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
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
Retail is Not Just a Big Data Retail is Not Just a Big Data Surfer, But a Surfer, But a Technology DriverTechnology Driver
As Technology Evolved, Retail has Adapted and Demanded
What We Now Consider Mainstream, has Retail Roots
RFID VPNs
In-Transit Trackin
g
Real-Time Logistics
Supply Chain Management
Environmental Sensors
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?
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.
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
But Nothing Remains the Same…
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 salesThe 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…
Traditional retail Traditional retail data is moving data is moving
towards real-time.towards real-time.
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.
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
Roadblocks to Analytics “Perfection”
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
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 Bottom Line: You no longer need to make a Wal-Mart size investment to analyze your Wal-Mart size investment to analyze your
data.data.
Questions???
Feel free to contact us…
Marilyn Craig
- LinkedIn:
Terence Craig
- www.twitter.com/terencecraig
- blog.patternbuilders.com