Only 23% of businesses can integrate customer insights in real-time. Learn how to change that. Join us to hear from industry experts on how to transform your organization’s data into the best omnichannel customer experience. Through this webinar, participants will hear how one retailer, with over 5 million customers and 750 brands, developed precise customer lifetime models using trusted data and delivered personalized promotions at scale. Through a single customer view and customer analytics, the retailer was able to quickly learn what changes needed to be made to improve the customer buying journey, and make those changes rapidly and effectively. Presenters : Dan Mitchell, Director of Global Retail and CPG Practice at SAS, Eric Thorsen, VP Retail at Hortonworks

1. Crea%ng an
Omnichannel Customer
Journey in Retail
Eric Thorsen, GM Retail/CPG, Hortonworks
Dan Mitchell, Director Global Retail and CPG, SAS

2. Copyright © SAS Ins1tute Inc. All rights reserved.
Eric Thorsen
General Manager
Retail/CPG
Hortonworks
Speakers
Dan Mitchell
Director, Global
Retail and CPG
SAS

3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Millennials vs. Baby Boomers
Millennials will be 50% of workforce by 2020, 75% of workforce by 2030
Grew up alongside technology, tend to be more optimistic and community-minded
Boomers more likely to use traditional media (newspapers vs. magazines)
Millennials more likely to be swayed by word of mouth, tends to be digital
PWC “Next Generation Global Study 2013
Millennial and Boomer purchasing trends conducted by Radius Global Market Research (Radius GMR)

4. Copyright © SAS Ins1tute Inc. All rights reserved.
Understanding the New Consumer

5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Amazon Impact
• Growth of “everything store” eroding
tradiFonal shopping paGerns, basics
and staples, and reducing standard
purchases of tradiFonal items at
tradiFonal retailers
• Anecdotal impact of “Showrooming”
where consumers will research
products in-store, and ulFmate
purchase online through Amazon
• Amazon has advanced supply chain to
deliver in less than two days in large
markets, someFmes same day, taking
E-Commerce out of reach of
tradiFonal retailers and forcing deep
discounts to remain compeFFve
Forbes ar)cle showing how Amazon can grow to $3T
company!

6. Copyright © SAS Ins1tute Inc. All rights reserved.
“We need to be more agile in
responding to our customers needs
online and in store”
CEO of Major SoQlines Retail Group
“We need to enhance our in-store
experience, offering state-of-the -
art digital tools”
Treasurer,
Apparel & Accessories Retailer
“Improving technology to grow the
linkage between the internet & the
store to enhance the customer
experience is our top goal”
CEO, Department Store Retailer
PrioriFes of the Retail CEO

7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Traditional systems under pressure
Challenges
• Constrains data to app
• Can’t manage new data
• Costly to Scale
Business Value
Clickstream
GeolocaFon
Web Data
Internet of Things
Docs, emails
Server logs
2012
2.8 ZeKabytes
2020
40 ZeKabytes
1
2 New Data
ERP CRM SCM
New
Tradi%onal
*Mul)ples of Bytes
Kilobyte
Megabyte
Gigabyte
Terabyte
Petabyte
Exabyte
ZeFabyte
YoFabyte
1,000,000,000,000,000,000,000
Much of the new data
exists in-flight between
systems and devices as
part of the Internet of
Anything

8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Hortonworks Connected Data PlaOorms and Solu%ons
Hortonworks
ConnecFon
Hortonworks Solu%ons
Enterprise Data
Warehouse OpFmizaFon
Cyber Security and
Threat Management
Internet of Things
and Streaming AnalyFcs
Hortonworks Connec%on
SubscripFon Support
SmartSense
Premier Support
EducaFonal Services
Professional Services
Community ConnecFon
Cloud
Hortonworks Data Cloud
AWS HDInsight
Data Center
Hortonworks Data Suite
HDF HDP

9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Holis%c Customer Interac%on Model
HDP and HDF
Subscrip%on
Opera%onal
Services
Applica%ons
Support/ ”Break
Fix”
Professional Services and
Partner SI’s
Configure, Manage
and Upgrade
Components
Included
Customer Proposal Components

10. 10
Transformation
--- Maturity Stages à
OptimizationExplorationAwareness
Marketing
Merchandising
IT Ops
---MaturityStagesà
Peer Compe%%ve Scale
Standard among peer group
Common among peer group
Strategic among peer group
New InnovaFons
Digital
Store Operations
No Use Case Name
1a Single View of Customer
1b Single View of Customer
2 Basket Analysis
3 Social Listening
4 Enriched Basket Analysis
5 Clickstream Analysis
6 RecommendaFon Engine
7 Price OpFmizaFon
8
Beacon/Sensor Monitoring
and Ingest
9 Store CommunicaFons
10 Email Management
11 EDW Enhancement
12 Inventory OpFmizaFon
13 Path to Purchase
14 Supply Chain Telemetry
15 Customer Service Analysis
16 PreventaFve Maintenance
17 Machine Learning / AI
4
Purchasing &
Logistics
10
11
13
1a
1b
12
Use Cases are available at different levels of maturity
15
14
16 17
8
8
7
3
5
2 9

