the$new$frontierin$price$...
TRANSCRIPT
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THE NEW FRONTIER IN PRICE OPTIMIZATION
Presented by Dr. David Simchi-‐Levi
Professor of Engineering Systems at MIT Co-‐Director of Leaders for Global Opera?ons
MIT Sloan School of Management
Moderated by Dr. Peter Hirst Associate Dean, MIT Sloan Execu?ve Educa?on
May 4, 2016 MIT Sloan Execu?ve Educa?on Webinar hGp://execu?ve.mit.edu
TwiGer: #MITWEBINAR
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INNOVATION@WORKTM Webinar Series
• Hear the latest insights from world-‐renowned MIT Sloan faculty
• Each webinar features current research from professors and lecturers who teach in our Execu?ve Educa?on Programs
• Visit execuOve.mit.edu for more informa?on on MIT Sloan Execu?ve Educa?on, David Simchi-‐Levi, and his course: Supply Chain Strategy and Management
MIT Sloan Execu?ve Educa?on Webinar hGp://execu?ve.mit.edu TwiGer: #MITWEBINAR
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About Dr. David Simchi-‐Levi
• Professor of Engineering Systems at MIT, Co-‐Director of Leaders for Global Opera?ons, and the Director of Accenture-‐MIT Alliance in Business Analy?cs
• He is considered one of the premier thought leaders in supply chain management and business analy?cs
• His research focuses on developing and implemen?ng robust and efficient techniques for opera?ons management
• Recipient of the 2014 INFORMS Wagner Prize for Excellence in Opera?ons Research Prac?ce; 2014 INFORMS Revenue Management and Pricing Sec?on Prac?ce Award; and Ford 2015 Engineering Excellence Award
• Chairman of OPS Rules, an opera?ons analy?cs consul?ng company and Opaly?cs, a cloud analy?cs pla\orm provider
• He was the founder of LogicTools which provided so]ware solu?ons and professional services for supply chain op?miza?on. LogicTools became part of IBM in 2009 MIT Sloan Execu?ve Educa?on Webinar hGp://execu?ve.mit.edu
TwiGer: #MITWEBINAR
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MoOvaOon – Online Retail
5
• ~$300B industry with ~10% annual growth (Last 5 years) Excludes online sales of brick & mortar stores
• Have addiOonal informaOon compared to brick & mortar stores Real-‐Ome customer purchase decisions (buy / no buy)
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6
A Few Stories …
• ForecasOng & Price OpOmizaOon Rue La La
• Learning & Price OpOmizaOon Groupon
• ForecasOng, Learning & Price OpOmizaOon B2W
• Conclusions
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7
A Few Stories …
• ForecasOng & Price OpOmizaOon Rue La La
• Learning & Price OpOmizaOon Groupon
• ForecasOng, Learning & Price OpOmizaOon B2W
• Conclusions
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Online Fashion Sample Sales Industry
• Offers extremely limited-time discounts (“flash sales”) on designer apparel & accessories
• Emerged in mid-2000s; ~$4B industry with ~17% annual growth in last 5 years (US)
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Snapshot of Rue La La’s Website
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“Style”
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“SKU”
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Flash Sales Operations Merchants
purchase items from designers
Designers ship items to warehouse*
Merchants decide when to sell items
(create “event”)
During event, customers
purchase items
Sell out of item?
