Shopin announced the solution to the lack of access to purchase data from all retailers ranging from Amazon, eBay to Macy’s, Nordstrom, Coach, Michael Kors and more, in the form of the Retail Intelligence Data Engine. R.I.D.E. (Retail Intelligence Data Engine) is a patent-protected innovation from Shopin’s team that has extracted and reverse-engineered the purchase data of online retailers from their websites and it measures the strength of influence of each retailer, brand or product upon each other, no matter where that product is sold. Shopin’s CEO Eran Eyal describes R.I.D.E. as a “Purchase Data Omniscience Engine”. Eran Eyal delves deeper: “In the past, only companies like Amazon have had sufficient purchase data to know which products to promote, recommend with each other, or which products to create. It’s important to note that 35% of Amazon’s revenue comes from their purchase data powered recommendations. That’s 17% of the United State’s total eCommerce revenue! We found a way to democratize and decentralize this data as well as over 80% of U.S. fashion e-commerce in scale. We don’t just deliver the data, we give you the predictive actionable recommendation.” Shopin released Q2 2019 figures that exceeded projections: 4BN+ Purchase data Transactions (value of over $400BN) 200MM+ product/ SKU cco-occurrence 300MM+ SKUs identified and tracked 150,000 Brands tracked Shopin brings a global perspective to retailers through an industry-wide transaction data fabric and unique analytics platform which leverages proprietary Visual AI and NLP technology. The interaction of their proprietary Visual Artificial Intelligence and Natural Language Processing models enables unique capabilities for the depth and breadth of their analysis and knowledge.

2. Our management team encompass over 15
decades of experience ranging from Senior AI
development at Walmart, a professor at M.I.T.,
The right through to building startups from the
ground up to their !rst $10MM of revenue or
taking mature
We’re a group of entrepreneurs
striving to ensure timeless brands
will remain timeless.
TEAM
leadership
Alexey Kyulkin
Senior Dev-Ops Engineer
Alexey served as a front-end engineer at Maker’s Brand
and as the backend engineer at Flow Health. Prior to his
startup career, he was formerly Head of Department
at Tomsk Polytechnic University.
Lane Campbell
Advisor: Strategy & Development
Lane is a lifelong entrepreneur who has realized !ve exits
in his career. He is a founding member of the Forbes Tech
Council and the CTO and CoFounder of Humble Advisors,
a wealth management !rm for high net worth
companies and individuals.
Eran Eyal
Co-CEO & Founder
Eran is a serial entrepreneur with three exits as a founder and
more than a decade of experience in retail and ecommerce.
He is the winner of the United Nations World Summit Award
for Innovation, Fast Company’s Most Innovative Startup, and
he is an investor and advisor for many startups.
Henrik Rasmussen
VP Biz Dev & Investor Relations
Henrik is an experienced technology executive who has
worked across several sub-sectors in technology related
industries as a business developer over the past 20 years.
Educated as a process engineer, Mr. Rasmussen has held
management positions in technology since the late 90’s and
has successfully branded and launched multiple products.
Prof Georgi Gospodinov
CTO
Georgi hails from Walmart, where he served in his tenure as a
leading data scientist and director of insights and analytics.
After earning his PHD in Mathematics, he turned his interests
to topological data analysis, complex networks, and dynamical
systems and worked as an applied mathematics professor in
Boston and NYC. Georgi also holds patents in blockchain
application in retail.
Vladimir Ustinov
Senior Dev-Ops Engineer
Vladimir has served as a senior engineer at Maker’s Brand,
Flow Health, EigenGraph, and other technology startups. He
holds a Master's Degree in Radio Physics and has a strong
data mining and numeric analysis background.

3. TEAM
core
Our team supported
by advisors who are
heavily involved on a
regular basis in driving
success.
Prof Richard Linares
Advisor
Professor Linares is a Charles Startk Draper. Assistant
Professor of Arti!cial Intelligence at M.I.T. Before his
tenure in Cambridge, he served as assistant professor
and University of Minnesota.
Mark Plaskow
Advisor
Mark Plaskow, founder of Scienti!c Clinics, is a Machine
Learning thought leader. He has recently worked with
some of the greats such as Sears and before
that companies like Ebay.
Amadeo Brenninkmeijer
Advisor
Amadeo is an accomplished angel investor with a strong
background in retail from the C&A family, Europe’s largest
Dutch retailer.

4. Amazon & Alibaba
35%
of Amazon’s revenue comes from
their product recommedation bar
driven by purchase data.
89%
Inconsistent
retailer data living in
legacy systems
3 out of 100
online shoppers actually
makes a purchase
40%
average return rate
Hacked
on average, more than 1
retailer hacked per month
Yet the rest of retail
doesn't look great…
Amazon
knows your customer better
than you do
1 out of 10
in-store shoppers actually
makes a purchase
“
”
of our revenue is attributable to
personalization based on
purchase data
- Je! Bezos

5. HOW DO WE
PROPOGATE
What the
MONOPOLIES like
Amazon have as a
choice...
And o!er every retailer,
brand & shopper as a
HUMAN RIGHT?

