The digital transformation of retail

1© Cloudera, Inc. All rights reserved.
The Digital Transformation of Retail
Frank Vullers
Business Value Strategist Cloudera

eCommerce threat or opportunity ?
China 19 %
Japan6,7 %
E-commerce share of total retail sales from 2015 to 20211
1 Statistica 2017
2 https://www.theatlantic.com/business/archive/2017/04/retail-meltdown-of-2017/522384
3 Forrester online retail forecast 2017-2022
4 Staticitca Digital economy compass 2017

China 19 %
Japan6,7 %
Share of online sales (grocery- non grocery)3
1 Statistica 2017

China 19 %
Japan6,7 %
Traditional retail (channels) are shrinking2
1 Statistica 2017

China 19 %
Japan6,7 %
Traditional retail (channels) are shrinking2
Rise of the platforms4
1 Statistica 2017

The data-driven enterprise
IoT explosion of new data
30B
connected
devices
440x
more data
Enterprises re-architect to
modernize IT infrastructure
open source
cloud
machine
learning
$200B
total
market1
1 IDC Worldwide Big Data and Business Analytics Market Through 2020

Three Factors Entrenching Big Data in Retail
1.TheSensorEconomy:PersonalizingProducts,Experiences,andOffersinRealTime
What Technologies will Brick-and-Mortar
Retailers Use In-Store ?
Brickstream and MIT Technology Review say…
In the U.S., an estimated
30% OF POTENTIAL SALES
are lost in physical store
locations because prices are
not context- and customer-
relevant enough and fail to
INCORPORATE DATA
about specific shoppers.
Sources Brickstream Corporation. Retail Analytics: What’s in Store?. 20 May 2014.
Banjal, Manju. “Evolving Beyond Coupons and Mobile Apps: Retail Technologies in the Sensor Economy.” MIT Technology Review. 15 July 2014.

2.ComplianceandStrategy:Innovationina SecureandGovernedEnvironment
Steep Fines
& Legal Fees
Greater
Scrutiny
Brand
Damage
Suspension or
Termination
Average Data Breach Costs $201
Per Compromised Account
Resulting Customer Churn Averages $5
Million in Annual Losses
Cyber Crime Expenses Average
$13 Million Annual Per Company
Sources: 2014 Cost of Data Breach Study: Global Analysis. Ponemon Institute. May 2014.
2014 Global Report on the Cost of Cyber Crime. Ponemon Institute. October 2014.
2009 Cost of Data Breach Study: Global Analysis. Ponemon Institute. May 2009.

3.InventoryandProfitability:PredictingDemandandReadingtheSignals
The Institute for Supply Management, DemandPlanning.net, and IDC say…
CPG manufacturers estimate
39% FORECAST ERROR
on total production and
inventory, contributing to a
$371 BILLION
OPPORTUNITY
related to better
use of big data
Sources: Cahill, Kristina, and Goupil, Rose Marie. “Manufacturing ISM Report on Business.” Institute for Supply Management. September 2013 to September 2014.
Asardohkar, Rohan. “Forecast Error Benchmarking Across Various Industries – Survey Results.” ForecastingBlog.com. 22 August 2012.
Vessert, Dan, et al. Capturing the $1.6 Trillion Data Dividend. International Data Corporation. May 2014.

79% of consumers listed ‘instant
ownership’ as the most appealing
attribute of any retailer, online or off

Future of Retail is in the data…
Virtual FittingThrough the App
2D ShoppingConsumer Analytics
Mobile Wallets
NFC Offers

Customer decision journey*
* Source: McKinsey & Company https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-consumer-decision-journey
Traditional Funnel Circular loop

The new pathway is
much more
fragmented and
dynamic … These
varied shopping
channels open up
thousands of
potential new
purchasing pathways,
more than five times
the number available
in the brick-and-
mortar world.
Source: GMA/IRI online grocery survey, April 2014

Customer 360 – An Industry Perspective
- What is Customer 360?
A holistic real-time view of your
individual customers
Across all products, systems, devices
and interaction channels
In order to deliver a consistent,
personalized, context specific and
relevant experience

Customer experience
expectations are converging
on the brand, not channel
 Consistent across all channels and
lines of business
 Contextualized to present location
and circumstances
 Personalized to reflect preferences
and aspirations
 Relevant in the moment to their
needs and expectations

