hortonworks - how hadoop makes the successful retailer
TRANSCRIPT
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
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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
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Agenda
What is really going on in Retail
Data Explosion
Challenges
Retail Use Case
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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
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What is really going on in Retail?
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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!
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What do the Retailer get back?
What is really going in Retail
Demographics
and sometimes
Psychographics
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?
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#relationship
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Customer 360
Single View of Customer
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came
The search engine is the advisor
#interact
55% - of people 15-35 yearsuse search engines to learn aboutproducts.
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#interact
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#interact
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came
Shopping behavior is changing beyond search
#interact
75% - The YoY growth in subscription of Youtube foodchannels from mobile devices.
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Data Enrichment
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came
Mobile is the go-to device
#mobile
75% - of people 15-35 yearsgo online via smartphone moreoften than computer.
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came
The smartphone is the in-store assistant
#mobile
71% of in-store shoppers saytheir device has become moreimportant to their in-store experience.
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Mobile
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Agenda
What is really going on in Retail
Data Explosion
Challenges
Retail Use Case
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#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
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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
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Agenda
What is really going on in Retail
Data Explosion
Challenges
Retail Use Case
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• Collect
• Store
• Protect
• Understand
• Analyze, compare and decide
• All in near real-time
Challenges
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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
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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
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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
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Hortonworks Capabilities
The Data Flow Thing
Processand
AnalyzeCollect
Store & Integrate
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New requirements
…to Activity InsightFrom reaction
…to OptimizationFrom planning
From break then fix
A shift from Reactive… …to Proactive
…to Preventive
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Data Science
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Iterative learning process
Visualize, Explore
Hypothesize;;Model
Measure/EvaluateAcquire Data
Clean Data
Question Answer
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Tools
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Agenda
What is really going on in Retail
Data Explosion
Challenges
Retail Use Case
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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
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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.
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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
Optimize Inventories
OPEX Reduction
Mainframe Offloads
Historical Records
Dataas a Service
PublicData Capture
Fraud Prevention
Device DataIngest
Rapid Reporting
Digital Protection
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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
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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
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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
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Architecture
HDFS/HBase
Feeds
SQLStreaming
Search UISearch Index
Retail: Real Time Offer ManagementReal Time Inventory ManagementReal Time PersonalizationReal Time Stream Analytics
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Customer Journey with Open Enterprise Hadoop
• Zulily
Store DesignIndividualization
Sentiment Analysis
BasketAnalysis
Optimize Inventories
Next Product to BuyRecommendation
Historical Records
PublicData Capture
DeviceData Ingest
Customer Retention
Page 43 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Thank You!
Mats Johansson
@matsjo66
https://se.linkedin.com/in/matsjo66