hortonworks - how hadoop makes the successful retailer

How Hadoop makesThe Successful Retailer

Mats JohanssonSolutions Engineer - EMEA

© Hortonworks Inc. 2011 – 2015. All Rights Reserved

About HortonworksONLY100

open sourceApache Hadoop data platform

% Founded in 2011

HADOOP1ST distribution to go public

IPO Fall 2014 (NASDAQ: HDP)

subscriptioncustomers+700

employees across800+

countries

technology partners1500+

17

TM

Hortonworks is the fastest ever public company to reach $100M in revenue.

Barclays, July 2015


Hortonworks Leads the Market

Hortonworks Is a Leader

• 75% of F100 Telcos• 65% of F100 P&C Insurers• 55% of F100 Manufacturers

• 46% of F100 Retailers• 40% of F100 Health Insurers

“The Forrester Wave™: Big Data Hadoop Solutions”

Hortonworks: Leads the market


Agenda

What is really going on in Retail

Data Explosion

Challenges

Retail Use Case


Challenges in Retail

Consumer Demand

Demand Signal

Management

Basket Analysis

Assortment Optimization

Path to Purchase

E-Commerce activity

Transferred to Physical Locations

Engaging Millennials

Sentiment Data

Social Listening

Targeted Promotions

“Market of one”

Cross Sell

Upsell

Consumer Loyalty

Spend History

Shared Sentiment

Trending styles

Supply Chain

Optimization

Quality Assurance

Sustainability

Back-office efficiency

ETL Offload

Market speedvs

ITs speed

Traditional Challenges


What is really going on in Retail?


They require answers, convenience and immediacy!

What is really going in Retail

Consumers are in control!

On Assortment, Product details, Price, Inventory, Availability and Reputation!


What do the Retailer get back?

What is really going in Retail

Demographics

and sometimes

Psychographics


?


#relationship


Customer 360

Single View of Customer


came

The search engine is the advisor

#interact

55% - of people 15-35 yearsuse search engines to learn aboutproducts.


#interact


came

Shopping behavior is changing beyond search

#interact

75% - The YoY growth in subscription of Youtube foodchannels from mobile devices.


Data Enrichment


came

Mobile is the go-to device

#mobile

75% - of people 15-35 yearsgo online via smartphone moreoften than computer.


came

The smartphone is the in-store assistant

#mobile

71% of in-store shoppers saytheir device has become moreimportant to their in-store experience.


Mobile


Agenda


Data Explosion

Challenges

Retail Use Case


#dataexplosion

Consumer Behavior• 2.5 Billion Connected People on Social networks by 2020• $65 Trillion (USD) Global Trade by 2020• 75 Billion Connected Devices by 2020• 2014 - 1 % of grocery retail revenue in Sweden were from online stores

Data Explosion• POS detail level and history

• Beacons, Smart Shelves, Smart Hangers, Smart Bins, Smart Racks, Smart…• Social Media, Tweets, Likes, Mentions, News• Clickstream, Web logs


Traditional systems under pressure

Clickstream

ERP, CRM, SCM

Web & Social

Geolocation

Internet of Things, Media

Server Logs

Files, Emails

New DataChallenges• Constrains data to app• Can’t manage new data• Costly to Scale

1

2.8 Zettabytesin 2012

44 Zettabytesin 2020

N E W

1 Zettabyte (ZB) = 1 million Petabytes (PB); Sources: IDC, IDG Enterprise, and AMR Research

Transform your industry via full fidelity of data in-flight, at rest and advanced analytics

