Innovation in Retail · solutions that address real retail challenges. The work of this...

Innovation in Retail: Using Machine Learning to Optimize Performance

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DESCRIPTIVEPREDICTIVE PRESCRIPTIVE

Oracle Retail Science, a group within Oracle’s Retail Global Business Unit, is devoted to helping the industry meet this challenge. The team works in partnership with participating retail customers and researchers from major universities to further the advancement of knowledge and solutions that enable new capabilities in predictive and individualized retailing.

Over the years, Oracle Retail Science has focused on three core areas of data analytics:

1. Descriptive (what happened?)

2. Predictive (what will happen?)

3. Prescriptive (what shall we do about it?)

They also, increasingly, focus on cognitive analytics (what is the next step?), in which solutions are devised to get smarter over time, creating progressively better recommendations and forecasts based on consumer and user behavior.

Today’s technology enables retailers to collect a wealth of information about customers as they make buying decisions. This presents a challenge: how to take full advantage of this information and use it to improve business performance.

INTRODUCTION

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“The Oracle Retail Science team works closely with retailers and end

users, as well as strategic partners from prominent universities

and labs, to develop innovative solutions that address real retail

challenges. The work of this cross-disciplinary team of specialists

in operations research, machine learning, mathematics, physics, management science, and other

relevant fields has contributed to over 50 U.S. patents dedicated to retail

processes and technologies.”

– Troy Parent, Senior Director, Retail Science & Insights, Oracle Retail Science

ORACLE RETAIL SCIENCE PLATFORM FOCUSIn a webcast presentation, Oracle Retail and Dr. David Simchi-Levi of Massachusetts Institute of Technology (MIT) partnered together to report on two projects conducted. The first of these was a collaboration with a flash sale retailer seeking help with managing inventory and maximizing sell-through. The second project was with a large-scale ecommerce site seeking a solution that would simultaneously increase revenue, profit, and market share.

Maximizing First-Exposure Opportunity

As noted, one participant in the Oracle Retail-MIT project was an online retailer that specializes in flash sales of high-fashion apparel, accessories, and home goods. At any given time on the website there are multiple sale events running in tandem; for each event, the site will display:• images of the products, • the price at which each is being offered, • the manufacturer’s suggested retail price for that item, • and the number of days and hours remaining in the sale.

Fashion designers do not want their products to be discounted too frequently, so the retailer’s contracts with its suppliers stipulate a period—sometimes as long as seven or eight months—between the first and any subsequent offering (“exposure,” in the trade). To avoid having capital tied up in temporarily unsaleable inventory, the retailer’s goal was to maximize first-exposure sell-through—percentage of inventory sold—in each of the five product categories.

Sell-through projections are largely based on price, which in turn is normally based on demand, i.e. the way a product performs in the market. However, flash-sale specialists constantly have to establish a price for a product with which they have no information about consumer interest; since they have never sold it before. They don’t want to underprice it and leave money on the table, and they don’t want to overprice it and have to sit in inventory for six months or more.

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“We often talk about

big data analytics. Here

we are talking about

small data—or no data—

analytics. How do you

connect a product you’ve

never sold before to a

product you have sold

before and optimize

price before observing

consumer behavior

around this product?”

—Prof. David Simchi-Levi, Professor of Engineering

Systems, MIT

https://videohub.oracle.com/media/Analytics+and+Machine+Learning+in+RetailA+Demand+Forecasting+and+Price+Optimization+/1_xttudjq8/73329341?partnerref=GB

< HOME > “The company reports

that these end-to-end

technologies have not

only improved profit,

revenue, and market

share, but have also

increased the diversity

and range of product the

company is selling on a

day-to-day basis.”


Systems, MIT

Goal: A Model That Optimizes Price

The team began by amassing the internal and external information that was available about each of the five product categories:

• With internal information, if the new product was a ladies’ running shoe, for example, the retailer would have sold other ladies’ running shoes, and would have information about color and size popularity.

• External information included brand popularity, discount relative to the manufacturer’s recommended price, and information about other products on sale at the same time that could cannibalize sales of this particular product. Added to the external information, was data about the timing of the sales event: the effect of year, month, day of the week, and start time of the event, all of which could have an impact on customer participation and traffic to the website.

