competing on analytics by thomas h. davenport & jeanne g. harris

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


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


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


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


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


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


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


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


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?


The Analytical DeltaThe Analytical Delta

PROGRESS

PE

RFO

RM

AN

CE

PIE

CE

S


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


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


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.


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


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


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


DataData

The prerequisite for everything analytical

Clean, common, integrated

Accessible in a warehouse

Measuring something new and important


New Metrics / DataNew Metrics / Data

Wine Chemistry Run ProductionDriving Data


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


Enterprise-Wide Customer ViewEnterprise-Wide Customer View

Sales Marketing Logistics Service

InternalTransaction

WebMetrics

ExternalGeo-Demo

ExternalAttitudinal

Types ofData

Processes in Which Data Used


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


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


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


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%


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


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


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


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.”

competing on analytics by thomas h. davenport & jeanne g. harris

Education

use analytics

analytics powerful

localized analytics

analyticalminimal analytical

analytical competition

rbc offer analytics

average analytical capability

driven competing