Opportunity

T R A D I T I O N A L

LAGGARDS

LEADERS2

Business Value

ERP CRM SCM


Agenda


Data Explosion

Challenges

Retail Use Case


• Collect

• Store

• Protect

• Understand

• Analyze, compare and decide

• All in near real-time

Challenges


Hortonworks Data Platformpowered by Apache Hadoop

Hortonworks Data Platformpowered by Apache Hadoop

EnrichContext

Store Data and Metadata

Internetof Anything

Hortonworks DataFlow powered by Apache NiFi

Perishable Insights

HistoricalInsights

Hortonworks DataFlow Adds to Hadoop Capabilities


Only Hortonworks Delivers Open Enterprise Hadoop

HOR TONWOR K S D ATA P L AT FORM

YARN: Data Operating System

CLICKSTREAM SENSOR SOCIAL MOBILE GEOLOCATION SERVERLOG

Batch Interactive Search Streaming Machine Learning

EXISTING


Offering You the Most Flexibility

AN Y D ATAExisting and new datasets

A N Y A P P L IC AT IONMultiple engines for data analysis

A N YWH ER EComplete range of deployment options

Batch

Interactive

Search

Streaming

Machine Learning

Click-stream Sensor

Social Mobile

Geo-Location

ServerLog Linux Windows

CloudOn-Premise


Hortonworks Capabilities

The Data Flow Thing

Processand

AnalyzeCollect

Store & Integrate


New requirements

…to Activity InsightFrom reaction

…to OptimizationFrom planning

From break then fix

A shift from Reactive… …to Proactive

…to Preventive


Data Science


Iterative learning process

Visualize, Explore

Hypothesize;;Model

Measure/EvaluateAcquire Data

Clean Data

Question Answer


Tools


Agenda


Data Explosion

Challenges

Retail Use Case

© Hortonworks Inc. 2011 – 2014. All Rights ReservedPage 34 © Hortonworks Inc. 2011 – 2015. All Rights Reserved

Historical Records

OPEXReduction

Mainframe Offloads

Fraud Prevention

Dataas a

Service

Public Data Capture

IT executives are delivering substantial reductions in operating costs by modernizing their data architectures with Open Enterprise Hadoop. These cost saving innovations include active archive of cold data, offloading ETL processes and enriching existing data.

Digital Protection

Device DataIngest

Rapid Reporting


Payment Tracking

Call Analysis

Machine Data

Product Design

Social Mapping

Factory Yields

Defect Detection

Due Diligence

M & A Proactive Repair

Disaster Mitigation

Investment Planning

Next Product Recs

Store Design

Risk Modeling

Ad Placement

Inventory Predictions

Sentiment Analysis

Ad Placement

Basket Analysis Segments

Customer Support

Supply Chain

Cross-Sell

Customer Retention

Vendor Scorecards

Optimize Inventories

Business executives are driving transformational outcomes with next-generation applications that empower new uses of Big Data including: data discovery, a single view of the customer and predictive analytics.


Hortonworks® customers leverage our technology to transform their businesses, either by achieving new business objectives or by reducing costs. The journey typically involves both of those goals in combination, across many use cases.

Social Mapping

Payment Tracking

FactoryYields

Defect Detection

Call Analysis

Machine Data

Product Design M & A

Due Diligence

Next Product Recs

Store Design

Risk Modeling

Ad Placement

Proactive Repair

Disaster Mitigation

Investment Planning

Inventory Predictions

Customer Support

Sentiment Analysis

Supply Chain

Ad Placement

Basket Analysis Segments

Cross-Sell

Customer Retention

Vendor Scorecards


OPEX Reduction

Mainframe Offloads

Historical Records

Dataas a Service

PublicData Capture

Fraud Prevention

Device DataIngest

Rapid Reporting

Digital Protection


Zulily

• Zulily

– US online fashion, apparel and toys retailer– target audience are mothers and their children– daily-deals retailer – bringing active customers fresh products every day.

– 4.5 million customers– US$ 1B in revenue 2014


Challenges

• Zulily

– Zulily creates over 100 daily online events that launch more than 9000 unique products each day.

– more than 50% of Zulily’s orders come from mobile devices.

– data fragmented across multiple systems– impressions tells more than clicks


Solution

• Zulily

– All data managed in Hortonworks Open Enterprise Hadoop.– Creation of Millions individual stores – from cluster based to 1to1 personalization

– Relevancy seamless across all platforms and channels –from store to e-mail.

– 86 % increase YoY


Architecture

HDFS/HBase

Feeds

SQLStreaming

Search UISearch Index

Retail: Real Time Offer ManagementReal Time Inventory ManagementReal Time PersonalizationReal Time Stream Analytics


Customer Journey with Open Enterprise Hadoop

• Zulily

Store DesignIndividualization

Sentiment Analysis

BasketAnalysis


Next Product to BuyRecommendation

Historical Records

PublicData Capture

DeviceData Ingest

Customer Retention


Thank You!

Mats Johansson

[email protected]

@matsjo66

https://se.linkedin.com/in/matsjo66

hortonworks - how hadoop makes the successful retailer

Data & Analytics