End *Sometimes designer will hold inventory
Yes
No
Focus on first time this happens
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0%
10%
20%
30%
40%
50%
60%
70%
0%-‐25% 25%-‐50% 50%-‐75% 75%-‐100% SOLD OUT(100%)
% of Items
% Inventory Sold (Sell-‐Through)
1st Exposure Sell-‐Through Distribution
Department 1Department 2Department 3Department 4Department 5
Sell-Through Distribution of New Products
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suggests price may be too low
suggests price may be too high
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Approach Goal: Maximize expected revenue from new products
Demand Forecasting
Challenges: Predicting demand for
items that have never been sold before
Estimating lost sales Techniques: Clustering Machine learning models
for regression
Price Optimization
Challenges: Structure of demand forecast Demand of each style is
dependent on price of competing styles exponential # variables
Techniques: Novel reformulation of price
optimization problem Creation of efficient algorithm
to solve daily
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Forecasting Model: Explanatory Variables Included
• Tested several machine learning techniques
– Regression trees performed best
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Regression Trees: Illustration and Intuition
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If condition is true, move left; otherwise, move right
Demand prediction
Why regression trees? – Use features to partition styles sold in past, and
only use relevant styles to predict demand – Allow for non-monotonic price/demand relationship
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Approach Goal: Maximize expected revenue from new products
Demand Forecasting
Challenges: Predicting demand for
items that have never been sold before
Estimating lost sales Techniques: Clustering Machine learning models
for regression
Price Optimization
Challenges: Structure of demand forecast Demand of each style is
dependent on price of competing styles exponential # variables
Techniques: Novel reformulation of price
optimization problem Creation of efficient algorithm
to solve daily
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Pricing Decision Support Tool
ETL Process
Op?miza?on Input
Op?mal Price Recommenda-‐
?ons
Rue La La Database
Reports and Visualiza1on
Ad hoc Reports
Query / Drill Down Visualizer
Standard Reports
Op?mizer Database
Op?mizer Database
Rue La La Enterprise Resource Planning System
Products Trans-‐ac?ons
Event Planning
Inventory-‐Constrained Demand Predic?on
Regression Tree
Predic?on (Rscript)
Impending Event Data
R Predic?ons
Inventory Informa?on
Retail Price Op1mizer
LP Bound Algorithm
LP_Solve API-‐based Op1mizer
Sta1s1cs Tool -‐ R
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Field Experiment • Goals
– Would implementing the tool’s recommended price increases cause a decrease in demand?
– What impact would the price increases have on revenue?
• Identified ~6,000 styles where tool recommended price increase
– Divided styles into 5 categories based on price point; for each category…
• Raised prices on ~half of styles (treatment group) • Did not raise prices on other ~half of styles (control group)
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Sell-Through Analysis
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styles with lowest price point
styles with highest price point
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Revenue Impact
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22
A Few Stories …
• ForecasOng & Price OpOmizaOon Rue La La
• Learning & Price OpOmizaOon Groupon
• ForecasOng, Learning & Price OpOmizaOon B2W
• Conclusions
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Example: Online Daily Deals Retailer o Selling thousands of deals
from local merchants everyday
o Demand prediction before launch is quite inaccurate
o Collecting lots of sales data but not using them to adjust price
o Product life is short • 50% of deals are sold within
first 4 days after launch • 70% sold within first month
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“Online” Revenue Management
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• Online = Online Retail Many products face demand uncertainty and short product life cycle
Collects huge amount of sales data Easy to adjust price dynamically
• Online = Online Learning & Op1miza1on Learning demand on the fly using data ExploraOon vs. ExploitaOon
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Exploration vs. Exploitation Tradeoff
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Learning
Earning
Test multiple prices to estimate demand
Offer price estimated from data to maximize revenue
Exploration vs. Exploitation
Tradeoff
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Demand Hypotheses
• 𝑚 demand func?on demand func?on hypotheses:
{ 𝑑↓1 , …, 𝑑↓𝑚 } • di (p) is the probability that a customer will purchase at a price p
26
di (p)
Fig: An example with 3 hypotheses
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Demand Hypotheses
• 𝑚 demand func?on demand func?on hypotheses:
{ 𝑑↓1 , …, 𝑑↓𝑚 } • di (p) is the probability that a customer will purchase at a price p
• If hypothesis 𝑖 holds, holds, customer buys with probability
• The retailer does not know the true hypothesis
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di (p)
Fig: An example with 3 hypotheses