6. TECHNOLOGY
that drives conversions
RETAILER
Increase sales
conversions
Reduced attrition
Enhance security
SHOPPER
A new paradigm
in personalization
Tailored dynamic rewards
Ownership and security
Purchase Data
Visual AI
Blockchain

7. INTRODUCING

8. R.I.D.E. Retail
Intelligence Data Engine
Purchase data insights:
Forecast which
products are likely
to be bought with
products in your
existing catalogue.
Unveil the dangers
of removing brands
and products from
your catalogue.
4
transactions parsed
BN
45
SKUS
MM
150
Brands identi!ed
K
200
SKUS added daily
K
200
SKU co-occurrences
MM

9. How R.I.D.E. works
(provisional patents)
Actionable insights arise
Bought with
connections
identi!ed
SKUs are
identi!edAnalysis of purchase data
Deep data
analysis
Visual A.I. adds attributes
and normalizes SKU data
and connectionsConsumer Data direct from
retailer added (optional)
Data Lake
R.I.D.E. analyzes
the retail industry

10. NEW PERSPECTIVES
to a range of o!ers and information that in!uence
lifestyle choices and behavior, puts the company at a
severe disadvantage with the mega retailers like
Amazon
Limited access to the customer,
while the customer has constant
access across multiple channels
including brand store and ecommerce customers, as
well as at authorized resellers and ecommerce sites
Global customer base: Transition to a robust
customer-centric model
and personalization, and add an
experiential value proposition,
an essential part of the current retail
innovation and transformation:
to not only o"er a great product, but to
the right customer, when they want it,
for a good price, and through a positive
and gratifying shopping experience
(online or in store)

11. CapabilitiesR.I.D.E.
Product and category
pro!le analysis
Comparative global analysis
Brand and SKU co-occurrence
essential trends and behaviors
Identify strength, in"uence,
criticality of the brand
Identify big opportunities
that may be overlooked
Present state of a#airs and
predictive (future) insights
Examples of our data-driven
reasoning and intelligence
CALVIN KLEIN
ADIDAS
HANES
CASIO
FRUIT OF THE LOOM
YUMMIE
ING INTERNATIONAL
CONCEPTS
ERGODYNE
ALFANI
TIME AND TRU
US POLO ASSN
BINZI
MINI FOCUS
COFUO
FANMIS
SEIKO
US INVICTA ASSN
GEORGE
VASSARATTE
DICKIES
ATHLETIC WORKS
WRANGLER
GUESS
NEW YORK
DKNY
LAUREN RALPH
LAUREN
MICHAEL KORS
GUCCI
COACH
LEVIS
PUMA
TORY BURCH

12. Components:R.I.D.E.
Co-occurrence Model
ShopScore (entire industry)
i-ShopScore (internal)
Brand/ SKU Strength
Add/ Delete Recommendations
Marketing Recommendations
A global data fabric, with a Visual AI
and NLP powered analytics and
recommendation system
Customer-transaction
based source of truth
and intelligence
Brand-level intelligence (strength,
dominance, criticality, brand substitutes
and competitors) of over 90,000 brands
Category-level intelligence
(over 65 categories and 8
super-categories/classes)
SKU-level intelligence (over 40 million
SKUs and over 4 Billion transactions
represented in the data fabric)
Customer preference pro!le
model and recommendations
Visual AI
SKU Similarity Model
Universal Catalogue
Customer Preference Pro!le
Complete- the-look
Image Decomposition Model
Taxonomy Model
Brand & Category Taxonomy
Dynamic Model
Style & Pattern Categories
Occasion & Context Categories
01 04
02 05
03 06
Recommendation Engine + NLP

13. (unique SKUs, excluding colors/sizes)
Global Catalogue: Coach
This is a global view of the Coach brand
representation by unique SKUs across the
retail industry
The SKU data used for this analysis enables a
deeper look at the Coach categories, and the
portfolio balance at each retailer
The comparative brand and category analysis will
be used for describing and ranking the strongest
and most successful Coach portfolio
COACH
Farfetch (23.99%)
Macys (20.36%)
Saks!fth (17.39%)
Nordstrom (8.97%)
Walmart (98.97%)
Bloomingdales (7.35%)
Ebay (4.43%)
Amazon (3.72%)

