Big Amateur’sMobile & 80   An Data, View on Big Data          Seconds@rolfeswinton
What is Big Data?@rolfeswinton
@rolfeswinton
+@rolfeswinton
Predictions@rolfeswinton
Quantified Self: Physical & Digital@rolfeswinton
Quantified Self: Data Visualisation@rolfeswinton
Our Agenda  • The Landscape of Big Data  • 5 Key Principles to Really Benefit from Working    With Big Data  • How We Appl...
How Big is Big Data?
Facebook @ 1Bn in Oct ‘12        1200        1000UsersIn       800Millions         600         400         200           0...
Twitter at Over 400M / DayTweets PerDay InMillions@rolfeswinton
Vast Amounts of Data                                       Source: IDC, EMC. 1EB = 1 Billion GB.@rolfeswinton
Most of it Unstructured@rolfeswinton
Where is Big Data Coming From?
Mobile data                 Mobile phones@rolfeswinton
“Internet of Things” data                 Jet engine / cow?@rolfeswinton
Storage & Processing                     Einiac@rolfeswinton
Cloud Computing                      Cloud + Siri@rolfeswinton
Computing Efficiency Driving Consumption                                                Source: Cloudyn@rolfeswinton
And the Next Step Change…                      • The fist quantum computer                        now on-line = 50,000+ se...
Our 5 Rules of Working    With Big Data
Big Data Laws #1Start with the pain in mindWhat is the specific question you need toanswer?
Big Data Laws #2• Data needs to come together in one place  – Single CRM / customer identifier  – Personally Identifiable ...
Big Data Laws #3• Bring specific questions but be ready for  surprising answers, and the need to change the  question  – C...
Big Data Laws #4• The greater the speed of analysis, the greater the  predictive value• But usually means rethinking curre...
Big Data Laws #5• Understand Why You Have the Data You Have  – You have the capacity to visualise people’s lives  – Better...
How We ApplyThese Principles
About RealityMine        RealityMine provides device-centric          consumer behavioral analytics@rolfeswinton
The RealityMine Platform@rolfeswinton
#1 – What’s the Pain    How to Increase Profitable Sales?@rolfeswinton
#1 – What’s the Pain?    The Hypothesis     Attract New                                   Most     Customers              ...
#2 Bring Data Together  Big Retail Data Sets to Fuse                    Customer                                          ...
#2 Bring Data Together    Data From the Entire Path to Purchase   Ad            GPS/Triangulation        Store-level   Fix...
#3 Be Ready for Surprises    The Scale of Opportunity                                The total potential                  ...
#3 Be Ready for Surprises     When is the Optimal Time to Reach Digital Shoppers?@rolfeswinton
#3 Be Ready for Surprises     Why Does WiFi in Stores Drive Increase Sales?@rolfeswinton
#3 Be Ready for Surprises      Relative Opportunity by Customer Segment                                                 Pr...
#3 Be Ready for Surprises             Understanding Right Pricing                                                PRICE POI...
#3 Be Ready for Surprises           Top 5 Reasons Why Customers Buy at Major Competitor       Brands I Want               ...
#3 Be Ready for Surprises     What’s the Optimal Offer to Deliver to Specific Shoppers at     Specific Times?             ...
#3 Be Ready for Surprises     What is the Opportunity Inside the Store?                                                   ...
#4 Speed = Predictive Value    Applying Real-time Analytics                                                Strategic probl...
Real Impact    This Retailer increased sales by    over 20% – an improvement of    hundreds of millions of dollars –    wi...
#4 Speed = Predictive Value   Create More Shopper Value                      • Manage the                        appropria...
#5 Know What You Have
What’s it Worth?        ―The Most Profitable Customer is         the Omni-channel Customer‖ — Forrester                   ...
Some Opportunities ForTransformation We See
Retail Pricing and Promotions                 THEN...                  NOW...                           Data driven real-t...
Personalised Media                 THEN...                      NOW...            Editorial Control   45,000 unique versio...
Fraud / Insurance Management            THEN...                NOW...        Credit databases     Behavioral profiles@rolf...
Health                THEN...                        NOW...            Annual Checkup         Continual Personal Monitorin...
Disaster Avoidance            THEN...                       NOW...        Manual Signalling      Data / Sensor Driven Aler...
