New rules reshape retail and customer relationships

New rules…
..Retailers’ New Games

Customer Relationships are
Multi – Dimensional and Non Linear
View
Product
banner
ad
Search
product
details
online
Ask friends
on Face
Book for
opinion
View product
reviews by
experts on
You Tube
Read
blog on
product
Search
for
product
SHOP ON
WEBSITE /
MOBILE /
STORE
Track
order
status on
mobile
Like You
on Face
Book
Download
the mobile
app
Share
posts on
your Face
Book page
Visit Store
for product
demo
Compare
prices with
competitors
Write
reviews
about
product
Follow
the
Twitter
page
….56 touch points between moment of inspiration and moment of transaction

What we are hearing from our Customers…

Anatomy of Digital Transformation
User Experience
Multi-Option Fulfillment
Cross Channel Loyalty
Single View of Customer
Mobility
Multichannel Foundation
On Demand
Service (search,
inventory)
Deep
Personalization
Time to Market
Components
Customer Experience Management as
a capability to achieve seamless and
intuitive shopping experience
Cross Channel Order Orchestration to
facilitate profitable cross channel
fulfillment supported by real-time Omni-
channel analytics
Faster Time to Market through frequent
updates/ release of functionalities
enabling fresh digital experience
Foundation of Customer knowledge base
to drive deep personalization and
targeting

Creating the customer “connections”
Creating a single view
Core
Attributes
Extended
Attributes
Transactions Interactions
Customer
information
management
In store behavior
Social Media
Data
Online Behavior



 Leverage Enterprise-wide data to create 3600
customer profiles
 Deploy Personalization as a differentiator –
Moving away from black box techniques
 Business goal driven – data driven “Plan” &
“Execute”
 Orchestrate personalization across touch-points
Personalization for Customer Engagement

Customer engagement in the store
 Self Scanning
 Identification of customer
 Self Help mobile App
 Customer shopping
enablement ( Aug Reality)
 Mobile Payment
Associate Empowerment
Associate
Customer
Center of cross channel orchestrations …Process Optimization
New Processes. Old Systems
Keeping Stores relevant…
Simplify…Standardize…Synergize
 Labor Productivity (receiving, cashiering,
RDQ….)
 Inventory Optimization
 Space Productivity
 Back office process optimization

Front end of tomorrow… empowered associates
Location based services
Mobile based customer engagement
Mobile Payment
Self Check Out, Faster Check Out
Associate mobility
Employee collaboration
Real time predictive alerts
Front end of
tomorrow
Empowered
Associates
1
Store
Technology for
agility
The New POS
Cloud
(Near) Real Time integration
Cost/ Process
Optimization
Energy Usage
Backroom Efficiency
Macro Space Optimization

…integrating customer into the merchandising process
..NOW ..NEXT
Lists, Basket, Trips
Consumer decision
trees
Demand
Substitution
Event, Sentiments
Weather Demand Based
Forecasting
Sentiment based
forecasting
Velocity based space and
inventory
Product Elasticity
Demographic based
assortment
Substitution, CDT based
space and Inventory
Basket Elasticity
Trips based assortment

New rules…
..Retailers’ New Games
Thank You

Legacy Rides the Elephant
Aashish Chandra
Divisional VP, Sears Holdings
GM / Head, Legacy Modernization, MetaScale

18
Legacy Rides The Elephant
Hadoop has
changed the
enterprise big
data game.
Are you
languishing in
the past or
adopting
outdated
trends?

19
The Classic Enterprise Challenge
The
Challenge
Growing
Data
Volumes
Shortened
Processing
Windows
Escalating
Costs
Hitting
Scalability
Ceilings
Demanding
Business
Rqts
ETL
Complexity
Latency in
Data
Tight IT
Budgets
Constant pressure to lower costs, deliver faster, migrate to real time and answer more diff
questions for business..
• Copy & Use  Source once and Re-use
• Linear  Parallel Processing
• Proprietary  Open Source
• Capital  Cloud Expense
• Batch  Real time
• Operating Costs  Down

21
Gbytes Tbytes 100's of Tbytes
Minutes
Hours
Days
Data Size
Seconds
Milliseconds
ComplexAnalyticalQuery
Hadoop
Database and high speed appliances
with parallel processing
Pbytes
Expensive
Inefficient
Cheaper price for
performance
Why Hadoop?

