mobility: it's time to be available for her

Mobility: Time to be available for HER!

Enabling Omni-Channel Retailing

#mongodbretail

Global Business Architect, MongoDB

Director, Solution Architecture, MongoDBEdouard Servan-Schreiber

Rebecca Bucnis

“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)

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

• 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

Introduction

The shopping process is fully mobile:

From awareness, to shopping, to check-out, to information source

Retail has evolved

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

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

Mandate #1. Prepare for Time, Geo-Spatial Selling

Informed, Digital Consumer Empowering Employees

Theme: Relevance and convenienceChallenge: Information availability & expectationsMongoDB Customers: Foursquare, 02, Retail giants, Verifone, Parse

Three Mobile Mandates

Single View of Business ≅ “Endless Aisle”

Theme: Product details & location up-to-minuteChallenge: Multiple vendors & legacy silo systemsMongoDB Customers: Staples, The Gap, Dillard’s, Bol.com

Three Mobile Mandates

Mandate #2. Find Her and ‘Know Her’

Theme: Selling Stage and ‘Persona’Challenge: Device proliferation, legacy silosMongoDB Customers: Foursquare, Panera, ASOS, Sitecore, Pearson

Three Mobile Mandates

Mandate #3. Tailor the Message

Theme: Every customer is unique

Challenges: Real-time delivery, insufficient connectivity to analytic information silosMongoDB Customers: Otto Germany, ASOS, Sitecore, Banking giants

Three Mobile Mandates

Then

In Store/Web Engage Anywhere

Check-out Help Selling Advice

General Adverts Tailored Messages

Now

Enabling agile delivery of seamless interactions & selling

14

Yesterday’s Tool for Today’s Data?

15

MongoDB Strategic Advantages

Horizontally Scalable-Sharding

AgileFlexible

High Performance &Strong Consistency

Application

HighlyAvailable-Replica Sets

{ customer: “roger”, date: new Date(), comment: “Spirited Away”, tags: [“Tezuka”, “Manga”]}

Notions

RDBMS MongoDB

Database Database

Table Collection

Row Document

Column Field

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 City0 John Doe New York1 Mark Smith San Francisco2 Jay Black Newark3 Meagan White London4 Edward Danields Boston

Order Number Store ID Product Customer ID10 100 Tablet 011 101 Smartphone 012 101 Dishwasher 013 200 Sofa 114 200 Coffee table 115 201 Suit 2

{ 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

{ 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

{ 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

{ 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

{ 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

West DC

Primary

Primary

Primary

Shard“West”

Shard“Center”

Shard“East”

Center DC East DC

Single View of Product Cluster Topology

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

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

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

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

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

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

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

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

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

•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

•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

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?

Questions?

Thank You!

@rebeccabucnis @edouardss

Resources

White Paper: Big Data: Examples and Guidelines for the Enterprise Decision Maker

http://www.mongodb.com/lp/whitepaper/big-data-nosql

Recorded Webinar Series: Thrive with Big Data

http://www.mongodb.com/lp/big-data-series

Recorded Webinar: What’s New with MongoDB Hadoop Integration

http://www.mongodb.com/presentations/webinar-whats-new-mongodb-hadoop-integration

Documentation: MongoDB Connector for Hadoop

http://docs.mongodb.org/ecosystem/tools/hadoop/

White Paper: Bringing Online Big Data to BI & Analytics

http://info.mongodb.com/rs/mongodb/images/MongoDB_BI_Analytics.pdf

Subscriptions, support, consulting, training

https://www.mongodb.com/products/how-to-buy

Resource Location