In order to meet the needs of the digitally-oriented consumer, retailers need to offer personalized service in real-time. By embracing mobile to deliver an integrated experience to customers, retailers can open new business opportunities. Yet, for many traditional retailers, providing a seamless experience across mobile and other channels presents challenges due to the limitations of legacy technology infrastructure and the ability to act in ‘real-time’. However, a new class of database technology is emerging that enables retailers to support new business requirements, improve customer experience and reduce cost. In the next session of webinar series - Omni-Channel Retailing: One-Step-at a Time you will learn why more and more retailers and ecommerce players are turning to MongoDB as a choice for their mobile platforms. Based on existing customers you will learn: How to meet the consumer where she is, whenever she wants - know where she is using geo-spatial services Engage with her and provide a ‘real-time’ experience, tailored to her expectations - check-her in or ‘check-her out’ at the POS and provide the latest update Deliver the most up-to-date information to your associates so they are empowered to serve the consumer when she engages with your brand - deliver the latest inventory information via mobile app to your employee In this discussion, you learn the latest in business techniques and how you can take advantage of MongoDB to deliver another piece of Omni-channel imperative - meeting your customer - at her convenience.

1. Mobility: Time to be available for HER!
Enabling Omni-Channel Retailing
#mongodbretail
Global Business Architect, MongoDB
Director, Solution Architecture, MongoDB
Edouard Servan-Schreiber
Rebecca Bucnis

2. “mobile is a lot closer to TV
than it is to desktop”
- Mark Zuckerberg
“mobile - it’s like assorting 100,000 stores
every minute, in the hand of
customers, every day…”
- Mickey Drexler
(paraphrased@ World Retail Congress – Oct 2013)

3. Presenters
Rebecca Bucnis
Global Business Architect
- Business Strategy
- Former Retailer
Amsterdam, The Netherlands
rebecca.bucnis@mongodb.com
@rebeccabucnis
Edouard Servan-Schreiber
Director, Solution Architecture
- Delivery of Solutions, Pre-Sales
- North America
New York, NY
edouard@mongodb.com
@edouardss
@rebeccabucnis @edouardss

4. • Introduction: Mobile Changes Everything
• 3 Mobile Mandates for Omni-Channel Retailing
• Why Use MongoDB for Mobile?
• Technical Deep-Dive – Going Mobile
• Customer Examples
• Wrap Up & Next Steps
Agenda

5. Introduction

6. The shopping process is fully mobile:
From awareness, to shopping, to check-out, to information source
Retail has evolved

7. 7
[Retail] Data has changed
• 90% of the world’s data was
created in the last two years
• 80% of enterprise data is
unstructured
• Unstructured data growing
2x faster than structured
Sources: IBM, Gartner 2012

8. 8
Retailers – Stuck in the Past?
More and
More
Devices &
Channels
Don’t
Understand
Customers
Source
Systems
are Siloed
Hard to
Adapt To
Change
Unable to
Execute in
Real-Time

9. Mandate #1. Prepare for Time, Geo-Spatial Selling
Informed, Digital Consumer Empowering Employees
Theme: Relevance and convenience
Challenge: Information availability & expectations
MongoDB Customers: Foursquare, 02, Retail giants, Verifone, Parse
Three Mobile Mandates

10. Single View of Business ≅ “Endless Aisle”
Theme: Product details & location up-to-minute
Challenge: Multiple vendors & legacy silo systems
MongoDB Customers: Staples, The Gap, Dillard’s, Bol.com
Three Mobile Mandates

11. Mandate #2. Find Her and ‘Know Her’
Theme: Selling Stage and ‘Persona’
Challenge: Device proliferation, legacy silos
MongoDB Customers: Foursquare, Panera, ASOS, Sitecore, Pearson
Three Mobile Mandates

12. Mandate #3. Tailor the Message
Theme: Every customer is unique
Challenges: Real-time delivery, insufficient connectivity to
analytic information silos
MongoDB Customers: Otto Germany, ASOS, Sitecore, Banking giants
Three Mobile Mandates

13. Then
In Store/Web Engage Anywhere
Check-out Help Selling Advice
General Adverts Tailored Messages
Now
Enabling agile delivery of seamless interactions & selling

14. 14
Yesterday’s Tool for Today’s Data?

15. 15
MongoDB Strategic Advantages
Horizontally Scalable
-Sharding
Agile
Flexible
High Performance &
Strong Consistency
Application
Highly
Available
-Replica Sets
{ customer: “roger”,
date: new Date(),
comment: “Spirited Away”,
tags: [“Tezuka”, “Manga”]}

16. Notions
RDBMS MongoDB
Database Database
Table Collection
Row Document
Column Field

17. 17
Customer View in a Document
Relational
MongoDB
{ customer_id : 1,
name : "Mark Smith",
city : "San Francisco",
location: [ <long> , <lat> ] ,
orders: [ {
order_number : 13,
store_id : 10,
date: “2014-01-03”,
products: [
{SKU: 24578234,
Qty: 3,
Unit_price: 350},
{SKU: 98762345,
Qty: 1,
Unit_Price: 110}
]
},
{ <...> }
]
}
CustomerID First Name Last Name City
0 John Doe New York
1 Mark Smith San Francisco
2 Jay Black Newark
3 Meagan White London
4 Edward Danields Boston
Order Number Store ID Product Customer ID
10 100 Tablet 0
11 101 Smartphone 0
12 101 Dishwasher 0
13 200 Sofa 1
14 200 Coffee table 1
15 201 Suit 2