22
Minutes
Hours
Days
Data Size
Seconds
Milliseconds
Hadoop
Pbytes
Expensive
Inefficient
Cheaper price for
performance
Why Hadoop?

23
Minutes
Hours
Days
Data Size
Seconds
Milliseconds
Hadoop
Pbytes
Gets expensive and inefficient as the data size
grows. Will need investments in specialized
appliances and will not scale beyond a point
Databases are best used for fast response
needs on smaller datasets and for specific SQL
access
Expensive
Inefficient
Cheaper price for
performance
Why Hadoop?

24
Minutes
Hours
Days
Data Size
Seconds
Milliseconds
Hadoop
Pbytes
access
Expensive
Inefficient
Cheaper price for
performance
Why Hadoop?

25
Minutes
Hours
Days
Data Size
Seconds
Milliseconds
Hadoop
Pbytes
access
Hadoop is inefficient for small datasets but is designed
to handle big and complex data: Stays efficient as you
encounter Big Data and run complex workloads. Will
provide a lower price for performance
Expensive
Inefficient
Cheaper price for
performance
Why Hadoop?

26
What Hadoop is Not?
Understanding Hadoop’s limitations will help you identify the right use cases:
•Hadoop is not a high-speed SQL database
•Hadoop is not a particularly simple technology
•Hadoop is not easy to connect to legacy systems. You can do it, but the
complexity needs to be considered.
•Hadoop is not a replacement for traditional data warehouses. It is an adjunctive
product to data warehouses.
•Normal DBAs will need to learn new skills before they can adopt Hadoop tools.
•The architecture around the data - the way you store data, the way you de-
normalize data, the way you ingest data, the way you extract data - is different in
Hadoop.
•Linux and Java skills are critical for making a Hadoop environment a reality.

27
A super-powerful environment that can transform your understanding of data:
• Store vast amounts of data.
• Run queries on huge data sets.
• Transform traditional ETL
• Archive data on Hadoop and still analyze it
• Ingest data at incredible speeds and
analyze it and report on it in near real-time
• Hadoop massively reduces the latency of data
• Hadoop allows you to ask questions that were previously impossible to answer
Capabilities of Hadoop

Big Data
The Sears Holdings Journey

29
Where did we start?
• Issues with meeting production schedules
• Multiple copies of data, no single version of truth
• ETL complexity, cost of software and cost to manage
• Time taken to setup ETL data sources for projects
• Latency in data, up to weeks in some cases
• Enterprise Data Warehouses unable to handle load
• Mainframe workload over consuming capacity
• IT Budgets not growing BUT data volumes escalating

30
The Sears Holdings Approach
Implement a
Hadoop-
centric
reference
architecture
Move
enterprise
batch
processing to
Hadoop
Make
Hadoop the
single point
of truth
Massively
reduce ETL
by
transforming
within
Hadoop
Move results
and
aggregates
back to
legacy
systems for
consumption
Retain, within
Hadoop,
source files
at the finest
granularity
for re-use
1 2 3 4 5 6
Key to our Approach:
1) allowing users to continue to use familiar consumption interfaces
2) providing inherent HA
3) enabling businesses to unlock previously unusable data

31
The Journey
In 3 years, we are in a much different place..
•From Legacy (>1000 lines) to Ruby / MapReduce (400 lines)
• COBOL is cryptic, difficult to support, difficult to train  PiG is simple, short
and easy to maintain
•We tried HIVE (~400 lines, SQL-like abstraction)
• Easy to Use, easy to experiment and test with
• Poor performance, difficult to implement business logic
•We evolved to PiG with Java UDF Extensions
• Compressed, very efficient, easy to code / read (~200 lines)
• Demonstrated success in transforming mainframe developers to PiG
developers in under 2 weeks
•As we progressed, our business partners requested more and more
data from the cluster –which required developer time
• We are now using Datameer as a business-user reporting and query front-
end to the cluster

32
Re-Think..
• The way you capture data
• The way you store data
• The structure of your data
• The way you analyze data
• The costs of data storage
• The size of your data
• What you can analyze
• The speed of analysis
• The skills of your team

