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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

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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

- [email protected]

- LinkedIn:

Terence Craig

- [email protected]

- www.twitter.com/terencecraig

- blog.patternbuilders.com