14. Bags 41,77%
Wallets 34,54%
Sandals 7,63%
Shoes 4,82%
Beauty 2,01%
Shorts 1,61%
Other 0,4%
Jewelry 40,35%
Wallets 30,26%
Sandals 10,96%
Shoes 7,89%
Eyewear 4,39%
Beauty 3,51%
Jewelry 32,98%
Wallets 21,57%
Bags 18%
Eyewear 6,6%
Sandals 6,6%
Boots 4,63%
Shoes 2,85%
Beauty 1,96%
(unique SKUs, excluding colors/sizes)Global Catalogue: Coach
40.4% 33.0%
The biggest di!erence is in the
top category: at Bloomingdale’s,
bags come in at:
at Nordstrom exhibit a heavy
focus on jewelry with
and at Macy’s, we
have jewelry at
The Coach portfolio at all three mega-retailers is dominated by bags, wallets, and jewelry, with sandals
and eyewear coming at fourth place.
We are able to superimpose those with customer conversion data
(transaction data) and draw conclusions about customer choices
and preferences.
Smaller retailers show even bigger variations of the Coach’s
portfolio, categories like bags often move over 50%. These
are di!erent faces of the global Coach catalogue.
41.8%

15. MichaelKors
USPoloASSN
KateSpadeNewYork
KateSpadeNewYork
GentleSoulsByKennethCole
UGG
GUCCI
RebeccaMinko!
Longchamp
ToryBurch
MARCJACOBS
MichaelKors
Skagen
ToryBurch
Gucci
Prada
SaintLaurent
SalvatoreFerragamo
SamEdelman
TedBakerLondon
28.8%
7.6%
29.5%
10.5%
9.2%
8.4%
8.0%
6.7%
4.6%
3.4%
2.7%
2.3%
2.1%
2.1%
4.9%
3.7%
3.7%
3.3%
2.7%
2.7%
Then we have the ability to
zoom in and look at the
category and SKU level
At this level, we can see signi"cant patterns
and trends that represent shift and direction
of customer preferences and purchase
behavior at a retailer and across the industry.
Brand to brand co-occurrence is
an aggregate view of all co-
occurring SKUs from a pair of
brands.
Brand Co-Occurrence

16. Brand Co-Occurrence: CoachCOACH
KateSpadeNewYork
UGG
GUCCI
RebeccaMinko!
Other
COACH
KateSpadeNewYork
UGG
GUCCI
RebeccaMinko!
Other
COACH
KateSpadeNewYork
UGG
GUCCI
RebeccaMinko!
Other
Bloomingdale’s shows a moderate self-co- occurrence
at the brand level, which means that Coach customers
shop for other categories and brands at the retailer.
This highlights opportunities for basket analysis and
category expansion for Coach.
A balanced brand co-occurrence at
Nordstrom indicates opportunities for
growth and potential for expansion
within the customer base.
Macy’s shows an overwhelming self-co-
occurrence at the brand level for Coach,
which means customers shop speci"cally
for the brand at the retailer.
The customer base is anchored entirely
on the brand, and customers are buying
complementary categories or
accessories elsewhere.

17. Brand Co-Occurrence: CoachKateSpadeNewYork
UGG
GUCCI
RebeccaMinko!
Longchamp
Other
KateSpadeNewYork
UGG
GUCCI
RebeccaMinko!
Longchamp
Other
KateSpadeNewYork
UGG
GUCCI
RebeccaMinko!
Longchamp
Other
Analysis of brand interactions for Coach with
other brands.
Kate Spade New York and Gucci seem to
be the top support for Coach at Macy’s.
Kate Spade New York and Gucci seem
to be the top support for Coach at
Nordstrom.
Bloomingdale’s shows a strong a unique
interaction between Coach and Kate
Spade New York. This shopping behavior
indicates strong complementary or even
competitive dynamics between the
brands. Further analysis will reveal the
details and opportunities for Coach.

18. Brand and category
co-occurrence
Dominant category co-occurrences
are represented by scaled
thicker connections.
UGG®
Women's Bandara Round Toe Leather
kate spade new york
Small Quilted Bifold Wallet
COACH
Women's Melody Pointed-Toe Booties COACH 1941
1941 Willis Leather Satchel
COACH
Skinny Continentall Leather Wallet
MICHAEL Michael Kors
Medium Cece Leather Shoulder Bag
COACH
Parker Leather Shoulder Bag
MARC JACOBS
Snapshot Leather Camera Bag
Tory Burch
Kira Small Chevron Camera Crossbody