The first axis: the data
Properties: batch, stream, real-time
Digital/Mobile
Digital Media
• Teradata Aprimo
• IBM Unica
• Oracle Eloqua
• X+1
Web Logs
• Microsoft IIS
• Apache
• nginx
• Google GWS
Clickstream/UX
• Adobe Omniture
• IBM Coremetrics
• IBM Tealeaf
• Google Analytics
Premium
• Webtrends
Mobile Application
SMS
Transaction CRM/Call Center Demographics Loyalty/Retention Social
Retail
Mobile
Web
Channel
Distributor
Bot
Call Center
Indirect
Kiosk
Embedded Commerce
Service
Billing
Customer Lifecycle
• Acquisition
• Churn
• Cross-Sell
• Upsell
CRM
• MS Dynamics
• Oracle/Siebel
• Salesforce
• SAP
Online Chat
• Oracle RightNow
• Moxie Live Chat
• LivePerson
• Instant Service
• Oracle Live Help
• BoldChat
• Zendesk Zopim
• Kana Live Chat
IVR
• Avaya
• Cisco
• Nortel
• Nuance
Data Broker / Syndicate
• Acxiom
• CoreLogic
• Datalogix
• eBureau
• ID Analytics
• Intelius
• PeekYou
• Rapleaf
• Recorded Future
• IHS Polk
• Nielsen
• InfoScout
• Symphony IRI
• Gfk
Behavior
Loyalty
• Aimia
• Brierley+Partners
• Comarch
• Epsilon
• Kobie
• ICF Olson 1to1
• Merkle
• Clutch
• CrowdTwist
• DataCandy
• Deluxe
• Inte Q
• ICLP
Survey
• ABA
• Medallia
• Forsee
• Allegiance
• Walker Information
Direct
• Twitter
• Facebook
• Bazaarvoice
Listening/Management
• Sprinklr
• Crimson Hexagon
• Radian6
• Lithium
• Simply Measured
• Curalate
• Datasift
Voice of the Community
• CSAT
• NPS
Data available
Analytical methods
Serving Results

The 2nd axis: analytical processing
Unsupervised learning: clustering, topic
modeling, time series analysis
Classification: gradient boosted trees,
SVMs, logistic regression, etc
Deep learning ("neural nets") and
natural language processing
Profile
Customer
→ Detect anomalous events
(e.g.; predictive inventory)
→ Score entities by behavior
(e.g.; churn analytics)
→ Classify or cluster
unstructured data (e.g.;
images or text for fraud
threats)
Data available
Analytical methods
Serving Results

The 3rd axis: serving actionable insights
Integration with web applications
via Spark or HBase
Integration with mobile apps via
Spark or HBase
Integration with enterprise
applications, e.g.; CRM, sales
Search applications via solr
Serving to standard BI tools (e.g.;
Tableau, Qlik)
Data available
Analytical methods
Serving Results

Customer 360 Levels of Insight
Core Customer Profile
Interactions Transactions

Common Customer Profile Delivers Consistency
• The master consumer record provides the
current, accurate and complete consumer data
to all systems and channels
• Enables deep consumer understanding
• Relationships and Roles
• Complete product and service portfolio
• Household
• Life Events
• Consumer Value Tier
• Aligns consumer data between the different
applications while maintaining specific views to
different business units
• Unique identification and management of
Consumers (de-duplication)
{Name}
{Address}
{Preferences}
{Transaction History}
{Interaction History}
{Relationships}
{Segmentation}
{Demographics / Psychographics}
{Life Events}
{Identifiers}
Extensible
“Containers”
for Customer
Profile
{Name}
{Address}
{Preferences}
{Transaction History}
Iteration 1
Iteration 2
{Interaction History}
{Relationships}

Transactions and Interactions
• A Person/Entity identifier is a data element that recognizes the
person navigating within that interaction channel.
• A session identifier is the unique value assigned to the customer
journey. Could be a native unique identifier in the interaction
channel or a combination of fields to create a unique identifier
• Date and time stamps are important elements to tie the sequence
of interactions within and across interaction channels.
• Events are the transitions the customer experiences during their
journey.
• Attributes are information carried along with the behavioral data
content that adds relevance
{Person/Entity}
{Session}
{DateTime}
{Event}
{Attribute}

Some customers in the retail space
Omnichannel Retailers Pure play Digital Marketing / Media
Leading
American
Retailer

• Many different data sources integrated
(click streams, in-store POS, online
ordering, and social media)
• Understanding of abandoned online
shopping cart behavior
• Optimized operational investments by
attributing revenue to the appropriate
channel
• Increased customer insight informs
supply chain plans
• Improved ability to explain and predict
returns
CUSTOMER 360
RETAIL / ONLINE
» CUSTOMER 360°
» PROCESS IMPROVEMENT
» PREDICTIVE ANALYTICS
360° View of Retail Customers / Behavior

