Marilyn Craig

Report
Retail: Lessons Learned
from the Original DataDriven Business and
Future Directions
Presenters:
Marilyn Craig, Senior Director, WW Sales &
Marketing Planning and Analysis, Logitech
Terence Craig, CEO/CTO, PatternBuilders
Before We Dive In… A Legal Disclaimer
 The views and opinions expressed by Marilyn
Craig in this presentation are hers and do not
necessarily reflect the opinion or any
endorsement from her employer, Logitech.
 PatternBuilders is stuck with Terence’s opinion,
whether they like it or not.
 Examples of analysis performed within this
presentation are only examples. No actual data
was harmed in making this presentation.
Retail—The First Industry to Surf the Big Data Tsunami
Before Big Data was really big, retail data was the “big” measurement standard.
When you factor out
science, government, and
social media, it still is.
t
Why was Retail the First to Catch the Big Data Wave?
 It’s all about the margins—every penny counts
 It’s all about the competition—more market share, more
customers, more sales
 It’s all about efficiencies—bottom line improvements
And it’s all about the data—multiple
systems, suppliers, channels, etc.
More “information” captured and
stored than ever before.
Retail is Not Just a Big Data
Surfer, But a Technology Driver
As Technology Evolved, Retail has Adapted and Demanded
Paper
Inventory
Records
Mainframe
EDI Networks
EDI Over the
Internet
Multinational
Collaborative
Forecasting
Real-time
Inventory
DecisionMaking
What We Now Consider Mainstream, has Retail Roots
RFID
Environmental
Sensors
VPNs
Real-Time
Logistics
In-Transit
Tracking
Supply
Chain
Management
What Keeps Retail Operating on the Technology Edge?
It’s about the 4 P’s
creating all that data and
all that data driving
decisions about the 4 P’s.
Price
Product
Place
Promotion
About All That Data…
Channel,
Reseller,
Retailer, DC,
Store, Online
Now, Consider this:
Advertising,
promotion lift
library, web-tostore, POP
Price, margin,
elasticity
Brand,
Product, SKU,
Serial Number,
RFID
Sources
of Retail
Data
CRM, Loyalty,
personalized
coupons
Channel/Trade
programs,
discounts,
rebates
Sell-in, Sellthru (and
again), Sell-out
655
3
750
50
52
4
7
10
8 years
regions
types
categories
products
weeks
Retailers
states
Stores
Billion+
ofto
of
data
historical
per
per
segregate
data
to
data
to
aggregate
per
monitor
points
data
theinvolved
(POS,
the
datadata with
for
retailer
store
year
managing
Inventory,
to
monitor
comparison
for
toto
monitor
trend
monitor
Marketing,
theanalysis
retail sales
Syndicated,
channel
etc)
10 x 750 x 50 x 52 x 3 x 7 x 4 =
x 50 =
x8=
10
1,638,000,000
81,900,000,000
655,200,000,000
xdata
750points
= 50
x
7500
=data
x
52
data
data
xpoints
=
3points
=
points
x 7 = 409,500,000
58,500,000
data
375,000
19,500,000
points
data
data
points
points
Retail’s Gold Standard—No One Does It Better (Yet)
 Largest retail company in the world:
Fortune 1 out of 500
 Largest sales data warehouse:
RetailLink, a $4 billion project (1991)
 Largest “civilian” data warehouse in the world:
2004: 460 terabytes, Internet half as large
 Defines data science:
What do hurricanes, strawberry Pop-Tarts, and
beer have in common?
But Nothing Remains the Same…
 Data continues to grow at an
exponential rate.
 New issues have come into play.
- Track and trace
- Cold chain management
- Stricter regulations
Where do we go from here?
The Future: Look Out!
Cheap, big analytics is going
to change the world.
It’s a Brave New World…
The old rule: new shelf spaces = more sales
The new rule: it’s all about analytic-driven efficiencies
The slow down in new storefronts means
growth (and profitability) will come from
efficiencies.
There’s More Data From the Store…
Smart
phones
(as payment
devices)
More
Ecommerce =
More Data
RFID or
something
similar
(finally)
Retail
Store
Data
Brand
protection,
expiration
management
Broader and
deeper
loyalty data
Traditional retail data
is moving towards
real-time.
There’s More Data from the Supply Chain…
Both are driving
standardization to an
amazing level.
Are analyzed constantly for
savings and regulatory
compliance.
Real time
monitoring of
shipment
Retailer
consolidation
and WalMart
hegemony
Humidity, Vibration,
Temperature,
Real-time
demandbased
delivery
Supply
Chain
Data
Routes and
packaging
Track and
trace
Increased
data sharing
(3PLs,
suppliers,
retailers)
Ever shortening lead times,
niche targeting, and
regulation drive this.
Retailing and supplying is
a team sport.
What’s Coming: Big Data = Big Analytics
Welcome to
The Minority
Report

Path analysis on the store floor.

More aggressive and more complex A/B tests… and lots
and lots of A/B tests.

Deep and constantly updated multivariate analysis
including personal and social media profiles, geo-location
and demographic

All of this makes real-time, targeted ads, discounts, and
offers delivered on the device of choice at the right place
a very profitable reality.
Roadblocks to Analytics “Perfection”
Legal
Cultural
• Data privacy laws.
• Multi-national retailers will encounter huge problems.
• In U.S., most valued customers are the most protective of their privacy.
• Participants don’t want to play nicely with each other.
• Retail and Supplier IT will have to get over earlier analytic failures.
• IT infrastructures will have to radically change to make the leap—the visionaries will win.
• Mega-retailers’ massive current IT investments will make change slow—but
Technology Manufactures/Suppliers will be early adopters.
And This All has an Impact on Your Infrastructure
 Sheer volume of data and its complexity is going to require new
data and analytics architectures.
 There is a need for both high performance batch (Hadoop) &
streaming/CEP (PatternBuilders, StreamInsight, etc.).
 NoSQL approaches are particularly well suited for this problem
domain.
While the public cloud is great, mega-retailer
paranoia will make adoption difficult.
The Good News: Financial Constraints are Disappearing
With the advent of:





OSS—who buys database licenses any more?
Moore’s Law
Kreiger’s Law—10 TBs costs what!
Offshoring—lot of great mathematicians out in the world.
Crowdsourcing —if you have Facebook, Foursquare, POS data and Radian 6, do you
really need Nielsen and NPD?
Bottom Line: You no longer need to make a Wal-Mart
size investment to analyze your data.
Questions???
Feel free to contact us…

Marilyn Craig
- [email protected]
- LinkedIn: www.linkedin.com/marilyncraig

Terence Craig
- [email protected]
- www.twitter.com/terencecraig
- blog.patternbuilders.com

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