19. Brand and category
co-occurrence (cont’d)
Dominant category co-occurrences are represented
by scaled thicker connections.
We observe significant Coach category co-occurrences with the same
categories from other brands, specifically Kate Spade New York (wallets),
UGG (boots), Michael Kors (bags), Tory Burch (bags), and Marc Jacobs (bags).
Although the data
indicates that 90.1%
of Coach wallet co-occurrence
connections are to wallets of Kate
Spade New York, there are certainly
instances where a Coach wallet is
purchased with a di!erent category
and a di!erent brand.
So the 90.1% !gure shows that
from the statistically signi!cant
instances, customer purchase Coach
and Kate Spade New York together in
the wallets category.
Cooc_pctRetailer Category1 Category2Cooc_brandBrand
COACH BLOOMINGDALES wallets wallets 60.76KATE SPADE NEW YORK
COACH BLOOMINGDALES bags bags 12.28LONGCHAMP
COACH BLOOMINGDALES bags bags 5.57SALVATORE
FERRAGAMO
COACH BLOOMINGDALES wallets wallets 30.17COACH
COACH BLOOMINGDALES bags bags 8.45MICHAEL KORS
COACH BLOOMINGDALES wallets wallets 10.56COACH
COACH BLOOMINGDALES jewelry jewelry 58.56GUCCI
COACH BLOOMINGDALES bags bags 7.68MARC JACOBS
COACH BLOOMINGDALES boots boots 90.70UGG
COACH BLOOMINGDALES bags bags 7.87TORY BURCH
COACH BLOOMINGDALES jewelry jewelry 41.44REBECCA MINKOFF
This indicates existence of substitute at the category level and an opportunity
for adjusting the brand’s category profile at Bloomingdale’s.

20. Bags 41,77%
Wallets 34,54%
Sandals 7,63%
Shoes 4,82%
Beauty 2,01%
Shorts 1,61%
Other 0,4%
Jewelry 40,35%
Wallets 30,26%
Sandals 10,96%
Shoes 7,89%
Eyewear 4,39%
Beauty 3,51%
Jewelry 32,98%
Wallets 21,57%
Bags 18%
Eyewear 6,6%
Sandals 6,6%
Boots 4,63%
Shoes 2,85%
Beauty 1,96%
Brand Strength
Coach is strongest and best positioned for growth at
Bloomingdale’s, according to the R.I.D.E. metric brand
strength which takes into account the customer base and
SKU-cooccurrences relative to all other brands at the retailer.
A stronger brand typically has many mixed-brand and
mixed-category connections, from large and diverse
customer baskets, as opposed to a dedicated one-item/
one-brand shopper.
MACYS COACH 0.109 0.349 0.167
Brand Retailer Brand_strength Brand_in!uence Critical_brand
BLOOMINGDALES COACH 0.669 0.787 0.586
NORDSTORM COACH 0.346 0.363 0.048

21. Critical Brands
A critical brand is a brand which is a “hub”, bridging
other brands and their customer bases in a unique
and critical way.
Brand Retailer Brand retailer_brand_strength Retailer_brand_hub
BLOOMINGDALES 0.4480252 1.0000000TORY BURCH
BLOOMINGDALES 0.7510386 0.8609072PAIGE
BLOOMINGDALES 0.7393817 0.7665254GUCCI
BLOOMINGDALES 0.7942386 0.6916556J BRAND
BLOOMINGDALES 0.7802160 0.62153107 FOR ALL MANKIND
BLOOMINGDALES 0.4113320 0.6076948SPANX
BLOOMINGDALES 0.1703310 0.5925210THEORY
BLOOMINGDALES 1.0000000 0.5854719ADIDAS
BLOOMINGDALES 0.4117046 0.5668006UGG
BLOOMINGDALES 0.8218821 0.9164843KATE SPADE NEW YORK
BLOOMINGDALES 0.1978662 0.7987277AQUA
BLOOMINGDALES 0.3623303 0.7323363CALVIN KLEIN
BLOOMINGDALES 0.7942841 0.6705062AG
BLOOMINGDALES 0.4334146 0.6105946BURBERRY
BLOOMINGDALES 0.0789601 0.6017752WHISTLES
BLOOMINGDALES 0.5865544 0.5678749MZ WALLACE
BLOOMINGDALES 0.2487048 0.5626138MICHAEL KORS
COACHBLOOMINGDALES 0.6686604 0.5858621
For COACH at Bloomingdales,
having opportunities to grow the
brand (increase strength) and having
already high criticality, means that
the relationships to other brands,
even if not the most, are very
important for Bloomingdales.
Easiest way to interpret criticality is
through the scenario of removing the
brand, the impact would be very
significant beyond its own customer
base due to the unique strategic
position of the brand, so we expect
losses of baskets/trips, or even loss
of adjacent sectors of the retailer's
customer base
Typically, the strongest brands are also critical, but not always. Coach is an
example of the converse at Bloomingdale’s, and this shows a particular kind
of customer base.
42%
31%
27%

22. Your
future with
QUESTIONS /
NEXT STEPS
Eran Eyal
eran@shopin.com