Exhibitor Insights Presentation from Retail’s BIG Show, January 15-17, 2017. JOHN ANDREWS, CEO, Celect VIVEK FARIAS, CTO, Celect

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How Brick and Mortar Retailers Can
Beat Amazon at Its Own Game
VIVEK FARIAS, CTO, Celect
JOHN ANDREWS, CEO, Celect

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JOHN ANDREWS
CEO, Celect
VP Product, Oracle Commerce
VP Product & Marketing, Endeca
VIVEK FARIAS
Co-Founder & CTO, Celect
Robert N. Noyce Professor, MIT Sloan
PhD in EE, Stanford University
• Who we are
• Predictive analytics SaaS platform
for retail
• Based in Boston, MA
• Venture-backed, technology out of
MIT
• Awards & Recognitions
• MIT Computer Science and
Artificial Intelligence (CSAIL) Top
50 Greatest Innovations

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What is Amazon’s Game?
A network of DCs and last-
mile logistics partners that
work well at scale.
Distant fulfillment
center
Sortation center Delivery via Carrier
or Amazon Flex
Customer
City storefront Delivery via
Amazon Flex
Customer
Local fulfillment
center
Delivery via
Carrier
Customer
1.1 Billion
Orders per Year

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What is Amazon’s Game?
• RISK POOLING
• Demand for large
chunks of population
served out of a
relatively small set of
Fulfillment Centers
• Minimizes unsold
inventory
Indiana FC target area

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You are Not Amazon
Which isn’t a bad thing.
Here’s two reasons why…

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Reason #1
You are the expert in your domain

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Reason #2
You have a physical presence
• 42% of in-store customers
showroom, we must accept this
reality.
• Store location puts you much
closer to the customer.
Locations of Home Depot &
Lowes in the Tri-state Area
Source: http://www.planetizen.com/

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Leveraging these Advantages is Hard
You have a hot new line of graphic t-shirts launching next
season. Which scenario would you prefer?
Pooled Demand
An average demand of 3,000 units spread across 3 stores
OR
Diffused Demand
An average demand of 3,000 units spread across 1,000 stores

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Leveraging these Advantages is Hard
You’re stuck with Diffused Demand
and one of two things is happening:
• Inventory isn’t where you need it,
causing stock outs.
• Too much inventory was bought,
resulting in markdowns.

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The Solution: Virtual Pooling
Transforming an environment with Diffused Demand
into one with Virtually Pooled Demand.

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The Solution: Virtual Pooling
Requires two key ingredients:
1. Predictive Analytics
2. Real-Time Optimization

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Management System (OMS)
1,000 UNITS
AVAILABLE
A SIMPLE IN-STORE
PURCHASE
WALKS INTO
STORE
TO BUY A TV
PURCHASED
999 UNITS
AVAILABLE

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Virtual Pooling with a Typical OMS
AN ONLINE
PURCHASE
OMS
STORE
1
WAREHOUSE
STORE 2
CUSTOMER
ORDERS A TV
ONLINE
• Where do we fulfill the
order from?
• A store? A
warehouse?
• Which one and how
do we avoid a split
shipment?
-1
-1
-1
Short time to customer
High weeks of supply
Long time to customer
High weeks of supply
Short time to customer
Low weeks of supply

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Virtual Pooling: 4 Pieces of the Puzzle
1. Shipping Cost
• Closer is better
• Aggregated orders are better than split
orders
• Will typically conflict with other critical
objectives

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Virtual Pooling: 4 Pieces of the Puzzle
2. Throughput
• Process as many orders as possible
• Limited processing capacity at a stores
• Can raise shipping costs

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Virtual Pooling: 4 Pieces of the Puzzle
3. Delay
• Minimize the time it takes to get to a
customer
• Can raise shipping costs
• Conflicts with the ability to satisfy in-
store demand

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Virtual Pooling: 4 Pieces of the Puzzle
4. Average Weeks-of-Supply
• Ship out of locations with many weeks-of-
supply
• Related: Ship out onesies
• Speeds up inventory turns and maximizes
full price sell-through
• Conflicts with shipping costs

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Virtual Pooling: 4 Pieces of the Puzzle
Average
Weeks of
Supply

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a Typical OMS?
Issue #1: Typical OMS is purely rules driven.
Issue #2: Works well on a few high priority objectives, but
doesn’t scale well beyond that.
Issue #3: There’s no way of ‘sacrificing now’ for a future
gain.

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It’s Usually One Extreme or Another
WEEKSOFSUPPLY
(INVENTORY)
THROUGHPUT
There’s a balance between the
extremes – to maximize inventory
turns and utilization.
OMS RULE:
Maximize
Throughput
OMS RULE:
Maximize Weeks
of Supply

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WEEKSOFSUPPLY
(INVENTORY)
Virtual Pooling with a Predictive OMS
8%
5%
Real-time Optimization
True Demand across all channels
THROUGHPUT
O

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Case Study: Vertical Fashion Retailer
• Goal: Optimize Order Fulfillment with respect to the
following parameters:
• Throughput / Units Shipped: Maximize utilization of network
capacity
• Shipping Cost: Reduce shipping cost (ship closer and avoid
splitting
• Onesies Shipped: Increase fulfillment of returned units not part of
original store assortment
• Weeks of Supply: Maximize turns
• Average Order Delay: Increase customer satisfaction

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Case Study: Vertical Fashion Retailer
Representative Day Status Quo Celect % Diff.
Throughput (units) 1,171 1,307 11.6%
Unit Shipping Cost $5.05 $4.61 -8.8%
Onesies Shipped 549 777 41.6%
Weeks of Supply 7.6 17.9 135%
Average delay 0.047 days -0.15 days -0.2 days
Comparative Results

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What should you take away?
1. The key to Amazon’s success is better Risk Pooling
2. Brick and mortar retailers have fundamentally Diffused
Demand
3. Modern predictive analytics can transform this demand
and create Virtually Pooled Demand

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Predictive Analytics for
Retail

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The Most Fundamental Task in Retail
The Right Product
The Right Place
The Right Person
The Right Price
The Right Time
Goal: Avoid Stock-outs and Markdowns Solution: True Demand Prediction

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Understand True Demand
A Choice Model tells you what a customer would
prefer to buy when given the choice.
Today, you only know what a customer bought
✗✔
✗

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Celect Optimization Platform
Assortment
Optimization
Build robust assortments
specifically optimized for
the foot traffic in each
individual store
Predictive
Personalization
Real-time online
recommendations based
on individual customer
preferences
Markdown
Optimization
Optimize markdowns
while maximizing
conversions and
revenues
CELECT OPTIMIZATION PLATFORM
Fulfillment
Optimization
Fulfill from stores based on
customer demand, without
negatively impacting store
assortments

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Questions?
Email us: info@celect.com
www.celect.com