The document discusses how MongoDB can help companies enable omni-channel retailing and calculate ROI with innovative e-commerce platforms. It describes how MongoDB allows for up-to-date product availability and information across channels in real-time through flexible schemas and horizontal scaling. Case studies are presented showing how companies like Otto Germany were able to build custom e-commerce platforms on MongoDB faster and with more agility and performance than traditional systems. The presentation concludes by encouraging companies to start prototyping omni-channel capabilities using MongoDB.

1. Calculating ROI with Innovative
E-Commerce Platforms
Enabling Omni-Channel Retailing
#mongodbretail
Global Business Architect, MongoDB
Director, Solution Architecture, MongoDB
Edouard Servan-Schreiber
Rebecca Bucnis

2. “Amazon.com strives to
be the e-commerce
destination where
consumers can find and
discover anything they
want to be buy online.
- Jeff Bezos, founder

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
• Demands of Modern E-Commerce
• Why Use MongoDB for E-Commerce
• Technical Capabilities and Enablers
• Innovative Case Studies with ROI
• Wrap Up & Next Steps
Agenda

5. Introduction

6. Retail in a World with Amazon.com

7. 7
Customer-Centric E-Commerce
1. Product Available? Product Anywhere
• Order Management & Fulfillment
2. Continually Fresh Content & Information
• Detailed product, pricing & UGC
3. Multi-Channel Integration
• Back-end systems inclusive
Based upon Forrester Wave - BtoC Commerce, 2013

8. 8
Disconnected Ecommerce > ROI
Speed to
Innovation
is Slow….
Inventory &
Fulfillment
more
complex
Single
Channel
Systems
(or Siloed)
Unable to
Execute in
Real-Time
Static
Informatio
n

9. 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”]}

10. 10
Information
Management
Merchandising
Content
Inventory
Customer
Channel
Sales &
Fulfillment
Insight
Social
Retail Architecture Overview
Customer
Channels
Amazon
Ebay
…
Stores
POS
Kiosk
…
Mobile
Smartphone
Tablet
Website
Contact
Center
API
Data and
Service
Integration
Social
Facebook
Twitter
…
Data
Warehouse
Analytics
Supply Chain
Management
System
Suppliers
3rd Party
In Network
Web
Servers
Application
Servers

11. 1. Order Management & Fulfillment
Theme: Product location and availability up-to-minute
Business Benefits: Ability to make a sale!
Modern Ecommerce

12. 12
Inventory
Inventory
MongoDB
External Inventory
Internal Inventory
Regional Inventory
Purchase Orders
Fulfillment
Promotions

13. 13
Demonstration Document Model
Definitions
• id: p0
Variations
• id: sku0
• pId: p0
Summary
• id: p0
• vars: [sku0,
sku1, …]
Stores
• id: s1
• Loc: [22, 33]
Inventory
• store: s1
• pId: p0
• vars:
[{sku: sku0, q: 3},
{sku: sku2, q: 2}]
Product

14. 14
> db.inventory.findOne()
{ "_id": "5354869f300487d20b2b011d",
"storeId": "store0",
"location": [
-86.95444,
33.40178
],
"productId": "p0",
"vars": [
{ "sku": "sku1", "q": 14 },
{ "sku": "sku3", "q": 7 },
{ "sku": "sku7", "q": 32 },
{ "sku": "sku14", "q": 65 },
...
]
}
Inventory - Quantities

15. Order Management & Fulfillment
Technical Challenges MongoDB Solution
• Cannot see the up to date inventory
by store as inventory is updated in
batch processes
• Inventory details are stored in
systems which cannot handle the
load of massive distributed reads
• Need efficient geospatial lookups
to find cheap fulfillment options
• Fast in-place updates able to
handle heavy load of real-time
changes
• Leveraging RAM for hot data
systematically and able to fulfill
massive concurrent reads
• Geospatial indexing enabling easy
search of inventory through nearby
stores

16. 2. Latest Information in Content & Product
Theme: Fresh and Engaging Content Low(est) Latency
Business Benefits: Converting sale, ‘discover’ product, drive revenue
Modern Ecommerce

17. Merchandising
Merchandising
MongoDB
Product Variation
Product Hierarchy
Pricing
Promotions
Ratings & Reviews
Calendar
Semantic Search
Product Definition
Localization

18. 18
Price: {
_id: <unique value>,
productId: "301671", // references product id
sku: "730223104376", // can reference specific sku
currency: "us-dollar",
price: 89.95,
storeGroup: "0001", // main store group
storeId: [ "1234", "2345", … ] // per store pricing
lastUpdated: Date("2014/04/01"), // last update time
…
}
Indices: productId + storeId, sku + storeId,
storeId + lastUpdated
Merchandising – Pricing

19. 19
• Get Variation from SKU
db.variation.find( { sku: "730223104376" } )
• Get all variations for a product, sorted by SKU
db.variation.find( { productId: "301671" } ).sort( { sku: 1 } )
• Find all variations of color "Blue" size 6
db.variation.find( { attributes: { $all: [ { color: "Blue" }, { size: 6 } ] } )
• Indices
sku, productId + sku, attributes, lastUpdated
Merchandising - Pricing

20. Continually Fresh Content &
Information
Technical Challenges MongoDB Solution
• Enabling numerous price changes
intra day and high granularity (per
store/channel pricing)
• Collecting and rendering users’
product reviews
• Welcoming new content and be able
to serve it right away
• Changing the site structure and
content within hours of decision
• Fast updates to a pricing structure
within a rich JSON document for
maximum flexibiity
• Able to take massive writes of
loosely structured data
• Storing of content using GridFS for
high availability and fast retrieval
• Flexible schema for easy custom
changes.