Enabling retailers better understand in-store
shopper behavior in real time
Challenge:
• Track each consumer’s path and journey in
the store with high accuracy
• Managing data from retail IoT sensors
Solution/ Impacts:
• Footfall Analytics: Real-time analysis into
how shoppers are browsing in stores
• Helps retailers optimize product placement
and staff management
• Increased sales by nine percent in a major
category for one retailer
RETAIL IoT
» FOOTFALL ANALYTICS
» REAL TIME INSIGHTS
» CUSTOMER/ PRODUCT ANALYTICS
IoT Enabled Retail Analytics
DATA-DRIVEN
PROCESS
DATA-DRIVEN
PRODUCTS

Forrester
TEI Study
The ability to handle real-time big data or turn
massive volumes of data into instant insight and
actions is the future of retail
Challenge
• Consumers, conditioned by the services of big e-tailers
expect to see timely recommendations and promotions,
as well as simplified navigation and expedited checkout
Solution
• Providing a modern and modular architecture, Cloudera
supported BRAIN delivers near- and real-time
integration of big data as well as a full range of analytics.
Benefit
• Better and deeper relation between customer and e-
tailer, where Otto’s customer-facing system is accepted
as a friend and shopping companion
DRIVE CUSTOMER
INSIGHTS
eCommerce
» CUSTOMER 360

Influence Abandonment
(Last minute Defense LMD)
Bounce Inspiration Basket CheckOut Order
Visit
Type
Going To Buy
Conversion
RESULTGUESS
Conversion
NOT Going To Buy
Convert!
50-70%
Abandon Basket
(Industry Benchmark)
2-9%
RecoveryRate
(Industry Benchmark)
80-90%
GUESS Quality

Source: Internet Retailer
https://www.internetretailer.com/2016/06/23/chinese-web-retailer-jdcom-books-100-million-orders-1-day

JD.com use case
• 2nd largest online retailer in China
• Real-time ingestion via Kafka
• Click logs
• Application/Browser tracing
• ~70 columns per row
• 6/18 sale day
• 15M transactions
• 10M inserts/sec peak
• 200 node cluster
• Query via JDBC -> Impala -> Kudu
Browser tracing Web logs
Kafka
Kudu
Impala
JDBC access
Marketing Dept.
Developers
Web-app

DATA-DRIVEN
PRODUCTS
HiPer (High Performance Computing
Platform) processes over 200,000 files and
12.5 million unstructured documents
monthly
• Significantly enhancing productivity with
9-10x improvement in delivery speed
• Procure-to-pay audit services across order,
invoice, shipment, and sales using
machine learning and search frameworks
• Ability to re-run and experiment on large
volumes of data
• 25% decrease in storage costs
RETAIL / MANUFACTURING
» DATA DRIVEN PRODUCTS
» MACHINE LEARNING
» IMPROVED SERVICE

Increasing Sales by 230%
• 61% increase in basket value and 230%
increase in overall purchases for targeted
shoppers
• 3X increase in demand from brands for
targeted offers due to increases in sales
• Optimized coupon planning by predicting
how many people will redeem a coupon
CUSTOMER 360
DIGITAL MARKETING
» PERSONALIZATION
» BEHAVIORAL TARGETING
» PREDICTIVE ANALYTICS

• Costco understands granular product
movements for smarter and faster
market research and procurement
decisions.
• Self-service analytics powering
merchandising queries, improving
performance over existing data
warehouse by up to 72x
• Able to retain full history of TLOG data
over without disrupting the existing
heterogeneous environment
Costco modernizes architecture at lower TCO
RETAIL / ONLINE
» ADVANCED ANALYTICS
» PROCESS IMPROVEMENT
» DATA WAREHOUSE OFFLOADCUSTOMER 360

DRIVE CUSTOMER INSIGHTS
CONNECT PRODUCT &
SERVICES EFFICIENCY (IoT) LOWER BUSINESS RISKS
Data is Transforming Business
MODERNIZE ARCHITECTURE