di (p)
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The Algorithm: Single Learning Price
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T 𝑇↓1
Initial price 𝑝↓1 Using data, choose demand 𝑖, set 𝑝↓2 = 𝑝↓𝑖↑∗
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Implementation at Groupon
o We applied the one-learning price algorithm
• The initial price is negotiated by Groupon with merchant
• Allow decrease by (5%, 30%)
• Merchant gets a fixed share
o Φ={𝑑↓1 ,…, 𝑑↓𝐾 } is generated from historical data by clustering
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Implementation at Groupon
o We applied the one-learning price algorithm
• The initial price is negotiated by Groupon with merchant
• Allow decrease by (5%, 30%)
• Merchant gets a fixed share
o Φ={𝑑↓1 ,…, 𝑑↓𝐾 } is generated from historical data by clustering
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Groupon: $7 Restaurant: $10
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Implementation at Groupon
o We applied the one-learning price algorithm
• The initial price is negotiated by Groupon with merchant
• Allow decrease by (5%, 30%)
• Merchant gets a fixed share
o Φ={𝑑↓1 ,…, 𝑑↓𝐾 } is generated from historical data by clustering
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Groupon: $7 Restaurant: $10
$15
Groupon: $5
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Pilot Live Experiment Impact by Deal Category
149% 107%
60% 85%
335%
44% 17%
-‐18% -‐2%
140%
-‐50%
0%
50%
100%
150%
200%
250%
300%
350%
400%
Beauty / Wellness / Healthcare
Food & Drink Leisure Offers / Ac?vi?es
Services Shopping
Bookings Revenue
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A Few Stories …
• ForecasOng & Price OpOmizaOon Rue La La
• Learning & Price OpOmizaOon Groupon
• ForecasOng, Learning & Price OpOmizaOon B2W
• Conclusions
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B2W Overview • Largest online retailer in Latin America
• Offers the widest selection of products across categories such as Mobile Phones, TVs, Domestic appliances, Games and Fashion
• Launched in 1999 as an extension to the physical store business
• 30% annual revenue growth for the last 3 years
• Competitors include Amazon, Walmart, Ponto Frio, Extra
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B2W Brands
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Dynamic Pricing Approach
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Demand Curve Generation Extract sensitivity of quantity sold with change in price Challenge: Historical data has multiple factors that influence quantity sold Method: Random Forest (Bagging)
Learning via Product Clustering products Identify clusters of products having similar characteristics Challenge: Wide range of characteristics in which products differ Method: K-Means
Optimization Maximize revenue (margin) of a cluster subject to a min margin (revenue) constraint Challenge: Solving efficiently Method: Mixed Integer Linear Programming
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Implemented Solution Provides Real Time Decision Support to the Commercial Team
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Dynamic Pricing--Operations • Algorithm now runs twice a day;
• Typically, the first few hours of the day generate the traffic prediction, which is an input to the Random Forest;
• No manual intervention. All prices are pushed directly to the web-site.
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Cluster 1: Low Price Products
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Cluster 2: Fast Selling Products
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Cluster 3: Premium Products
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Cluster 3: Premium Products-with Margin Optimization
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Competitive Benefits: Dynamic Pricing is Also a Powerful Tool for Market Positioning
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Unique SKUs Sold Daily in Control vs. Treatment Groups
Model sold 40% more unique SKUs everyday (chart below). Will help in positioning B2W as ‘better price every time, for everything’
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A Few Stories …
• ForecasOng & Price OpOmizaOon Rue La La
• Learning & Price OpOmizaOon Groupon
• ForecasOng, Learning & Price OpOmizaOon B2W
• Conclusions
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Summary
• New Challenges from emerging online retail business (e.g. flash sale, daily deals) High demand uncertainty, short product life cycle and limited inventory
• Combine ForecasOng, Learning and OpOmizaOon techniques to impact bokom line ExploraOon vs. ExploitaOon Tradeoff
• Our proposed methods are supported by theory, simulaOon results and PracOce
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Acknowledgement
• — Dr. Kris Johnson Ferreira, HBS
• — Mr. He Wang, MIT
• — OPS Rules
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The Logic of Logis9cs: Theory, Algorithms, and Applica9ons for Logis9cs Management Springer Science + Business Media, David Simchi-‐Levi, Xin Chen, Julien Bramel
Opera9ons Rules: Delivering Customer Value through Flexible Opera9ons, MIT Press, David Simchi-‐Levi Managing the Supply Chain: The Defini9ve Guide for the Business Professional, The McGraw-‐Hill Companies, David Simchi-‐Levi, Philip Kaminsky, Edith Simchi-‐Levi
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