21. 3. Simplistic Back-End Integration
Theme: Connecting analytics to real-time execution
Business Benefits: Customer satisfaction, increased revenue
Modern Ecommerce

22. 22
Insight
Insight
MongoDB
Advertising metrics
Clickstream
Recommendations
Session Capture
Activity Logging
Geo Tracking
Product Analytics
Customer Insight
Application Logs

23. 23
Streams of User Activity

24. 24
Activity logging - Architecture
MongoDB
HVDF
API
Activity Logging
User History
External
Analytics:
Hadoop,
Spark,
Storm,
…
User Preferences
Recommendations
Trends
Product Map
Apps
Internal
Analytics:
Aggregation,
MR
All user activity
is recorded
MongoDB –
Hadoop
Connector
Personalization

25. 25
{ _id: ObjectId(),
geoCode: 1,
sessionId: "2373BB…",
device: { id: "1234",
type: "mobile/iphone",
userAgent: "Chrome/34.0.1847.131"
}
type: "VIEW|CART_ADD|CART_REMOVE|ORDER|…",
itemId: "301671",
sku: "730223104376",
order: { id: "12520185",
… },
location: [ -86.95444, 33.40178 ],
timeStamp: Date("2014/04/01 …")
}
User Activity - Model

26. 26
Dynamic schema for sample data
Sample 1
{
deviceId: XXXX,
time: Date(…)
type: "VIEW",
…
}
Channel
Sample 2
{
deviceId: XXXX,
time: Date(…)
type: "CART_ADD",
cartId: 123, …
}
Sample 3
{
deviceId: XXXX,
time: Date(…)
type: “FB_LIKE”
}
Each sample
can have
variable fields

27. 27
Dynamic queries on Channels
Channel
Sample Sample Sample Sample
App
App
App
Indexes
Queries Pipelines Map-Reduce
Create custom
indexes on
Channels
Use full mongodb
query language to
access samples
Use mongodb
aggregation
pipelines to access
samples
Use mongodb
inline map-reduce
to access samples
Full access to
field, text, and geo
indexing

28. Multi-Channel Integration
Technical Challenges MongoDB Solution
• Original legacy source systems are
rigid, inflexible and do not easily
exchange information
• Need to add a new data source on
very short notice to get larger view
of customers
• Keep history of customer
information in loosely structured
form for deep analytics
• Ability to maintain original source
systems, yet create a blended view
without ‘rip and replace’
• Flexible schema for easy custom
changes and enhancements to
customer profile
• Massive scaling on demand to
keep historical data for as long as
needed.

29. Innovative Case Studies with ROI

30. • Built custom ecommerce
platform on MongoDB in
8 Months
•Fast time to market
•Database can meet
evolving business needs
•Superior user experience
ROI = Original innovation,
performance & flexibility
Customer Examples

31. • Delivered agile
automated supply chain
service to online retailers
powered by MongoDB
•Decreased supplier
onboard time by 12x
•Grew from 400K records
to 40M in 12 months
•Significant cost
reductions
Customer Examples

32. Compatibility Matching System
used to match potential
partners
“With our...SQL-based system, the
entire user profile set was stored on
each server, which impacted
performance and impeded our ability
to scale horizontally.
MongoDB supports the scale that our
business demands and allows us to
generate matches in real-time.”
Thod Nguyen, CTO, eHarmony
95% Faster Matches

33. 33
• www.otto.de
• €2.5bn eCommerce
site
• Largest web property
for female and child
clothing in Europe
• 1998 – 2013: based
on Intershop
Otto Germany

34. 34
Search & Navigate
Dynamic Product
Shop, Pages & Content
User Experience
& Personalization
Customer Journey
Order Management
Focused Capabilities for E-Commerce

35. 35
Press Release – Otto Germany

36. 36
Executing Modern E-Commerce
RevenuePotential
Product Availability Unclear/ Can’t deliver
Product Available – Deliver without insight
Some products available
Unavailable; went to store
Product Available - Deliver Anywhere with insight
Time to Execution

37. Then
E-Commerce Island Integrated Fulfillment
Static Information Continual Refresh
Unknown Visitor Tailored Journey
Now
Enabling agile delivery of seamless interactions & selling

38. 1. Assess your retail data and omni-channel capabilities
2. Join us and Engage:
• Big Data Analytics - London – 19 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?

39. Questions?

40. Thank You!
@rebeccabucnis @edouardss
Rebecca.bucnis@mongodb.com Edss@mongodb.com

41. 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