Competing on
Analytics
The New Science of Winning
Tom Davenport
University of Houston ISRC
November 15, 2007
The Planets Are Aligned for Analytics
 Powerful IT
 Data critical mass
 Skills sufficiency
 Business need
2 | 2007 © All Rights Reserved.
Thomas H. Davenport – Competing on Analytics
What Are Analytics?
Competitive Advantage
Analytics
Decision Optimization
What’s the best that can happen?
Predictive Analytics
What will happen next?
Forecasting
What if these trends continue?
Statistical models
Why is this happening?
Alerts
What actions are needed?
Query/drill down
Where exactly is the problem?
Ad hoc reports
How many, how often, where?
Standard reports
What happened?
Reporting
Degree of Intelligence
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Thomas H. Davenport – Competing on Analytics
What Should Organizations Do with
Analytics?
 Using analytics is good
 Finding the best customers, and charging them
the right price
 Minimizing inventory in supply chains
 Allocating costs accurately and understanding
how financial performance is driven
 Competing on analytics is better
 Making analytics and fact-based decisions a key
element of strategy and competition
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Thomas H. Davenport – Competing on Analytics
What Is Analytical Competition About?
Dispassionate analysis
Passionate advocacy
Data and statistics
Intuition
Computers
People
Discipline and rigor
Creativity and insight
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Thomas H. Davenport – Competing on Analytics
Analytical Competitors
Old Hands Polishing Their Edge
 Marriott — Revenue management
 Wal-Mart — Supply chain analytics
 RBC — Cost and customer profitability
 P&G — Supply chain
 Progressive — Pricing risk
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Thomas H. Davenport – Competing on Analytics
Analytical Competitors
Major Turnaround in Strategy or Culture
 Harrah’s — Loyalty and service
 Tesco — Loyalty and Internet groceries
 MCI — Network pricing
 Rogers / Nextel / Verizon Wireless / Cablecom —
Customer relationship processes
 A’s / Red Sox / Patriots / Rockets — Players for price
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Thomas H. Davenport – Competing on Analytics
Analytical Competitors
Number-Crunchers from Birth
 Capital One — “Information-based strategy”
 Amazon — Supply chain, advertising, page changes
 Yahoo — Pages as controlled experiments
 Netflix — Movie preference algorithms
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Thomas H. Davenport – Competing on Analytics
Analytical Competitors
Cut Across Industries
Consumer Products
Industrial Products
• Kraft
• Deere
• Mars
• Cemex
• E&J Gallo
Financial Services
• Bank of America
• Barclay’s
• Humana
Government
• New York Police Dept.
• VA Hospitals
Retail
• J.C. Penney
• Best Buy
Transport / Travel and
Entertainment
• FedEx
• Schneider
• Hilton
• Army Recruiting
9 | 2007 © All Rights Reserved.
Thomas H. Davenport – Competing on Analytics
Analytics in Professional Sports
 Identify undervalued attributes
 Develop new performance metrics
 Know when a player is ready to move up
 Use your own selection criteria
 Assess the ability to work as part of a team
 Understand risk better than your competitors
 Determine who gets hurt and who gets tired
 Who inspires others to play better?
 Who drags down the team?
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Thomas H. Davenport – Competing on Analytics
The Analytical Delta
PROGRESS
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Thomas H. Davenport – Competing on Analytics
The Analytical Performance Delta
STAGE 5: Analytical Competitors
11/32 firms
STAGE 4: Analytical Companies
More analytical =
higher performance
6/32
STAGE 3: Analytical Aspirations
7/32
STAGE 2: Localized Analytics
6/32
STAGE 1: Analytically Impaired
2/32
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Thomas H. Davenport – Competing on Analytics
The Analytical Performance Delta (cont.)
15% of top performers versus 3% of low performers indicated
that analytical capabilities are a key element of their strategy.
47%
2002
2006
37%
33%
27%
19%
12%
9%
8%
10%
0%
No analytical
capability
Minimal analytical
capability
Some analytical
capability
Above average
analytical
capability
Analytic capability
is a key element of
strategy
Source: Accenture Survey of 205/392 companies
13 | 2007 © All Rights Reserved.
Thomas H. Davenport – Competing on Analytics
High Performers Use Analytics
Top performers have a greater analytical orientation than low performers.
High
Performers
Low
Performers
65 % have significant decision-support/analytical capabilities 23%
36
77
77
value analytical insights to a very large extent
8
have above average analytical capability within industry 33
have BI/Data Warehouse modules installed
62
73
make decisions based on data and analysis
51
40
use analytics across their entire organization
23
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Thomas H. Davenport – Competing on Analytics
How Analytical Competitors Make Money
 Optimize a distinctive capability or external relationship
 Customer relationships, supply chain, HR, R&D, etc.
 Harrah’s, Marriott, Amazon, etc.
