2017 MITX Data Summit: John Andrews, Celect

YOUR DATA IS

SCREAMING AT YOU:

LEARNING TO LISTEN

THROUGH CHOICE

MODELING

J O H N A N D R E W SCEO, Celect

@celect

# M I T X D A T A

Your Data is Screaming at

You!Learning to Listen Through Choice

Modeling

2017 MITX Data Summit

June 14, 2017

• 20 Years optimizing supply chains with a focus on omni-channel

commerce

• Celect exists to provide a SaaS platform to help enterprises optimize

their inventory portfolios

• By bringing science to art of retail, we regularly see 10%+ improvements

in revenue

• An MIT AI Lab Top 50 Technology Innovation

3 © 2017 Celect, Inc. All Rights Reserved.

A VERY BRIEF INTRODUCTION

RETAIL TRANSFORMATION

© 2017 Celect, Inc. All Rights Reserved.4


TECHNOLOGY

CHANNELS

SHOPPING BEHAVIOR

HUNDREDS OF STORES CLOSING

ACROSS MAJOR RETAILERS



5 BIGGEST CHAINS

CLOSED 20%

OF THEIR STORES

IN THE LAST 3

YEARS


DATA OVERLOAD

Inventory, POS,

Browse History,

Transaction Logs,

Search, etc.

BLURRED

PHYSICAL &

DIGITAL

The Shift from Silos

to Integrated…

Harder than You

Think

AREA OF

OPPORTUNITY

Improved Inventory

Optimization

CONTEXT

• $600M spent annually on Shoes

• 90K Shoe ‘types’ or SKUs (style/color/size)

• 100 Stores (with room for 5K Shoe SKUs)

• 10M unique customers


Goal: Avoid Stock-outs and Markdowns Solution: True Demand Prediction

THE COMPLEXITY OF BUYING SHOES

Plan Buy Allocate Market/Sell Liquidate

10

Am I investing

in the right

areas?

Will I over-buy

or under-buy?

Is the product

in the right

stores?

Will I maximize

full price sell-

through?

How much

margin will we

lose?

Data-driven Decisions Optimized Assortments Optimized Allocation Engaged Customers Optimized Markdown

© 2017 Celect, Inc. All Rights Reserved.

OPTIMIZED DECISION MAKING

EXPECTATIONS HAVE CHANGED


Your customers expect everything, everywhere, all the time.

THIS

STILL

EXISTS


DECISIONS HAVE BEEN

MADE THIS WAY FOR YEARS

1980s 1990s 2000s Today

SO WHY CHANGE NOW?


DECISIONS HAVE BEEN

MADE THIS WAY FOR YEARS

1980s 1990s 2000s Today

90% OF DATA TODAY CREATED IN

THE LAST 2 YEARS

PRESSURES

SHRINKING MARGINS / PROFIT

X-CHANNEL INVENTORY

CHALLENGES

REDUCED FOOT TRAFFIC

DEMAND UNCERTAINTY© 2017 Celect, Inc. All Rights Reserved.14


RETAIL TRANSACTION DATA


DATA ABOUT EACH CUSTOMER IS SPARSE

Understanding

CHOICE


©2017 Celect, Inc. All Rights Reserved.18

A SIMPLE QUESTION

Comparisons are eas(ier) …

… but also, comparisons

are everywhere.


CONTEXT VIA COMPARISONS


Traditional Reviews: Peter prefers Tamarind to Cafe Boulud

Peter G.

Peter G.’s Profile

About Peter G.

Peter G.

COMPARISONS ARE EVERYWHERE

21

Click Stream Data

©2017 Celect, Inc. All Rights Reserved.


22

Click Stream Data: Vinay is more in the mood for

The Expendables 3 vs. Cosmos or The Bank Job or ...

?©2017 Celect, Inc. All Rights Reserved.



(Brick and Mortar) Retail Data


24

(Brick and Mortar) Retail Data: I prefer Neutrogena to Dove,

and Aveeno, and …



WOULD YOUR

CUSTOMERS LIKE THIS

HOODIE?


WHICH WOULD

SELL BETTER IN

YOUR CHICAGO

STORE?


DOES THIS JACKET HAVE

A CANNIBALISTIC

EFFECT?

EXPECTATIONS HAVE CHANGED


Your customers expect everything, everywhere, all the time.

THIS

WON’T

TELL YOU

THAT(and is useless)


UNDERSTAND

TRUE DEMAND

29

Representative Customer


A MENTAL PICTURE OF A CHOICE

MODEL

30

30% 30% 7% 7% 20% 6%


A GENERIC CHOICE MODEL

31

= 30+30 = 60%

30% 30% 7% 7% 20% 6%



32

30% 30% 7% 7% 20% 6%



33

= 30+30+20 = 80%

30% 30% 7% 7% 20% 6%



34

$200

$300

$100

30% 30% 7% 7% 20% 6%



35

$100 $100$200 $200 $300 $300

30% 30% 7% 7% 20% 6%



36

30% 30% 7% 7% 20% 6%

$100 $100$200 $200 $300 $300


$200 $300


37

$200 $300$200 $200 $300 $300

30% 30% 7% 7% 20% 6%



© 2017 Celect, Inc. Confidential. Do Not Distribute.38

A Choice Model tells you what a customer would

prefer to buy when given the choice.

Today, you understand what your customer bought.

✔

✗

✗What if you also

knew what they

didn’t buy, but had

the option to?

TLOG

PRODUCTS

BROWSE

SHIPMENTS & RECEIPTS

LOCATIONS

INVENTORY

PREDICTING CUSTOMER CHOICE

THE BENEFIT


PREDICTIVE ANALYTICS

DATA IS GOLD


SOLUTION:‒ THE POWER OF CHOICE & CONTEXT

‒ TENSOR COMPLETION ACROSS SPARSE, HETEROGENEOUS DATA

‒ HIGH-SCALE, INTERACTIVE OPTIMIZATION


GROUNDBEAKING ADVANCE

IN MACHINE LEARNING

© 2017 Celect, Inc. Confidential. Do Not Distribute.43

PLAN

OPTIMIZATION

BUY

OPTIMIZATION

FULFILLMENT

OPTIMIZATION

INVENTORY OPTIMIZATION

Luxury Department Store

$4.5M in incremental store

revenue for Salon Shoes

111% full price sell-through

increase

Specialty Apparel Retailer

6% projected revenue

increase

$32M projected over one

quarter

3% increase in buy accuracy

Specialty Shoe Retailer

>$3M forecasted impact

127% increase in ‘weeks of

supply’

26% decrease in split

shipments

25% increase in throughput

ROI

‘WE HAVE NOW REACHED A TIPPING POINT WITH

CONSUMERS EXPECTING EVERYTHING AVAILABLE

EVERYWHERE ALL THE TIME – MEETING THIS

EXPECTATION WHILE SIMULTANEOULSY

REDUCING INVENTORY IS SIMPLY IMPOSSIBLE

WITHOUT ADVANCED ANALYTICS.’

- B RYA N E S H E L M A N , C O O A L D O G R O U P


THANK YOU!


John Andrews [email protected]

www.celect.com

Visit blog.celect.com

Follow us on Twitter! @celect

mailto:[email protected]

http://www.celect.com/screaming-data-webcast

http://blog.celect.com/

https://www.twitter.com/celect

2017 MITX Data Summit: John Andrews, Celect

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