Calculating ROI with Innovative eCommerce Platforms

Preview:

Citation preview

Calculating ROI with Innovative E-Commerce Platforms

Enabling Omni-Channel Retailing

#mongodbretail

Global Business Architect, MongoDB

Director, Solution Architecture, MongoDBEdouard Servan-Schreiber

Rebecca Bucnis

“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

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

• 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

Introduction

Retail in a World with Amazon.com

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

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

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”]}

10

Information Management

Merchandising

Content

Inventory

Customer

Channel

Sales & Fulfillment

Insight

Social

Retail Architecture Overview

Customer

ChannelsAmazon

Ebay…

StoresPOSKiosk

MobileSmartphone

Tablet

Website

Contact Center

APIData and Service

Integration

SocialFacebook

Twitter…

Data Warehouse

Analytics

Supply Chain Management

System

Suppliers

3rd Party

In Network

Web Servers

Application Servers

1. Order Management & Fulfillment

Theme: Product location and availability up-to-minute

Business Benefits: Ability to make a sale!

Modern Ecommerce

12

Inventory

Inventory

MongoDB

External Inventory

Internal Inventory

Regional Inventory

Purchase Orders

Fulfillment

Promotions

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

> 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

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

2. Latest Information in Content & Product

Theme: Fresh and Engaging Content Low(est) Latency Business Benefits: Converting sale, ‘discover’ product,

drive revenue

Modern Ecommerce

Merchandising

Merchandising

MongoDB

Product Variation

Product Hierarchy

Pricing

Promotions

Ratings & Reviews

Calendar

Semantic Search

Product Definition

Localization

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

• 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

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.

3. Simplistic Back-End Integration

Theme: Connecting analytics to real-time execution

Business Benefits: Customer satisfaction, increased revenue

Modern Ecommerce

22

Insight

Insight

MongoDB

Advertising metrics

Clickstream

Recommendations

Session Capture

Activity Logging

Geo Tracking

Product Analytics

Customer Insight

Application Logs

23

Streams of User Activity

24

Activity logging - Architecture

MongoDB

HVDFAPI

Activity LoggingUser History

External Analytics:Hadoop,Spark,Storm,

User Preferences

Recommendations

Trends

Product MapApps

Internal Analytics:

Aggregation,MR

All user activity is recorded

MongoDB – Hadoop

Connector

Personalization

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

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

Dynamic queries on Channels

Channel

Sample Sample Sample Sample

AppApp

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

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.

Innovative Case Studies with ROI

• 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

• 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

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

• 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

Search & Navigate

Dynamic Product

Shop, Pages & Content

User Experience& Personalization

Customer Journey

Order Management

Focused Capabilities for E-Commerce

35

Press Release – Otto Germany

“With MongoDB, we chose a partner who could really support us in this process with MongoDB

consultants helping us in both design and training. As a result we have a modern, digitally-oriented application development environment which will allow us to implement our innovative ideas as

quickly as we create them.

We have made the right decision in opting for the leading NoSQL company in MongoDB.”- Mr. Peter Wolter – Head of Ecommerce Solutions

36

Executing Modern E-Commerce

R

even

ue P

ote

nti

al

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

Then

E-Commerce Island Integrated Fulfillment

Static Information Continual Refresh

Unknown Visitor Tailored Journey

Now

Enabling agile delivery of seamless interactions & selling

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?

Questions?

Thank You!

@rebeccabucnis @edouardssRebecca.bucnis@mongodb.com Edss@mongodb.com

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

Recommended