Competing on Competing on AnalyticsAnalytics
The New Science of WinningThe New Science of Winning
Tom DavenportTom DavenportUniversity of Houston ISRCUniversity of Houston ISRCNovember 15, 2007November 15, 2007
Thomas H. Davenport – Competing on Analytics 2 | 2007 © All Rights Reserved.
The Planets Are Aligned for AnalyticsThe Planets Are Aligned for Analytics
Powerful IT
Data critical mass
Skills sufficiency
Business need
Thomas H. Davenport – Competing on Analytics 3 | 2007 © All Rights Reserved.
What Are Analytics?What Are Analytics?
AnalyticsWhat’s the best that can happen?
What will happen next?
What if these trends continue?
Why is this happening?
What actions are needed?
Where exactly is the problem?
How many, how often, where?
What happened?Co
mp
etit
ive
Ad
van
tag
e
Degree of Intelligence
Reporting
Decision Optimization
Predictive Analytics
Forecasting
Statistical models
Alerts
Query/drill down
Ad hoc reports
Standard reports
Thomas H. Davenport – Competing on Analytics 4 | 2007 © All Rights Reserved.
What Should Organizations Do with What Should Organizations Do with Analytics?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
Thomas H. Davenport – Competing on Analytics 5 | 2007 © All Rights Reserved.
What Is Analytical Competition About?What Is Analytical Competition About?
Dispassionate analysis
Data and statistics
Computers
Discipline and rigor
Passionate advocacy
Intuition
People
Creativity and insight
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Analytical Competitors Analytical Competitors Old Hands Polishing Their EdgeOld 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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Analytical Competitors Analytical Competitors Major Turnaround in Strategy or CultureMajor 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
Thomas H. Davenport – Competing on Analytics 8 | 2007 © All Rights Reserved.
Analytical Competitors Analytical Competitors Number-Crunchers from BirthNumber-Crunchers from Birth
Capital One — “Information-based strategy”
Amazon — Supply chain, advertising, page changes
Yahoo — Pages as controlled experiments
Netflix — Movie preference algorithms
Thomas H. Davenport – Competing on Analytics 9 | 2007 © All Rights Reserved.
Analytical Competitors Analytical Competitors Cut Across IndustriesCut Across Industries
Consumer Products
• Kraft
• Mars
• E&J Gallo
Financial Services
• Bank of America
• Barclay’s
• Humana
Government
• New York Police Dept.
• VA Hospitals
• Army Recruiting
Industrial Products
• Deere
• Cemex
Retail
• J.C. Penney
• Best Buy
Transport / Travel and Entertainment
• FedEx
• Schneider
• Hilton
Thomas H. Davenport – Competing on Analytics 10 | 2007 © All Rights Reserved.
Analytics in Professional SportsAnalytics 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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The Analytical DeltaThe Analytical Delta
PROGRESS
PE
RFO
RM
AN
CE
PIE
CE
S
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STAGE 5: Analytical Competitors
STAGE 4: Analytical Companies
STAGE 3: Analytical Aspirations
STAGE 2: Localized Analytics
STAGE 1: Analytically Impaired
The Analytical Performance DeltaThe Analytical Performance Delta
11/32 firms
6/32
7/32
6/32
2/32
More analytical =higher performanceP
ER
FOR
MA
NC
E
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15% of top performers versus 3% of low performers indicated that analytical capabilities are a key element of their strategy.
No analytical capability
Minimal analytical capability
Some analytical capability
Above average analytical capability
Analytic capability is a key element of
strategy
12%
0%
33%
8%
27%
37%
19%
47%
9% 10%
Source: Accenture Survey of 205/392 companies
The Analytical Performance Delta (cont.)The Analytical Performance Delta (cont.)
2002
2006
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High Performers Use AnalyticsHigh Performers Use Analytics
65 % have significant decision-support/analytical capabilities 23%
36 value analytical insights to a very large extent 8
77 have above average analytical capability within industry 33
77 have BI/Data Warehouse modules installed 62
73 make decisions based on data and analysis 51
40 use analytics across their entire organization 23
High LowPerformers Performers
Top performers have a greater analytical orientation than low performers.
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How Analytical Competitors Make MoneyHow 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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The Analytical Landscape Is Always ChangingThe 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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The Analytical DELTA — Pieces The Analytical DELTA — Pieces
PIE
CE
S
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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DataData
The prerequisite for everything analytical
Clean, common, integrated
Accessible in a warehouse
Measuring something new and important
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New Metrics / DataNew Metrics / Data
Wine Chemistry Run ProductionDriving Data
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EnterpriseEnterprise
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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Enterprise-Wide Customer ViewEnterprise-Wide Customer View
Sales Marketing Logistics Service
InternalTransaction
WebMetrics
ExternalGeo-Demo
ExternalAttitudinal
Types ofData
Processes in Which Data Used
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LeadershipLeadership
Gary Loveman at Harrah’s “Do we think, or do we know?” “Three ways to get fired”
Barry Beracha at Sara Lee “In God we trust, all others bring data”
Jeff Bezos at Amazon “We never throw away data”
“Our CEO is a real data dog”
Sara Lee executive
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The Great DivideThe Great Divide
Is your senior management team committed?
Full steam ahead!• Hire the people• Build the systems• Create the processes
Prove the value!• Run a pilot• Measure the benefit• Try to spread it
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TargetsTargets
With limited analytical resources, pick a major strategic target, with a minor or twoHarrah’s = Loyalty + Service
Patriots = Player selection + TFE
Barclay’s = Asset analysis + Credit cards
UPS = Operations + Customer data
Can also have two primary user group targetsWal-Mart = Category managers + Suppliers
Owens & Minor = Logistics + Hospitals
Progressive = Actuaries + Customers
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AnalystsAnalysts
5-10%
Analytical Professionals— Can create algorithms
Analytical Semi-Professionals— Can use visual tools, create simple models
Analytical Amateurs— Can use spreadsheets
15-20%
70-80%
Thomas H. Davenport – Competing on Analytics 26 | 2007 © All Rights Reserved.
Taking ActionTaking Action
Analytics need to be embedded into the machinery of organizational action
Operational decision-making
Business processes
Manager and employee behavior
Customer expectations
Thomas H. Davenport – Competing on Analytics 27 | 2007 © All Rights Reserved.
The Analytical DELTA — Progress The Analytical DELTA — Progress
PROGRESS
Success FactorStage 1
Analytically Impaired
Moving to:
Stage 2Localized Analytics
Stage 3Analytical
AspirationsStage 4
Analytical Companies
Stage 5Analytical
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
Thomas H. Davenport – Competing on Analytics 28 | 2007 © All Rights Reserved.
Next Steps for AnalyticsNext 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
Thomas H. Davenport – Competing on Analytics 29 | 2007 © All Rights Reserved.
It Doesn’t Happen Overnight — Start Now!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.”