Library of retail use cases
Path to Purchase
360 Degree view
Shopping mission
Abandon baskets
Sales & Marketing
Connect product & servicesDrive Customer Insight Lower Business Risks
Product/Service Improvement
Logistics
Modernize architecture
Risk / Fraud
Commercial Risks
Storage Architecture
Storage Costs
Active Archive
ETL Off loadScalable Architecture
Infrastructure Simplification
EDW Optimization
Advanced / real time Analytics
Path to churn
Social Segmentation
Share of Wallet
Customer Satisfaction
Sentiment Analysis
Social InsightsSocial media signals
Next Best Offer
RealTime Personalisation
Event Triggered Offers
Location marketing
Channel behaviour Spend Attribution
Search term analysis
Digital experience
Channel optimization
Customer Path
Search recommend.
Cross Selling Product Affinity
First in basket
Cannibalization impacts
Price Optimization
Promo item selection
Cross Promo Affinity
Store space plans
Competitor impact Store Performance
HR survey Predictive Maintenance
Fraud prevention Cyber treat
Social impact
Measuring Multivariate Usability root cause
Service Efficiency
Network Optimization Demand Forecast
Optimize Markdown optimize Availability
Waste reduction
Optimize Store order Promotional forecasting
AR Risk Product/Food Safety
IoT/RFID
Assortment Optimization Performance Transparency
Inventory & demand visibilityStore & Pickup fulfillment
Optimize spend on paper / Catalog
Splunk Optimization

Cloudera retail customers: strategic initiatives
Customer Intimacy Forecast / Planning Supply Chain Agility
• Big data journey is defined by selected data/ analytical methods/ applications served
• Maturity in data management, analytical methods and application integration will drive value
• Approach includes Cloudera,3rd party apps and knowledge development via data science
1 32

Retail Strategic Initiative 1: Customer intimacy
What drives customer intimacy?
• POS / TLOG
• Memberships /
Loyalty Programs
• Social / Sentiment
• Clickstreams
• Promotions / Trade
• Campaigns / SEO /
Affiliate
Next Best Offer
Omni-Channel
Churn Modeling
Clickstream
In-store Pathing
Social Sentiment
Combine operational and behavioral data
to gain a full profile of the customer and
evolve customer engagement from
aggregations to "a segment of one"
1

Retail Strategic Initiative 2: Forecasting / planning
What drives the forecast & plan?
• POS / TLOG
• E-commerce
• In-Store Ordering
• Sensors
• Clickstreams
• Schematic / Displays
• Store Layout
• Orders / Receipts
• Staffing / Scheduling
• Social Sentiment
Assortment
Optimization
Pricing
Optimization
Performance
Transparency
Labor
Optimization
Use the increasing granularity of data on
pricing and sales with the historical and
real time information of consumer
consumption and engagement
2

Retail Strategic Initiative 3: Supply chain agility
What drives the supply chain?
• POS / TLOG
• E-commerce
• In-Store Ordering
• Sensors
• Clickstreams
• Weather Data
• Social Sentiment
• Gas Prices
• Orders / Receipts
• Staffing / Scheduling
Inventory &
Demand Visibility
On-Shelf Availability
Store Pick-Up &
Fulfillment
Informed Supplier
Negotiations
Gain end-to-end visibility and a mix of
operational and historical data to improve
insight for decision support and fulfillment
systems and models
3

Trends for 2017*
Retailers who promote product quality,
transparency, & sustainability will flourish
Stores providing unique in-store experiences will
thrive
Retailers across the board will adopt mobile
payment solutions
Smaller stores are in; larger stores are out
Personalization will become increasingly
important to consumers
Same-day shipping will become more prominent
1
2
3
4
5
6
Retailers will continue to invest in omnichannel
Retailtainment will pervade the industry
Data will continue to be a significant component
of retail succes
Specialty stores will be more productive than
department stores
Retailers will turn to apps, services, and third
parties to fulfill the needs of modern shoppers
Retail and technology will become even more
inseparable
7
8
9
10
11
12
*https://www.vendhq.com/university/retail-trends-and-predictions-2017

Trends for 2017*: Digital transformation areas
Retailers who promote product quality,
transparency, & sustainability will flourish
Stores providing unique in-store experiences will
thrive
Retailers across the board will adopt mobile
payment solutions
Smaller stores are in; larger stores are out
Personalization will become increasingly
important to consumers
Same-day shipping will become more prominent
1
2
3
4
5
6
Retailers will continue to invest in omnichannel
Retailtainment will pervade the industry
Data will continue to be a significant component
of retail succes
Specialty stores will be more productive than
department stores
Retailers will turn to apps, services, and third
parties to fulfill the needs of modern shoppers
Retail and technology will become even more
inseparable
7
8
9
10
11
12
*https://www.vendhq.com/university/retail-trends-and-predictions-2017
Digital transformation

Thank you
Frank Vullers
Business Value Strategist Cloudera
fvullers@cloudera.com
@FrankVullers

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