Retail lessons learned from the first data driven business and future directions.

1. Retail: Lessons Learned
from the Original Data-
Driven Business and
Future Directions
Presenters:
Marilyn Craig, Senior Director, WW Sales &
Marketing Planning and Analysis, Logitech
Terence Craig, CEO/CTO, PatternBuilders

2. 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.

3. 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

4. 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

5. Retail is Not Just a Big DataRetail is Not Just a Big Data
Surfer, But aSurfer, But a Technology DriverTechnology Driver

6. As Technology Evolved, Retail has Adapted and Demanded

7. What We Now Consider Mainstream, has Retail Roots
RFID VPNs
In-
Transit
Tracking
Real-Time
Logistics
Supply
Chain
Management
Environmental
Sensors

8. 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)
 One of the 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?

9. 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.

10. About All That Data…
3 years of historical data
for comparison
10 x 750 x 50 x 52 x 3 =
58,500,000 data points
4 regions to segregate the data
10 x 750 x 50 x 52 x 3 x 7 x 4 =
1,638,000,000 data points
50 states to segregate the data
10 x 750 x 50 x 52 x 3 x 7 x 4 x 50 =
81,900,000,000 data points
7 types of data to monitor (POS,
Inventory, Marketing, Syndicated, etc)
10 x 750 x 50 x 52 x 3 x 7 = 409,500,000
data points
8 categories to aggregate the data
10 x 750 x 50 x 52 x 3 x 7 x 4 x 50 x 8 =
655,200,000,000 data points
10 Retailers
to monitor
10 data points
750 Stores per
retailer to monitor
10 x 750 = 7500
data points
50 products per
store to monitor
10 x 750 x 50 =
375,000 data points
52 weeks of data per
year for trend analysis
10 x 750 x 50 x 52 =
19,500,000 data points
Now, Consider this:
655 Billion+ data points involved with
managing the retail sales channel

11. But Nothing Remains the Same…
Where do we go from here?

12. The Future: Look Out!
Cheap, big analytics is going to
change the world.

13. 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.

14. There’s More Data From the Store…
Traditional retail dataTraditional retail data
is moving towards real-is moving towards real-
time.time.

15. There’s More Data from the Supply Chain…
Humidity, Vibration,
Temperature,
Ever shortening lead times,
niche targeting, and regulation
drive this. Retailing and
supplying is a team sport.
Are analyzed constantly for
savings and regulatory
compliance.
Both are driving
standardization to an
amazing level.

16. What’s Coming: Big Data = Big Analytics
 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.
Welcome to
The Minority
Report

17. Roadblocks to Analytics “Perfection”

18. 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.

19. The Good News: Financial Constraints are Disappearing
With the advent of:
 OSS—who buys database licenses any more?
 Moore’s Law
 Kryder'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-MartBottom Line: You no longer need to make a Wal-Mart
size investment to analyze your data.size investment to analyze your data.

20. Questions???
Feel free to contact us…
 Marilyn Craig
- MCraig@logitech.com
- LinkedIn:
 Terence Craig
- Terence@patternbuilders.com
- www.twitter.com/terencecraig
- blog.patternbuilders.com