33
Mainframe Migration
Batch Processing - JOB FLOW
JCL1 - APPLICATION 1
Mainframe Batch Processing Flow
User Interface Data Sources
Batch
Processing
External
Systems/
Datawarehouse
Input
Resultant Data Resultant Data
SORT Input SPLIT
Input
SORT
Input COBOL
Input FILTER
Input FORMAT
LOAD TO DATABASE
COPY Input COBOL Input FORMAT
Input
Input

34
Mainframe Migration
Mainframe Batch Processing Flow
Batch
Processing
External
Systems/
Datawarehouse
Input
Resultant Data Resultant Data
SORT Input SPLIT
Input
SORT
Input COBOL
Input FILTER
Input FORMAT
LOAD TO DATABASE
Input
Input
Commodity Hardware Based Software Framework
Batch Process - APPLICATION 1
Batch Processing - JOB FLOW - Legacy Platform
Invention - Migration methodology for Legacy Applications to Commodity Hardware
External
Systems/
Datawarehouse
Batch
Processing
Input Resultant Data
PIG/MR Input PIG/MR
Input
PIG/MR
Input PIG/MR
Input PIG/MR
Input PIG/MR
LOAD TO DATABASE
Input
Input
Resultant Data
Seamless migration of high MIPS processing jobs with no application alteration

35
Mainframe Migration
Mysql
Hbase
Hadoop
price
LEGACY -
TERADATA/DB2
product
SOLR
S
ales
C
ustom
er
Enterprise
Systems
JQUERY/AJAX
Quart
z
JAXB
REST API
JDBC/IBATIS
JBOSSJ2EE/JBOSS/SPRING
Batch Processing
HIVE
RUBY/MAPREDUCE
JBOSSHADOOP/PIG
DB2
Oracle
UDB
price
Teradata
product
MySQL
S
ales
C
ustom
er
Enterprise
Systems
JQUERY/AJAX
Quart
z
JAXB
REST API
JDBC/IBATIS
JBOSSJ2EE/WebSphere
Mainframe Batch Processing
VSAM
JBOSSCOBOL/JCL
MetaScale

36
Enterprise Data Hub & ETL Replacement
Experience evolved to move into ETL Replacement and architecting Enterprise
Data Hub
• A major system effort in our Marketing department was heavily reliant on
traditional ETL
• As data volumes increased the system began to have performance issues as the ETL
platform degraded
• Re-work CPU-intensive portions in Hadoop
• Now run those workloads 20-50 times faster in Hadoop
• Run-times do not grow as data volumes grow
• Enterprise Data Hub
• Source Data once, Re-use multiple times
• ETL gives way to ELTTTTTT

37
The Sears Holdings Architecture

38
Current Focus
• ETL Complexity is no longer needed – Data Hub
• Source Once, Re-Use many times
• ETL changes to ELTTTTTTTTT
• Data Latency is the thing of the past
• Analysis is routinely possible within minutes of data creation
• Long Running Workload
• Can be eliminated and executed at any time
• Run times are a fraction of the original clock time
• Batch Processing on Mainframes or other conventional Batch
• Run 10, 50, even 100 times Faster
• Intelligent Archive
• Put your archives/ large data on Hadoop and make it intelligent
• Archive with the ability to run analytics or join it with other data
• Modernize Legacy
• Mainframe MIPS Reductions has very attractive ROI
• Move Data Warehouse workload – Reduce Cost – Go Faster

39
Simple & Maintainable
Complexity
creates fog;
Simplicity
clears it.

40
Faster & Resilient
Data Analytics
driving the speed
of business..
Secured and Resilient..

41
Summary of Benefits
• Readily available resources &
commodity skills
• Access to latest technologies
• IT Operational Efficiencies
• Moved 7000 lines of COBOL
code to under 150 lines in PiG
• Ancient systems no longer
bottleneck for business
• Faster time to Market
• Mission critical “Item Master”
application in COBOL/JCL being
converted by our tool in Java
(JOBOL)
• Modernized COBOL, JCL, DB2,
VSAM, IMS & so on
• Reduced batch processing in
COBOL/JCL from over 6 hrs to
less than 10 min in PiG Latin on
Hadoop
• Simpler, and easily maintainable
code
• Massively Parallel Processing
• Significant reduction in ISV costs
& mainframe software licenses
fees
• Open Source platform
• Saved ~ $2MM annually within 13
weeks by MIPS Optimization
efforts
• Reduced 1500+ MIPS by moving
batch processing to Hadoop
Cost
Savings
Transform
I.T.
Skills &
Resources
Business
Agility