This collected data was subjected to analysis by a machine learning algorithm called a regression tree, which is essentially an overlay of scenarios. You begin at the top of the tree with an assumption—say, a starting price of a hundred dollars. Then you move down through a series of possible modifying factors—other products on sale at the same time, for instance—branching out to the left (price goes up) or the right (price goes down) as you go. At the same time, you are calculating and re-calculating the effects of this or that not only on price but on demand.

The challenge to the team was this: how do we set price to maximize the opportunity we have during a product’s first exposure?

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Regression Trees: Illustration and Intuition

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“For most products, if

price goes up, demand

goes down—but for

fashion products, demand

sometimes increases,

as price is indicative of

brand or quality. To make

accurate projections,

you need a sophisticated

technique that is sensitive

to this type of behavior.”


Systems, MIT

Trial and Results For six months, the team ran a field experiment with two objectives:

• The first was straightforward: impact on revenue.

• The second, remembering that in many cases the algorithm recommended increasing price, was that increasing price—which would of course increase profit and, if all went well, revenue—raises the possibility of decreasing market share. The retailer wanted to be certain that increased price had no negative effect on market share.

To test this, the team set up a sub-experiment across six thousand products for which the tool recommended a price increase. They were divided into five categories, ranging from Category A (least expensive products) to E (most expensive). Half were separated into a control group whose pricing was not directed by the algorithm; for the other half, the algorithm generated forecast and price and pushed them into the website.

After some minor corrections in Category A (the algorithm was too aggressive in its initial price increases), it was demonstrated that price increases did not affect market share in any category. As to revenues, for Categories B, C, and D, the algorithm-driven pricing increased revenues by between 10% and 12%. For Category E—the most expensive products—it increased revenues by 22%.

Algorithm-Driven Pricing Results:Revenue Impact: Increase in Revenue with 90% Confidence Interval

Innovation in Retail USING DATA ANALYTICS TO OPTIMIZE RETAIL PERFORMANCE

ABOUT ORACLE RETAIL SCIENCE:

ONLINE FLASH SALES:

The Oracle Retail Science team works in partnership with participating retail customers and researchers from major universities like (MIT) to further the advancement of knowledge and solutions that enable new capabilities in predictive and individualized retailing.

Emerged in mid-2000s

$4B INDUSTRY

17% annual growth in last 5 years (US)

TEST #1:

10-12%

22%

Copyright © 2017, Oracle and/or its af�liates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its af�liates. Other names may be trademarks of their respective owners. 301117

Follow the Blog:

Escaping Cannibals and Forecasting the Unknown: Machine Learning Applications in Retail Pricing and Forecasting

Guidebook Merchandising Success: Building a Foundation

for Retail Innovation and Growth

Data Analytics: How retailers can use descriptive, predictive, prescriptive and cognitive analytics to create better retail forecasts based on consumer and user behavior.

The following are tests conducted in the Retail industry by Massachusetts Institute of Technology (MIT) and the Oracle Retail Science team.

LEARN MORE ABOUT DATA ANALYTICS: DOWNLOADTHE ORACLE RETAIL SCIENCE GUIDEBOOK TODAY

A collaboration with an on-line �ash sale retailer seeking help with managing inventory and maximizing sell-through.

The Goal: To see price increases impact on revenue & market share.

The Challenge: Set pricing to maximize the opportunity during a product’s 1st exposure to the market.

Scope:• 6 month timeframe

• Price increase on 3,000 products (3,000 control group)

Split into 5 categories (A-E) A = least expensive E = most expensive

Algorithm-Driven Pricing Results:

50+ U.S. patents dedicated to retail processes and technologies

Works with 20 of the top 20 retailers worldwide

Turns data into $ with consistent user & data scientist experience

Gets you in the driver’s seat with innovation workbench

Helps you stay ahead with latest machine learning & AI solutions

Achieves lowest TCO with a full suite of optimization solutions

TEST #2:

A large-scale ecommerce site seeking a solution that would simultaneously increase revenue, pro�t, and market share.

Algorithm applied to premium product sales (smartphones & expensive televisions).


