projects may begin as early as January 2015 and must be completed within a 12-month timeframe.
September 16, 2014
Strata Conference and Hadoop World 2014
Wednesday, October 15
Big Data is reaching beyond the Internet and into the machines that drive our world. Visit Industrial Internet day to gain insights from the way that power plants, factories, cars, and airplanes make use of sensors and software intelligence to improve operations and help managers make good decisions.
Big Data Analytics: Enabling Innovation while Reducing Risk
David Simchi-Levi, Professor of Engineering, MIT
We outline how big data and decision science (i) help Ford change the way they manage its supply network to increase resilience; (ii) improved revenue at Rue La La, an online Retailer; and (iii) motivated Rolls-Royce to introduce a new business model.
September 15, 2014
Rue La La’s use of the new price-optimization application is an example of how analytics can change the way a company operates. We started this project with the goal of reducing inventory, and ended up with a cutting-edge, demand-shaping application that has a tremendous impact on the retailer’s bottom line,” says David Simchi-Levi, a professor of civil and environmental engineering and engineering systems, and co-chair of the Accenture and MIT Alliance in Business Analytics.
Rue La La Expects 10 Percent Increase in Revenue
Murali Narayanaswamy, vice president of pricing and operations strategy at Rue La La, says, “This research project fundamentally changed our business. After implementing these analytics techniques, we’re expecting an increase in revenue of more than 10 percent with little impact on demand.”
View the video here
September 15, 2014
MIT’s Kris Johnson, Alex Lee, and David Simchi-Levi — along with online retailer Rue La La — have received the 2014 INFORMS Revenue Management and Pricing Section Practice Award for a project that is expected to increase Rue La La’s revenues. The award for the analytics-based pricing-optimization application developed through the Accenture and MIT Alliance in Business Analytics was presented on June 5 at the INFORMS Revenue Management and Pricing Conference in Istanbul.
The Institute for Operations Research and the Management Sciences (INFORMS) is the largest society for professionals in the field of operations research, management science, and analytics. Its annual Revenue Management and Pricing Section Practice Award recognizes outstanding applications of revenue management and pricing techniques. The winner is selected based on impact, originality and innovation, and technical merit.
February 26, 2014
Accenture and MIT Alliance in Business Analytics launches data science challenge in collaboration with Chicago
The Accenture and MIT Alliance in Business Analytics have launched an annual data science challenge for 2014 that is being conducted in collaboration with the city of Chicago.
The challenge invites MIT students to analyze Chicago’s publicly available data sets and develop data visualizations that will provide the city with insights that can help it better serve residents, visitors, and businesses. Through data visualization, or visual renderings of data sets, people with no background in data analysis can more easily understand insights from complex data sets.
Participants in the 2014 challenge will use information publicly available through Chicago’s Data Portal — such as the Chicago Traffic Tracker and Census data — to develop data visualizations.
David Simchi-Levi, an MIT professor and co-head of the Accenture and MIT Alliance in Business Analytics, said, “We are very pleased to launch the annual Accenture and MIT Data Science Challenge with the city of Chicago. We believe that this challenge, bringing together MIT students with the city of Chicago’s data sets, will assist the alliance’s research and help serve the city’s utilization of government data and engagement with their community.”
March 13, 2013
New collaboration will develop tools and techniques focused on big data and decision science.
MIT and Accenture today announced a five-year research collaboration to develop advanced analytics solutions. The alliance’s research aims to close the gap between the advance of analytics technologies and their successful application in specific industry and government environments.
The Accenture and MIT Alliance in Business Analytics combines Accenture’s industry and analytics expertise with MIT’s scientific and technological leadership. Its two streams of collaboration cover the challenges of harnessing big data and new approaches to improve the science of decision-making. The alliance will be headed by Narendra Mulani, senior managing director for Accenture Analytics, and David Simchi-Levi, professor of civil and environmental engineering and engineering systems at MIT.