11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Retail Use Case: Major Big Box Retailer
Web Logging
• Track behavior of web
visitors. Analyze logs
Single View of
Customer
• Online
• In-store
• Project Desk
• Commercial Counter
RecommendaFon
Engine
• Knowing details of consumer
deliver relevant promoFons
Price
OpFmizaFon
• Scan compeFtor
pricing on-line
• Suggest new price
based on margin
point and
awareness of
compeFFve pricing
Financial
ReporFng
• Calculate COGS
and insert into
ERP system
• Replace legacy BI
Report that would
never complete
A True Hadoop “Journey”
• Five Use Cases since original project
• 18-month lifecycle with HDP Hadoop
• In-house talent
• Significant annual savings due to
lower-cost storage and compute
Customers typically “Start Small, Think Big”

12. 12
To Legacy
Retail Conceptual Architecture
API Layer
Algorithms, ReporFng
and AnalyFcs Access
Hortonworks Data Plaform (HDP)
Banners, Geos, Stores,
Sales & Inventory
Syndicated Data, Unstructured Data,
Safety, Quality, Warrantee
Data Ingest
Apps
Mobile
Digital
Self-Service
Affinity
RFM
Tops and Flops
Supply Chain
Loyalty
Customer Profiles
Cleansing
Staging
Storage
Syndicated Data
Private Label Mfg
Feeds to Legacy:
• Customer DNA
• CRM Systems
• ERP Systems
• MarkeFng AutomaFon
• Digital
• Brand AnalyFcs
• Loyalty

13. Copyright © SAS Ins1tute Inc. All rights reserved.
Omnichannel Analy1cs

14. Copyright © SAS Ins1tute Inc. All rights reserved.

15. Copyright © SAS Ins1tute Inc. All rights reserved.
SAS® Omnichannel Analy1cs Powers Retail
…at every step of the customer journey

16. Copyright © SAS Ins1tute Inc. All rights reserved.
What makes up context?
Customer Rela1onship
Context: Previous purchases
Real-Time Context:
In-Store & On-line
Personal Context:
Current Shopping Purpose
Relevance
16

17. Copyright © SAS Ins1tute Inc. All rights reserved.
The Big Picture
Whether in-store or
on-line, Customers
ini1ate events that
trigger the need for a
response
Sense
01
Their full profile and
history, apply contextual
analy1cs to iden1fy the
best ac1on
Understand
02
Engage in a 1mely,
convenient and
consistent way.
Real-1me or right-1me
Act
03

18. Copyright © SAS Ins1tute Inc. All rights reserved.
Context: Defined by Customer Profile
Who
They
Are
What
They
Buy
How
They
Interact
What
They
Feel &
Say
Where
They Are
Now
Who
They
Know
Their
Value &
Poten1al
Their
Loyalty

19. Copyright © SAS Ins1tute Inc. All rights reserved.
Core Capabili1es Required
Transac1ons
Channel Interac1ons
Opinions/Sen1ments
Listen/Filter/Trigger
Sense
01
First Party, Third Party
Online, Offline
Structured, Unstructured
Match, Merge, Purge
Analy1cs:
Descrip1ve
Diagnos1c
Predic1ve
Understand
02
Outbound
Inbound/Real-1me
Orchestrated Omni-
Channel
Act
03

20. Copyright © SAS Ins1tute Inc. All rights reserved.
Real-Fme CX + Omnichannel AnalyFcs + Customer Journey OpFmizaFon

21. Copyright © SAS Ins1tute Inc. All rights reserved.
Data and AnalyFcs
Aligned with customer journey and business objecFves
CUSTOMER JOURNEY STAGE
ANALYTICS
KEY BUSINESS OBJECTIVE
Discover
Profile customers
Segmenta1on
Evaluate prospects
Lead scoring
Reach right prospects
Acquisi1on models
Explore
Analyze customer response
Offer/ contact op1miza1on
Op1mize marke1ng mix
Marke1ng mix modeling
Test marke1ng
A/B, mul1variate tes1ng
Buy
Predict future behavior
Propensity models
Target accurately
Segmenta1on, valua1on models
Personalize marke1ng
Next best ac1on models, social/ text VOC analysis
Engage
Expand breadth of customer interac1ons
Cross-sell/ upsell
Increase depth of customer interac1ons
Loyalty models
Incorporate customer feedback
Value of customer analysis
Manage customer airi1on/ defec1on
Churn/ airi1on models
Maximize customer value
Life1me value models

22. 22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Hadoop for Retail
DATA REPOSITORIES
ANALYSIS
Single view of consumer
Targeted promoFons
RecommendaFon engines
Basket analysis
Price opFmizaFon
Inventory opFmizaFon
Loyalty management
Path to purchase
Security
Opera%ons
Governance
& Integra%on
° 1 ° ° ° ° ° ° °
° ° ° ° ° ° ° ° °
° ° ° ° ° ° ° ° N
YARN : Data Opera%ng System
Script
SQL
NoSQL
Stream
Search
Others
HDFS
(Hadoop Distributed File System)
In-Mem
ERP
EDW
RDBMS
CRM
EMERGING & NON-TRADITIONAL SOURCES
SOCIAL MEDIA
BEACONS
SENSOR RFID
CLICKSTREAM
IN-STORE
WIFI LOGS
SERVER LOGS
TRADITIONAL SOURCES
CRM STORES PRODUCT CATALOG STAFFING PLANS
ERP POS TRANSACTIONS INVENTORY WEB TRANSACTIONS

23. 23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Thank you!

24. 24 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Q&A
Addi%onal Retail Informa%on:
Hortonworks.com -> Solu%ons (by industry) -> Retail
SAS.com -> Industries -> Retail