1. Big Amateur’sMobile & 80
An Data, View on Big Data
Seconds
@rolfeswinton

2. What is Big Data?
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3. @rolfeswinton

4. +
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5. Predictions
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6. Quantified Self: Physical & Digital
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7. Quantified Self: Data Visualisation
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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. How Big is Big Data?

10. Facebook @ 1Bn in Oct ‘12
1200
1000
Users
In 800
Millions
600
400
200
0
Source: Benphoster.com
@rolfeswinton

11. Twitter at Over 400M / Day
Tweets Per
Day In
Millions
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12. Vast Amounts of Data
Source: IDC, EMC. 1EB = 1 Billion GB.
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13. Most of it Unstructured
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14. Where is Big Data
Coming From?

15. Mobile data
Mobile phones
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16. “Internet of Things” data
Jet engine / cow?
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17. Storage & Processing
Einiac
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18. Cloud Computing
Cloud + Siri
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19. Computing Efficiency Driving Consumption
Source: Cloudyn
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20. And the Next Step Change…
• The fist quantum computer
now on-line = 50,000+ servers
• Wearable computing = near
perfect information on
consumer behavior
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21. Our 5 Rules of Working
With Big Data

22. Big Data Laws #1
Start with the pain in mind
What is the specific question you need to
answer?

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. 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. Big Data Laws #4
• The greater the speed of analysis, the greater the
predictive value
• But usually means rethinking current business processes…

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. How We Apply
These Principles

28. About RealityMine
RealityMine provides device-centric
consumer behavioral analytics
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29. The RealityMine Platform
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30. #1 – What’s the Pain
How to Increase Profitable Sales?
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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
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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
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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. #3 Be Ready for Surprises
The Scale of Opportunity
The total potential
customer spend that
can be addressed by the
retailer
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35. #3 Be Ready for Surprises
When is the Optimal Time to Reach Digital Shoppers?
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36. #3 Be Ready for Surprises
Why Does WiFi in Stores Drive Increase Sales?
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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. #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
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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)
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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. #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. #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. 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. #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. #5 Know What You Have

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
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47. Some Opportunities For
Transformation We See

48. Retail Pricing and Promotions
THEN... NOW...
Data driven real-time pricing, offers
Mass Sales and product recommendations
@rolfeswinton

49. Personalised Media
THEN... NOW...
Editorial Control 45,000 unique versions every 5
minutes
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50. Fraud / Insurance Management
THEN... NOW...
Credit databases Behavioral profiles
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51. Health
THEN... NOW...
Annual Checkup Continual Personal Monitoring
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52. Disaster Avoidance
THEN... NOW...
Manual Signalling Data / Sensor Driven Alerts
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53. Big Amateur’sMobile & 80
An Data, View on Big Data
Seconds
@rolfeswinton