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An Amateur’s View on Big DataBig Data, Mobile & 80 Seconds
@rolfeswinton
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
Facebook @ 1Bn in Oct ‘12
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UsersIn Millions
Source: Benphoster.com@rolfeswinton
Vast Amounts of Data
Source: IDC, EMC. 1EB = 1 Billion GB.
@rolfeswinton
And the Next Step Change…• The fist quantum computer
now on-line = 50,000+ servers
• Wearable computing = near perfect information on consumer behavior
@rolfeswinton
Big Data Laws #1
Start with the pain in mind
What is the specific question you need to answer?
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
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
• The greater the speed of analysis, the greater the predictive value
• But usually means rethinking current business processes…
Big Data Laws #4
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
About RealityMine
RealityMine provides device-centric consumer behavioral
analytics
@rolfeswinton
#1 – What’s the Pain?The Hypothesis
Sell to ExistingCustomers
Attract NewCustomers
Sell Existing Types of Merchandise
Introduce New Types of Merchandise
Easiest,Fastest,
Least Risky
Most Difficult
@rolfeswinton
#2 Bring Data TogetherBig Retail Data Sets to Fuse
$Customer
Identification of the Specific
Opportunities for Growth
Concept
Categories Competition
ChannelShopper, Inventory
&Financial
Data
con
sum
er fe
eb
ack
@rolfeswinton
#2 Bring Data TogetherData From the Entire Path to Purchase
Ad Analytics
GPS/Triangulation Location and Mobile Behavioral Analytics
Store-level Intelligence
WiFiDataInventory DataFinancial Data
Fixture Level IntelligenceMobile DataPOS Data
@rolfeswinton
#3 Be Ready for Surprises
The Scale of Opportunity
The total potential customer spend that can be addressed by the retailer
@rolfeswinton
#3 Be Ready for SurprisesWhen is the Optimal Time to Reach Digital Shoppers?
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Use Per Hour
Category
News and Media
Shopping
Social Networking
Travel
@rolfeswinton
#3 Be Ready for SurprisesWhy Does WiFi in Stores Drive Increase Sales?
@rolfeswinton
#3 Be Ready for SurprisesRelative Opportunity by Customer Segment
Proprietary analytics to identify and quantify specific customer segments for targeting that have the greatest potential
@rolfeswinton
#3 Be Ready for Surprises
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$0 $100 $200 $300 $400 $500 $600 $700 $800 $900
Understanding Right Pricing
PRICE POINTS PERCEIVED TOO EXPENSIVE AT CLIENT
PERCEIVEDCHEAP
A.S.P.
}InventoryConcentration
PRICE POINT NOT REPRESENTED
Identify Where Additional Options Are Justified Or Where The Category Needs To Be Edited – Can Be Done By Channel
@rolfeswinton
#3 Be Ready for Surprises
$0 $2.5 $5.0 $7.5 $10.0 $12.5
Lost Sales Opportunity ($Millions)
Usually In-Stock
Good Return Policy
Sales/Promotions
More Choice
Brands I Want $11.1M
Not Having the Right Brands at Our Client Costs the Company $11.1M in Lost Sales to its Major Competitor
Top 5 Reasons Why Customers Buy at Major Competitor
@rolfeswinton
#3 Be Ready for SurprisesWhat’s the Optimal Offer to Deliver to Specific Shoppers at Specific Times?
2 for 1 10% offHow to Use the Product
@rolfeswinton
#3 Be Ready for Surprises
Long Checkout Lines Costing $275M =
automated staff triggers to add tills or mobile
checkout staff
“Showrooming” via competitor sites
costing$132M = mix of smarter bundled
offers & in-store help / support
$320M opportunity to capture sales from one key
competitor via targeted offers optimized through
real-time A/B testing
Lack of Clear Information Hierarchy & Poor
Customer Circulation Costing $78M = Strategic
information delivery
What is the Opportunity Inside the Store?
@rolfeswinton
#4 Speed = Predictive Value Applying Real-time Analytics
Jan MarMay Jul
Sep
Nov0
0.51
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Top QuartileBottom Quartile
Strategic problem identified: e.g. Long checkout lines cost the company $275M annually
Dashboards set-up to report daily checkout line avg. wait times to operations management
Messages sent to store managers in real-time when long queues are anticipated
Feedback loop
@rolfeswinton
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
#4 Speed = Predictive ValueCreate 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
What’s it Worth?
“The Most Profitable Customer is the Omni-channel Customer” — Forrester
$4 : $1Omni-channel customer
Single channel customer
Relative difference in sales by customer
@rolfeswinton
Retail Pricing and Promotions
Mass SalesData driven real-time pricing, offers and product recommendations
THEN... NOW...
@rolfeswinton
Personalised Media
Editorial Control 45,000 unique versions every 5 minutes
THEN... NOW...
@rolfeswinton
Fraud / Insurance Management
Credit databases Behavioral profiles
THEN... NOW...
@rolfeswinton
Health
Annual Checkup Continual Personal Monitoring
THEN... NOW...
@rolfeswinton
Disaster Avoidance
Manual Signalling Data / Sensor Driven Alerts
THEN... NOW...
@rolfeswinton
An Amateur’s View on Big DataBig Data, Mobile & 80 Seconds
@rolfeswinton