42
The Learning
• Big Data is here and ready – Avoid the hype
• An Enterprise Data Architecture model is essential
• Hadoop can revolutionize Enterprise workload
• Can reduce strain on legacy platforms
• Can reduce cost
• Can bring new business opportunities
• The Solution must be an Eco-system
• Must be part of an overall enterprise data strategy
• Not to be underestimated

43
Formoreinformation,visit:
www.metascale.com
Follow us on Twitter @LegacyModernizationMadeEasy
Join us on LinkedIn: www.linkedin.com/company/metascale-llc
Legacy Modernization Made Easy!

This presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other
authorized recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied,
distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates.
© 2013 Gartner, Inc. and/or its affiliates. All rights reserved.
Jeff Roster
Jeff.Roster@Gartner.com
Twitter: @JeffPR
Instagram: @JeffPR
Retail 2013…and Beyond

Nexus of Forces: Social, Mobile, Cloud
& Information

Nexus Impacts Differ Across Industries
Manufacturing
Government
Professional Services
Media
Retail
Manufacturing
Communications
Banking
Healthcare
Manufacturing
Wholesale Distribution
"Process
Value
Targets"
"Business
Model
Redesign"
"Business as
Usual"
"Technology
Platform
Refresh"

54
Major Market Opportunity for Software
45.3%
41.5%
38.7%
36.8%
32.1%
29.2%
23.6%
17.0%
12.3%
5.7%
2.8%
Retiring legacy systems
Developing applications to satisfy
empowered consumers
Application integration
Managing big data
Optimizing stores as a major channel
Consumer smart devices in the
enterprise
Upgrading store-level bandwidth and
infrastructure
PCI compliance
Mobile security
Fighting intrusions and Web attacks
Fighting against inflation
Over the next 3 years what “pains’ will you devote significant resources to solving?

55
Mobile POS Breaks into the Mainstream
50.0%
43.4%
42.5%
42.5%
41.5%
40.6%
40.6%
39.6%
39.6%
36.8%
Campaign analysis and forecasting
Forecasting and planning
Mobile POS
Predictive analytics
In-store pickup or returns of web goods
Multi-channel planning and forecasting
Campaign management
Allocation
Assortment planning
POS peripherals
Top 10 Technologies for 2013

56
IT Spend Continues to Climb
IT Budgets as a Percent
of Total Revenue
Change in Year-Over-Year
IT Budget
11.3%
30.2%
10.4%
6.6%
1.9%
7.5%
Less than 1%
1% to< 2%
2% to < 3%
3% to < 4%
4% to l< 5%
5% or more
4.7%
3.8%
4.7%
28.3%
26.4%
16.0%
16.0%
Decrease 10% or
more
Decrease between
5% to < 10%
Decrease between
1% to < 5%
No change
Increase between
1% to < 5%
Increase between
5% to < 10%
Increase 10% or
more

57
Has SaaS Finally Hit the Mainstream
56.6%
50.9%
38.7%
31.1%
35.8%
We seek best of breed software
We seek integrated solutions suites
We seek software-as-service models
We use in-house IT resources to
develop software
We use third party services to help
develop software
Your general point of view in how you want to acquire software going forward.

58
Status of Organization's Customer Facing
Current Mobile Channel Development?
17.9%
44.3%
28.3%
9.4%
Not planning any activity
Planning under way
Pilots in progress
Fully functioning mobile commerce
strategy in place

59
An Industry in Full Transformation Mode
13.2%
32.1%
35.8%
18.9%
Basic IT infrastructure and systems with
critical limitations
Mostly basic IT infrastructure and systems
but some advanced upgrades
Mostly advanced IT infrastructure and
systems but lack of comprehensive
integration
Advanced IT infrastructure and systems
with deep integration
14.2%
7.5%
28.3%
15.1%
7.5%
27.4%
We don't have an e-commerce platform
Platform needs up dating, but no plan to
upgrade
Currently upgrading platform now
Plan to upgrade within 12 months
Plan to upgrade within 24 months
Re-platformed within 2 years, no need to
upgrade
Maturity of IT Architecture
Status of E-Commerce Platform