Big Amateur’sMobile & 80   An Data, View on Big Data          Seconds@rolfeswinton
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Big Data, Mobile, & 80 Seconds by Rolfe Swinton of RealityMine - Presented at Insight Innovation eXchange LATAM 2013

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Big Data, Mobile, & 80 Seconds by Rolfe Swinton of RealityMine - Presented at Insight Innovation eXchange LATAM 2013

  1. 1. Big Amateur’sMobile & 80 An Data, View on Big Data Seconds@rolfeswinton
  2. 2. What is Big Data?@rolfeswinton
  3. 3. @rolfeswinton
  4. 4. +@rolfeswinton
  5. 5. Predictions@rolfeswinton
  6. 6. Quantified Self: Physical & Digital@rolfeswinton
  7. 7. Quantified Self: Data Visualisation@rolfeswinton
  8. 8. Our Agenda • The Landscape of Big Data • 5 Key Principles to Really Benefit from Working With Big Data • How We Apply These in Our Work • Some More Opportunities for Transformation@rolfeswinton
  9. 9. How Big is Big Data?
  10. 10. Facebook @ 1Bn in Oct ‘12 1200 1000UsersIn 800Millions 600 400 200 0 Source: Benphoster.com @rolfeswinton
  11. 11. Twitter at Over 400M / DayTweets PerDay InMillions@rolfeswinton
  12. 12. Vast Amounts of Data Source: IDC, EMC. 1EB = 1 Billion GB.@rolfeswinton
  13. 13. Most of it Unstructured@rolfeswinton
  14. 14. Where is Big Data Coming From?
  15. 15. Mobile data Mobile phones@rolfeswinton
  16. 16. “Internet of Things” data Jet engine / cow?@rolfeswinton
  17. 17. Storage & Processing Einiac@rolfeswinton
  18. 18. Cloud Computing Cloud + Siri@rolfeswinton
  19. 19. Computing Efficiency Driving Consumption Source: Cloudyn@rolfeswinton
  20. 20. And the Next Step Change… • The fist quantum computer now on-line = 50,000+ servers • Wearable computing = near perfect information on consumer behavior@rolfeswinton
  21. 21. Our 5 Rules of Working With Big Data
  22. 22. Big Data Laws #1Start with the pain in mindWhat is the specific question you need toanswer?
  23. 23. Big Data Laws #2• Data needs to come together in one place – Single CRM / customer identifier – Personally Identifiable Information (PII) – Unified data structure across business (silos) – Sensor data – Social data, photos, messages, etc. – Small Data + Big Data• And plan for explosive growth in data volumes as you unify it
  24. 24. Big Data Laws #3• Bring specific questions but be ready for surprising answers, and the need to change the question – Creativity & science – Machine & human – Variety of ways to explore the data (visualisation) “The racing technology on the yachts competing for the 2013 America’s Cup will be the most advanced ever” - The Wall Street Journal MarketWatch
  25. 25. Big Data Laws #4• The greater the speed of analysis, the greater the predictive value• But usually means rethinking current business processes…
  26. 26. Big Data Laws #5• Understand Why You Have the Data You Have – You have the capacity to visualise people’s lives – Better be able to justify it to your customers and to regulators – Better be able to understand what’s worth keeping and what you need to get rid of
  27. 27. How We ApplyThese Principles
  28. 28. About RealityMine RealityMine provides device-centric consumer behavioral analytics@rolfeswinton
  29. 29. The RealityMine Platform@rolfeswinton
  30. 30. #1 – What’s the Pain How to Increase Profitable Sales?@rolfeswinton
  31. 31. #1 – What’s the Pain? The Hypothesis Attract New Most Customers Difficult Easiest, Sell to Existing Fastest, Customers Least Risky Sell Existing Types Introduce New Types of Merchandise of Merchandise@rolfeswinton
  32. 32. #2 Bring Data Together Big Retail Data Sets to Fuse Customer consumer feeback $ Channel Concept Identification of the Shopper, Inven tory Specific & Opportunities for Financial Growth Data Categories Competition@rolfeswinton
  33. 33. #2 Bring Data Together Data From the Entire Path to Purchase Ad GPS/Triangulation Store-level Fixture Level Analytics Location and Mobile Intelligence Intelligence Behavioral Analytics WiFiData Mobile Data Inventory Data POS Data@rolfeswinton Financial Data