18. { customer_id : 1,
name : "Mark Smith",
city : "San Francisco",
location: [ <long> , <lat> ] ,
Preferences: [ <category1>, <category2>,…],
Shopping-cart: [ {sku: …., count: ..}, …. ],
Next-best-offers: [ <offer1>, <offer2>, <offer3> ]
}
Customer View ready for Mobility

19. { customer_id : 1,
name : "Mark Smith",
city : "San Francisco",
location: [ <long> , <lat> ] ,
Preferences: [ <category1>, <category2>,…],
Shopping-cart: [ {sku: …., count: ..}, …. ],
Next-best-offers: [ <offer1>, <offer2>, <offer3> ]
}
Customer View ready for Mobility
Key for the document

20. { customer_id : 1,
name : "Mark Smith",
city : "San Francisco",
location: [ <long> , <lat> ] ,
Preferences: [ <category1>, <category2>,…],
Shopping-cart: [ {sku: …., count: ..}, …. ],
Next-best-offers: [ <offer1>, <offer2>, <offer3> ]
}
Customer View ready for Mobility
Key for the document
Allows geospatial
searches and real
time updates of
location

21. { customer_id : 1,
name : "Mark Smith",
city : "San Francisco",
location: [ <long> , <lat> ] ,
Preferences: [ <category1>, <category2>,…],
Shopping-cart: [ {sku: …., count: ..}, …. ],
Next-best-offers: [ <offer1>, <offer2>, <offer3> ]
}
Customer View ready for Mobility
Key for the document
Allows geospatial
searches and real
time updates of
location
Critical status
information to serve
her what she wants
at the right place and
time

22. { store_id: 342 ,
SKU: 4839475638 ,
prod_description: <...> ,
location: [ <longitude> , <latitude> ] ,
promotion: { discount: 25, start: <date>, end: <date> } ,
Attributes: {
color: ... ,
size: ... ,
...
} ,
Stock_level: 10,
Stock_available: 7,
Last_stock_udpate: ‘2014-03-02:13:02:36’
reorder_level: 5
Product View ready for Mobility

23. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Single View of Product Cluster Topology

24. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DCPrimary node replicates data
to all secondaries in the shard
as fast as possible
Single View of Product Cluster Topology

25. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Center Shard contains
all the data for stores
in Center region
“Single View” Cluster Topology

26. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Center Shard contains
all the data for stores
in Center region
Local writes enable
very high throughput
of updates
“Single View” Cluster Topology

27. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Each region is able to
see the data of all
stores from its “local”
DC.
“Single View” Cluster Topology

28. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Two nodes in each DC
for painless maintenance
with zero downtime
“Single View” Cluster Topology

29. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Even if a DC goes out, the
database remains fully available
thanks to automated failover
“Single View” Cluster Topology

30. West DC
Primary
Primary
Primary
Shard
“West”
Shard
“Center”
Shard
“East”
Center DC East DC
Data set can grow, shards can
add up, without any rewrite of the
application code
“Single View” Cluster Topology

31. Customer Examples: Retail Giants
• Mobile platform for
.com site of retail
giant
•Supports ongoing
demand for mobile
selling; revenue
increasing
•Manage heavy loads
and scaled for Black
Friday without issue

32. Customer Examples:
• Application provider
• Mobile retailing applications
• Working to provide hand-
held check-out capabilities
via mobile
• Chose MongoDB as the
platform to run their
applications
• Continue to develop and
maintain capabilities,
moving to customer loyalty

33. •A social messaging
platform
•Provides social and
geographic context,
with real-time analytics
•Built in 9 months on
MongoDB
•Support full view of
customers now at 15
million
Customer Examples

34. •A social platform
•Provides social and
geographic context to
people
•Entertaining them and
rewarding them for
business
•Manage check-ins and
capture & distribute
content with MongoDB
Customer Examples

35. 1. Assess your retail data and stage
2. Join us and Engage:
• Big Data Analytics - London – 19 June
• ROI: Innovation in Ecommerce – Webinar 11 June
• MongoDB World - New York – June 23-25
• Customer Experience Exchange – London 2-3 July
3. Start one step at a time - with “prototype” capabilities
What’s Next?

36. Questions?

37. Thank You!
@rebeccabucnis @edouardss

38. Resources
White Paper: Big Data: Examples and
Guidelines for the Enterprise Decision Maker
http://www.mongodb.com/lp/w
hitepaper/big-data-nosql
Recorded Webinar Series: Thrive with Big
Data
http://www.mongodb.com/lp/bi
g-data-series
Recorded Webinar: What’s New with
MongoDB Hadoop Integration
http://www.mongodb.com/pres
entations/webinar-whats-new-
mongodb-hadoop-integration
Documentation: MongoDB Connector for
Hadoop
http://docs.mongodb.org/ecosy
stem/tools/hadoop/
White Paper: Bringing Online Big Data to BI
& Analytics
http://info.mongodb.com/rs/mo
ngodb/images/MongoDB_BI_An
alytics.pdf
Subscriptions, support, consulting, training
https://www.mongodb.com/pro
ducts/how-to-buy
Resource Location