60
Has POS Hardware Commoditized?
39%
39%
6%
47%
8%
15%
4%
19%
17%
18%
14%
15%
6%
24%
6%
10%
20%
18%
28%
15%
14%
18%
5%
17%
11%
14%
18%
8%
5%
13%
14%
19%
POS peripherals
POS software
Mobile POS
POS terminals
(traditional, fixed)
Self checkout terminals
In-store pickup or returns
of web goods
Item level RFID
Returns management
Up-to-date tech in place Started but not finished major tech upgrade
Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months
Status of In Store Technology

61
Mobile POS Looks to be Additive
Yes, already
investing
29.2%
Yes,
planning to
invest during
2013
22.6%
No
35.8%
Don't know
12.3% Yes, plan to
decrease
fixed POS
25.5%
No plans to
decrease
fixed POS
63.6%
Don't know
10.9%
Does your organization plan to invest in mobile
point of sale (POS) in 2013
Does your organization plan to decrease the
number of fixed POS devices in stores during
2013 as a result of its mobile POS
investments?

62
Digital Signage on the Rise
29%
25%
19%
19%
17%
10%
5%
3%
21%
14%
8%
16%
11%
13%
11%
6%
12%
8%
10%
11%
12%
9%
7%
3%
19%
16%
13%
19%
21%
14%
20%
14%
Frequent shopper or
loyalty program
Store level Loss prevention
Kiosks
Shopper tracking capability
Store level task management
Digital signage displays
NFC (Near Field
Communication) payments
Electronic shelf labels
Status of In Store Technology

63
Multichannel Key Even in Supply Chain
32%
24%
24%
22%
20%
16%
8%
2%
16%
13%
14%
13%
14%
16%
12%
7%
11%
8%
8%
10%
19%
18%
17%
8%
8%
18%
13%
11%
17%
22%
10%
9%
Warehouse management
systems
Distributed order management
systems
Transportation management
systems
Sourcing
Real time inventory visibility
(SCIV)
Multichannel fulfillment
Trade promotion management
Radio frequency identification
(RFID) Case/Pallet
Status of Supply Chain Technology

64
Optimization Finds its Ways to retail
37%
25%
22%
14%
13%
14%
15%
18%
14%
11%
16%
11%
Time and attendance
Labor scheduling and optimization
Task management
27%
25%
23%
20%
5%
10%
13%
11%
13%
13%
21%
15%
18%
13%
15%
11%
14%
12%
12%
24%
Human Resources and benefits
Education and training
Recruitment and on boarding
Recruiting via social media (e.g.
Facebook, LinkedIn)
Mobile-enabled workforce and/or
HR applications
Status of Workforce Mgmt. Technology
Status of Human Resources Technology

65
Merchandise Technology
33%
25%
24%
24%
21%
21%
19%
19%
17%
15%
12%
9%
8%
21%
18%
25%
13%
21%
17%
23%
15%
14%
25%
19%
21%
16%
11%
17%
19%
14%
19%
15%
17%
16%
15%
25%
14%
20%
25%
10%
8%
13%
8%
7%
11%
12%
15%
13%
16%
8%
12%
22%
Replenishment
Item management
Forecasting and planning
New product or private label development
Allocation
Category management
Assortment planning
Price and markdown optimization
Product lifecycle management
Campaign analysis and forecasting
Shelf and space planning
Campaign management
Multi-channel planning and forecasting
Status of Merchandise Technology

66
BI/Analytics Continues to Show Strength
25%
21%
19%
10%
9%
21%
22%
16%
18%
18%
16%
15%
16%
25%
17%
13%
20%
17%
21%
26%
Market basket
analysis
Shopper Tracking
Margin optimization
Predictive analytics
Social media
analytics
Status of BI/Analytics solutions

Recommendations
Be assured the rate of innovation in retail will
accelerate dramatically over the next 3 years
- Meaning your competitors are transforming their
businesses….what about you?
Understand your organizations (really senior
Mgt.s) readiness to absorb new technologies.
You are the change agent. Embrace the job!

Thank You
Jeff.roster@gartner.com
68
*

New rules reshape retail and customer relationships

New rules reshape retail and customer relationships

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