Learn how TigerGraph's real-time deep link analytics powered by a highly scalable graph database, combined with machine learning, is allowing eCommerce companies to: -Identify what a customer is looking for at a particular business moment -Recommend related items that will support customer needs and increase value of the final order -Recommend other items that align with the customer preferences, likes and dislikes to increase click-through rate and order value

1. TigerGraph for eCommerce
Increasing Revenue & Customer Loyalty With A Real-Time Product Recommendation
Engine

2. © 2018 TigerGraph. All Rights Reserved
Speaking Today
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Gaurav Deshpande
Vice President of Marketing
TigerGraph
gaurav@tigergraph.com, +1 510 388 2360
Big Data Analytics Veteran

3. © 2018 TigerGraph. All Rights Reserved
Agenda
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”Amazonification” of Retail & Stories of 3 retailers who
compete with and win against Amazon
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2
1
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Addressing eCommerce personalization
in Real-Time with Deep Link Analytics
Addressing personalization –
Traditional Approach
TigerGraph delivers the world’s fastest and most
scalable graph analytics platform for eCommerce

4. © 2018 TigerGraph. All Rights Reserved 4
The Networked
Age
Data Never Sleeps
Internet of things
BIG Data
Social Networks
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Amazonification Cometh
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Amazon grabbed 4% of all US Retail sales in 2017
Amazon had 44% of all U. S. eCommerce
sales in 2017
• Luxury beauty (up 47%)
• Grocery (up 33%)
Google narrows search gap with Amazon, Dec 2017
49% of online shoppers visit Amazon first
when searching for products
Fastest growing categories
• Pantry items (up 38%)
• Furniture (up 33%)One Click Retail 2017 Survey

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Fighting the Amazonification – What Works
• Customer Intimacy and
community
• Immersive personalized
shopping experience for
higher engagement
• Personalized curation for
the target buyer
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Decoding The Magic of Lululemon
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Can you Guess which pants cost $118 and which cost $11.99?

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Decoding The Magic of Lululemon
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$11.99 – Forever 21 $118 – Lululemon
• Understanding the
customer
• Immediate needs
• Interests & Preferences
• Values & Drivers
• Tailoring the
experience
• In-store and online
• Building the
community and brand
• Yoga classes every
Sunday morning
• Ambassador program

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Taking a Tip From Poshmark – Building Stickiness
One Click/Voice Command At a Time
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• Understanding the customer
• Browsing, search and order history à
Fashion Style Profile
• Tailoring the experience
• Start with the Why – Why is customer here
and how can we get her the right outfit
that matches her style & occasion
• Make it more convenient with Amazon
Alexa
• Choose one of the 9 Themes (date night,
casual weekend, cocktail party etc.)
• Match shopper style and size with 6
sellers
• Sellers create and place outfits in virtual
dressing room
• Shopper reviews outfits and makes the
purchase

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Wish.com - Competing and Winning Against
Amazon on Their Own Turf
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• Sixth largest eCommerce retailers in
the world
• Billions in revenue in just 3 years since
launch in 2011
• 300 million users across the world –
from Russia to Brazil to the United
States
• Understanding the customer
• Browsing, search and order history,
Likes, Reviews, Location, Device… ->
Real-time 360 Customer Profile
• Tailoring the experience
• Started as an app where people
make a wish list, and vendors provide
products to match
• Understand immediate need, present
additional product recommendations
based on interests and preferences

11. © 2018 TigerGraph. All Rights Reserved
Traditional Approach for Recommendation
• Product Likeness
• Category
• Application/Utility
• Substitution
• Complementary
• Customer Likeness
• Purchases by
other customers
who bought the
item
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Fidget Spinner

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Likes and Reviews
Search
History
Browsing
History
Order
History
Channel
Interaction
History
Demographic Device
Location
Interests
Preferences
Network
Needs
Values
Drivers
Real-Time
Customer 360
Profile
New Approach: Recommendations Based on a Real-
Time Customer 360 Profile

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Ecommerce Real-Time Personalized Recommendation
Recommendations change in real time as Recommendation Graph is updated and Customer’s journey evolves.
Person A Person C
Shops at Store Y
Bought item X online
from Store Y
Knows/likes/follows B B commented (to C)
Person B
C (also) shops at store Y
Is located at Z
Item X is stored
at Warehouse W
Item X has
tag/feature F
Customer
Visit Click Path
Would you like…?
B likes hobby H
Real-Time
Customer
360 Graph
Real-Time
Product
Graph
Real-time Recommendation Graph =
+

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Putting it all Together – Real-Time Product
Recommendation Engine Personalized for Target Buyer
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Asked a question
about light up toys
Gaurav
John
Browsing &
Search history
Order history
San Francisco
Device
used
Effort/DIY
Sensory
Interests
Superheroes
Variety
Preferences
Convenience

15. © 2018 TigerGraph. All Rights Reserved 15
Graphs – The Natural Model for
Interconnected Data
• Natural storage model for
modeling data and its
interconnections or
relationships
• Natural model for many
types of transactions
• Natural computational
model for
knowledge/inference/
learning – chaining and
combining observations

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The Why: Mission Statement
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Unleash the power of interconnected data
for deeper insights and better outcomes

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Native Graph Storage
Parallel Loading
Parallel Multi-Hop Analytics
Parallel Updates
(in real-time)
Scale up to Support Query
Volume
Privacy for Sensitive Data
Graph 1.0
Example – Neo4j
Graph 2.0
Example - DataStax
Graph 3.0
TigerGraph
Hours to load
terabytes
Sub-second
across 3-10+
hops
Mutable/
Transactional
2 billion+ per day
in production
Runs out of
time/memory
after 3+ hops
Scales for simple
queries (1-2 hops)
Times out
after 3+ hops
Days to load
Evolution of Graph
Databases
Days to load
MultiGraph
service
Download the benchark at - https://info.tigergraph.com/benchmark

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Delivering Complex Big Data Analytics with
TigerGraph
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Transactional (Mutable) Graph
Hundreds of thousands of updates per
second, Billions of transactions per day
Ease of Development & Deployment
Easy to use query language (GSQL) for developing &
deploying complex analytics in days with RESTful APIs
Privacy for Sensitive Data
Control access to sensitive data based on
user role, department or organization
Real-time Performance
Sub-second response for queries touching tens
of millions of entities/relationships
Scalability for Massive Datasets
100 B+ entities, 1 Trillion+ relationships
Deep Link Multi-Hop Analytics
Queries traverse 3 to 10+ hops deep into the
graph performing complex calculations

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Real-Time Graph Analytics Platform
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20. © 2018 TigerGraph. All Rights Reserved
Customers
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#1 e-payment company in the world,
100M daily active users
#1 US payment company
#1 Mobile E-commerce
#1 Mobile global supply-chain
#1 Power-Grid company in the world
the largest transaction graph in
production in the world (100B+ vertices,
2B+ daily real time update)
Business Graph
E-commerce
Supply-chain logistics
Electrical Power Grid
#1 world ride sharing Risk and Compliance

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Learn More about TigerGraph
• Download the TigerGraph for eCommerce
solution brief
https://www.tigergraph.com/resources/
• Read the benchmark report comparing
TigerGraph with older generation graph
solutions -
https://info.tigergraph.com/benchmark
• See TigerGraph in action – take the two week
free trial for full enterprise edition:
https://www.tigergraph.com/try-tigergraph/
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