  34. 34. #3 Be Ready for Surprises The Scale of Opportunity The total potential customer spend that can be addressed by the retailer@rolfeswinton
  35. 35. #3 Be Ready for Surprises When is the Optimal Time to Reach Digital Shoppers?@rolfeswinton
  36. 36. #3 Be Ready for Surprises Why Does WiFi in Stores Drive Increase Sales?@rolfeswinton
  37. 37. #3 Be Ready for Surprises Relative Opportunity by Customer Segment Proprietary analytics to identify and quantify specific customer segments for targeting that have the greatest potential@rolfeswinton
  38. 38. #3 Be Ready for Surprises Understanding Right Pricing PRICE POINT NOT A.S.P. REPRESENTED 12% PERCEIVED PRICE POINTS PERCEIVED Identify Where 10% CHEAP TOO EXPENSIVE AT CLIENT Additional Options 8% Are Justified Or 6% Where The Category 4% Needs To Be Edited 2% – Can Be Done By Channel 0% $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 } Inventory Concentration@rolfeswinton
  39. 39. #3 Be Ready for Surprises Top 5 Reasons Why Customers Buy at Major Competitor Brands I Want $11.1M More Choice Sales/Promotions Not Having the Right Brands at Our Client Costs the Company Good Return Policy $11.1M in Lost Sales to its Major Competitor Usually In-Stock $0 $2.5 $5.0 $7.5 $10.0 $12.5 Lost Sales Opportunity ($Millions)@rolfeswinton
  40. 40. #3 Be Ready for Surprises What’s the Optimal Offer to Deliver to Specific Shoppers at Specific Times? How to 2 for 1 10% off Use the Product@rolfeswinton
  41. 41. #3 Be Ready for Surprises What is the Opportunity Inside the Store? Lack of Clear Information Hierarchy & Poor Customer Circulation Costing $78M = Strategic information delivery ―Showrooming‖ via competitor sites costing$132M = mix of Long Checkout Lines smarter bundled offers Costing $275M = automated & in-store help / support staff triggers to add tills or mobile checkout staff $320M opportunity to capture sales from one key competitor via targeted offers optimized through real-time A/B testing@rolfeswinton
  42. 42. #4 Speed = Predictive Value Applying Real-time Analytics Strategic problem identified: e.g. Long checkout lines cost the company $275M annually Dashboards set-up to report 5 daily checkout line avg. wait 4 times to operations management 3 Top Quartile 2 Bottom 1 Quartile Messages sent to store 0 managers in real-time Jan Mar May Jul Sep Nov when long queues are Feedback anticipated loop@rolfeswinton
  43. 43. Real Impact This Retailer increased sales by over 20% – an improvement of hundreds of millions of dollars – with an increase in gross margin Only part of this implemented to date…@rolfeswinton
  44. 44. #4 Speed = Predictive Value Create More Shopper Value • Manage the appropriate delivery of – Digital product information – Coordinated item suggestions – Targeted promotional coupons – YouTube and/or other video content – Customer reviews of items – Real-time customer feedback – Help available online – Stock checking – Online ordering – Auto negotiation tools – Instant ability to request live help – And more…@rolfeswinton
  45. 45. #5 Know What You Have
  46. 46. What’s it Worth? ―The Most Profitable Customer is the Omni-channel Customer‖ — Forrester Relative difference in sales by customer $4 : $1 Omni-channel customer Single channel customer@rolfeswinton
  47. 47. Some Opportunities ForTransformation We See
  48. 48. Retail Pricing and Promotions THEN... NOW... Data driven real-time pricing, offers Mass Sales and product recommendations@rolfeswinton
  49. 49. Personalised Media THEN... NOW... Editorial Control 45,000 unique versions every 5 minutes@rolfeswinton
  50. 50. Fraud / Insurance Management THEN... NOW... Credit databases Behavioral profiles@rolfeswinton
  51. 51. Health THEN... NOW... Annual Checkup Continual Personal Monitoring@rolfeswinton
  52. 52. Disaster Avoidance THEN... NOW... Manual Signalling Data / Sensor Driven Alerts@rolfeswinton
  53. 53. Big Amateur’sMobile & 80 An Data, View on Big Data Seconds@rolfeswinton

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