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Retail: Lessons Learned from the Original Data- Driven Business and Future Directions Presenters: Marilyn Craig, Senior Director, WW Sales & Marketing.

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Presentation on theme: "Retail: Lessons Learned from the Original Data- Driven Business and Future Directions Presenters: Marilyn Craig, Senior Director, WW Sales & Marketing."— Presentation transcript:

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 Data Surfer, But a Technology Driver

6 As Technology Evolved, Retail has Adapted and Demanded Paper Inventory Records Mainframe EDI Networks EDI Over the Internet Multinational Collaborative Forecasting Real-time Inventory Decision- Making

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

8 What Keeps Retail Operating on the Technology Edge? PriceProduct PlacePromotion It’s about the 4 P’s creating all that data and all that data driving decisions about the 4 P’s.

9 About All That Data… Sources of Retail Data Channel, Reseller, Retailer, DC, Store, Online Brand, Product, SKU, Serial Number, RFID Sell-in, Sell- thru (and again), Sell-out Channel/Trade programs, discounts, rebates CRM, Loyalty, personalized coupons Price, margin, elasticity Advertising, promotion lift library, web-to- store, POP 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

10 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?

11 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?

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… Retail Store Data Smart phones (as payment devices) Brand protection, expiration management Broader and deeper loyalty data RFID or something similar (finally) More Ecommerce = More Data Traditional retail data is moving towards real-time.

15 There’s More Data from the Supply Chain… Supply Chain Data Real time monitoring of shipment Real-time demand- based delivery Track and trace Increased data sharing (3PLs, suppliers, retailers) Routes and packaging Retailer consolidation and Wal- Mart hegemony 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” Legal Data privacy laws. Multi-national retailers will encounter huge problems. Cultural In U.S., most valued customers are the most protective of their privacy. Participants don’t want to play nicely with each other. Technology 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 Manufactures/Suppliers will be early adopters.

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  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.

20 Questions??? Feel free to contact us…  Marilyn Craig - - LinkedIn: LinkedIn:  Terence Craig - - -

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