A’s - price increase didn’t impact market

share

Unit Sales

B,C and D’s - increased revenues

471%Revenues Pro�tability

391%

366%

“The company reports that

these end-to-end

technologies have not only

improved pro�t, revenue, and

market share, but have

also increased the diversity




—Prof. David Simchi-Levi, Professor of Engineering Systems,

MITE’s – increased revenue by



ONLINE FLASH SALES:



$4B INDUSTRY


TEST #1:

10-12%

22%


Follow the Blog:




















TEST #2:





share

Unit Sales



391%

366%


these end-to-end












ONLINE FLASH SALES:



$4B INDUSTRY


TEST #1:

10-12%

22%


Follow the Blog:




















TEST #2:





share

Unit Sales



391%

366%


these end-to-end












ONLINE FLASH SALES:



$4B INDUSTRY


TEST #1:

10-12%

22%


Follow the Blog:




















TEST #2:





share

Unit Sales



391%

366%


these end-to-end












ONLINE FLASH SALES:



$4B INDUSTRY


TEST #1:

10-12%

22%


Follow the Blog:




















TEST #2:





share

Unit Sales



391%

366%


these end-to-end












ONLINE FLASH SALES:



$4B INDUSTRY


TEST #1:

10-12%

22%


Follow the Blog:




















TEST #2:





share

Unit Sales



391%

366%


these end-to-end










PERFORMING ON A LARGER STAGEBased on these results, Dr. Simchi-Levi and the Oracle Retail Science team were approached by a large, broad-spectrum online retailer who wanted to work with them to develop a solution that would simultaneously increase revenue, profit, and market share. After some interim testing, the retailer asked to have the algorithm applied to its premium product category, which includes smartphones and expensive televisions.

First-round results were not promising—the algorithm was heavily outperformed by the control group. The team optimized what they were doing to account for cannibalization by considering all products across the category. This done, final results for premium product sales governed by the algorithm were spectacular: revenue up 471%, profitability up 366%, and unit sales up 391%.

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“At a certain point, the

company was ready to

integrate our technology

with their system. Every

night we would download

data from the online

retailer’s ERP solution

about all the products

they were going to

sell the next day, and

fed it into the demand

forecasting technology to

generate a forecast.”


Systems, MIT

Cluster 3: Premium Products with Margin Optimization

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Since these ecommerce projects were carried out, Oracle Retail Science and the MIT team have implemented the technologies described here for bricks-and-mortar retailers as well. While the timing and other characteristics are different from online implementations, these technologies have led to dramatic improvements in the same KPIs—profitability, sell-through, revenue, and market share.

The Oracle Retail Science team is, of course, conducting research in other subjects as well. Areas in which the tools have been or are being developed include markdown promotion and offer optimization, returns optimization/store inventory rebalancing, social analytics and fraud detection.

Oracle strongly encourages retailers to participate in—and benefit from—these activities, whether as partners in the tool development such as discussed here or as collaborative participants in the Oracle Retail Science focus group. Contact us today to join the group.

Customers Who Read This Article Also Viewed

Blog: Escaping Cannibals and Forecasting the Unknown: Machine Learning Applications in Retail Pricing and Forecasting

Blog: R374IL 4N4LY71C5: Transforming the Retail Enterprise

CONCLUSION: USING MACHINE LEARNING TO OPTIMIZE PERFORMANCE

“We definitely want to keep

working with our retailers.

The collaboration

across our Workbench, the

universities’ research, our

research, and our retailers makes

for innovation and new

solutions that can be brought

to the market.”

—Troy Parent, Senior Director, Retail Science & Insights, Oracle Retail Science

mailto:[email protected]?subject=Interest%20in%20Oracle%20Retail%20Sciences%20Focus%20Group

https://blogs.oracle.com/retail/escaping-cannibals-and-forecasting-the-unknown%3a-machine-learning-applications-in-retail-pricing-and-forecasting?partnerref=GB

https://blogs.oracle.com/retail/escaping-cannibals-and-forecasting-the-unknown%3a-machine-learning-applications-in-retail-pricing-and-forecasting?partnerref=GB

https://blogs.oracle.com/retail/r374il-4n4ly71c5:-transforming-the-retail-enterprise?partnerref=GB

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November 2017

Oracle Corporation

World Headquarters

500 Oracle Parkway

Redwood Shores, CA 94065

U.S.A.

Find your local Oracle contact

number here:

http://www.oracle.com/us/corpo-

rate/contact/global-070511.html

Oracle.com/Retail

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