 Understand and take action on the business better
 MCI, Sara Lee Bakeries, RBC
 Offer analytics to customers as the core offering
 Apex Management Group in insurance risk management
 Franklin Portfolio Associates in equity portfolio development
 Offer analytics to customers to augment existing product or service
 SmartSwing in golf clubs
 Nielsen/IRI in retail/consumer products
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Thomas H. Davenport – Competing on Analytics
The Analytical Landscape Is Always Changing
 Airlines—letting a business model
become obsolete
 Baseball teams—on-base
percentage becomes over-valued
 Capital One—other banks catch up,
and they enter a new business
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Thomas H. Davenport – Competing on Analytics
The Analytical DELTA — Pieces
Data . . . . . . . . breadth, integration, quality
Enterprise . . . . . . . .approach to managing analytics
Leadership . . . . . . . . . . . . passion and commitment
Targets . . . . . . . . . . . first deep, then broad
Analysts . . . . . professionals and amateurs
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Thomas H. Davenport – Competing on Analytics
Data
 The prerequisite for everything analytical
 Clean, common, integrated
 Accessible in a warehouse
 Measuring something new and important
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Thomas H. Davenport – Competing on Analytics
New Metrics / Data
Wine Chemistry
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Driving Data
Run Production
Thomas H. Davenport – Competing on Analytics
Enterprise
 If you’re competing on analytics, it doesn’t make
sense to manage them locally
 No fiefdoms of data
 Avoiding the analytical equivalent of duct tape
 Some level of centralized expertise for hard-core
analytics
 Firms may also need to upgrade hardware and
infrastructure
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Thomas H. Davenport – Competing on Analytics
Enterprise-Wide Customer View
Types of
Data
Sales
Processes in Which Data Used
Marketing
Logistics
Service
Internal
Transaction
Web
Metrics
External
Geo-Demo
External
Attitudinal
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Thomas H. Davenport – Competing on Analytics
Leadership
 Gary Loveman at Harrah’s
 “Do we think, or do we know?”
 “Three ways to get fired”
 Barry Beracha at Sara Lee
“Our CEO is a real
data dog”
Sara Lee
executive
 “In God we trust, all others bring data”
 Jeff Bezos at Amazon
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 “We never throw away data”
Thomas H. Davenport – Competing on Analytics
The Great Divide
Full steam ahead!
Is your senior
management
team
committed?
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• Hire the people
• Build the systems
• Create the processes
Prove the value!
• Run a pilot
• Measure the benefit
• Try to spread it
Thomas H. Davenport – Competing on Analytics
Targets
With limited analytical resources, pick a major
strategic target, with a minor or two
Harrah’s = Loyalty + Service
Patriots = Player selection + TFE
Barclay’s = Asset analysis + Credit cards
UPS = Operations + Customer data
Can also have two primary user group targets
Wal-Mart = Category managers + Suppliers
Owens & Minor = Logistics + Hospitals
Progressive = Actuaries + Customers
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Thomas H. Davenport – Competing on Analytics
Analysts
5-10%
Analytical Professionals
— Can create algorithms
15-20%
Analytical Semi-Professionals
— Can use visual tools, create
simple models
Analytical Amateurs
— Can use spreadsheets
70-80%
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Thomas H. Davenport – Competing on Analytics
Taking Action
 Analytics need to be embedded
into the machinery of
organizational action
 Operational decision-making
 Business processes
 Manager and employee behavior
 Customer expectations
26 | 2007 © All Rights Reserved.
Thomas H. Davenport – Competing on Analytics
The Analytical DELTA — Progress
Moving to:
Success Factor
Stage 1
Analytically Impaired
Stage 2
Localized Analytics
Stage 3
Analytical
Aspirations
Stage 4
Analytical Companies
Stage 5
Analytical
Competitors
Data
Inconsistent, poor
quality, poorly
organized
Data useable, but in
functional or process
silos
Organization
beginning to create
centralized data
repository
Integrated, accurate,
common data in central
warehouse
Relentless search for
new data and metrics
Enterprise
n/a
Islands of data,
technology, and
expertise
Early stages of an
enterprise-wide
approach
Key data, technology
and analysts are
central-ized or
networked
All key analytical
resources centrally
managed
Leadership
No awareness or
interest
Only at the function or
process level
Leaders beginning to
recognize importance
of analytics
Leadership support for
analytical competence
Strong leadership
passion for analytical
competition
Targets
n/a
Multiple disconnected
targets that may not be
strategically important
Analytical efforts
coalescing behind a
small set of targets
Analytical activity
centered on a few key
domains
Analytics support the
firm’s distinctive
capability and strategy
Analysts
Few skills, and these
attached to specific
functions
Isolated pockets of
analysts with no
communication
Influx of analysts in
key target areas
Highly capable analysts
in central or networked
organization
World-class
professional analysts
and attention to
analytical amateurs
PROGRESS
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Thomas H. Davenport – Competing on Analytics
Next Steps for Analytics
 Continual pursuit of new data types
 Real-time action
 Content mining, intangibles analytics
 Engineering multi-modal decision-making
 Model management / analytical resource
management / knowledge management
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Thomas H. Davenport – Competing on Analytics
It Doesn’t Happen Overnight — Start Now!
 Takes a while to put data and infrastructure foundation
in place, and even longer to develop human
capabilities, a fact-based culture, and “success
stories”
 Barclay’s five-year plan for “Information-Based
Customer Management”
 UPS — “We’ve been collecting data for six or seven
years, but it’s only become usable in the last two or
three, with enough time and experience to validate
conclusions based on data.”
29 | 2007 © All Rights Reserved.
Thomas H. Davenport – Competing on Analytics