Copyright © 2011 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
Winning with Analytics:
Secrets of Data Charged Organizations
Jeanne G. Harris
Executive Research Fellow, Director of Research
Accenture Institute for High Performance
Copyright © 2011 Accenture All Rights Reserved.
Analytics are changing the way people
learn, make decisions and compete.
“We all use math every day. . . To forecast the weather, to handle money. We also use math to analyze crime, to predict patterns and to predict behavior.”
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Analytics Are Transforming Professional
Sports
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Research Background
• 2000 study of 20 companies and how they built analytical capabilities
• 2005 study of 32 companies with business intelligence initiatives; resulted in ―Competing on Analytics‖ article in HBR
• Additional interviews with 50+ companies for book research
• Accenture surveys of 217 and 402 companies in 2002 and 2006 to determine frequencies of analytical activity among large companies with enterprise systems
• A year of research with the business research consortium of 27 companies
• Talent Engagement, Attitudes and Motivations Survey of 1367 US respondents, including 799 analysts
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Research Background
• 2000 study of 20 companies and how they built analytical capabilities
• 2005 study of 32 companies with business intelligence initiatives; resulted in ―Competing on Analytics‖ article in HBR
• Additional interviews with 50+ companies for book research
• Accenture surveys of 217 and 402 companies in 2002 and 2006 to determine frequencies of analytical activity among large companies with enterprise systems
• A year of research with the business research consortium of 27 companies
• Talent Engagement, Attitudes and Motivations Survey of 1367 US respondents, including 799 analysts
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Analytics Move to Center Stage
• Analytics: The extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions & actions.
• Analytics, statistics, and fact-based decisions are not new to organizations
• DSS, ESS, BI, etc were important and provided value, but were often marginal to the mainstream of the business
• With organizations that compete on analytics, the capability moves to center stage
“Analytics is the new plastics.” -- John Ridding, Financial Times CEO
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How do organizations compete – and win –
with analytics?
These high performers have discovered the power
of analytics to out-think and out-execute the
competition.
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Capital One’s Information Based
Strategy
• Capital One uses analytics in every aspect of their business, which they refer to as their ―Information Based Strategy‖
• Analytics are used extensively to: – Identify and target unique customer segments – Assess credit and risk – Evaluate income and spending behaviors – Determine revenue potential – Predict and prevent fraud – Optimize collections – Tailor customer offers/terms – Evaluate and hire employees
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Progressive Insurance:
“Skimming the cream . . .”
• Progressive Insurance excels by seeking desirable sub-segments of undesirable risk pools
• All motorcycling enthusiasts are not the same
– Hell’s Angels
– Weekend suburbanites
– Urban commuters
– Stunt riders
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Caesar’s Entertainment is an
Analytical Competitor
―Opportunities abound to employ simple analytic methods to substantially increase profitability, especially in large businesses such as mine where a single insight can ring the cash register literally millions of times.
A ten-basis-point movement of slot pricing toward the estimated demand curve for a given game could enhance our profitability by an eight-figure amount and be unobservable to the guest.‖
‒ Gary Loveman, CEO
Art, Gambling or Science?
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Forces Driving Trend to More
Sophisticated Analytics
• Data
– Maturing transactional enterprise systems
– Growing standardized external information
– More data through the internet and other sources
– More data about the physical world
• Technology
– Maturing IT infrastructure and analytical architecture
– Sophisticated analytical techniques
– Massive processing power
– Ability to analyze unstructured content, including text and images
– Automated applications with embedded rules and models
• Demand
– New generation of analytical leaders
– Growing financial oversight requirements
– Increasing importance of strategies requiring greater precision and insights into individual behaviors
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Why make decisions on facts and
analytics instead of your gut?
• Extensive evidence that having
experts is good, but experts
using analytics is much better
• Statistical predictions
consistently outperform ―gut
based‖ predictions
• Expert intuition is best only
when there is little time, limited
data and few variables
40% of major business decisions are not based not on data and facts, but on “gut instinct”
– Accenture research
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High performance is associated with more extensive
and sophisticated use of analytical capabilities.
Low Performers High Performers
Have significant decision-support/analytical capabilities 23% 65%
Value analytical insights to a very large extent 8% 36%
Have above-average analytical capability within industry 33% 77%
Use analytics across their entire organization 23% 40%
High performers are 5 times more likely to say “Analytics is a key
element of our business strategy” than low performers
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Companies that have invested heavily in analytics
capabilities significantly outperform the market
Market Performance 2002 – 2009
Companies that invest heavily in advanced analytical capabilities outperform the S&P 500 on average by 64%
Companies that invest heavily in developing analytical skills and adopting an analytical mindset recover quicker from economic downturns
Analytical Shakers*
S&P 500 Index
Source: Accenture research 40%
70%
100%
130%
160%
190%
220%
250%
2002 2003 2004 2005 2006 2007 2008 2009
Companies that have invested in advanced analytics capabilities significantly outperform the market.
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What’s the best that can happen?
(Optimization)
What’s the next best action?
(Recommendation)
How and why did it happen? (Modeling)
What will happen? (Prediction, Simulation)
What is happening now? (Alerts)
What happened? (Reporting)
Past Present Future
Information
Insight
Analytical Questions
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Business Intelligence, Analytics and Decisions:
Bridging the Language Barrier
Competitive Advantage
Optimization
Predictive Modeling
Forecasting/
extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard Reports
―What’s the best that can happen?‖
―What will happen next?‖
―What if these trends continue?‖
―Why is this happening?‖
―What actions are needed?‖
―What exactly is the
problem?‖
―How many, how often, where?‖
―What happened?‖
Descriptive Analytics (the “what”)
Sophistication of Intelligence
Predictive Analytics (the “so what”)
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―We share many of the same pains as business. We may articulate them differently, but we still need to show the best possible return on our investment."
– Calvin Johnson, Director of the Office of Research and Evaluation Court Services and Offender Supervision Agency for the District of Columbia
• Allocate Resources Effectively DC Court Services and Offender Supervision Agency
• Predictive model creates a risk assessment profile for probationers
• Used to allocate resources, match probationers to officers and programs to optimize chances of success
• Improved Transparency for Enterprise Performance New York City Citywide Performance Reporting (CPR)
• Gives New Yorkers access to 300 constantly updated performance metrics from city agencies
• Customer Targeting Royal Shakespeare Company
• Examined seven years of ticket sales to optimize its share of wallet among its existing customers—and to identify new audiences
• Increased the number of ―regulars‖ by more than 70 percent.
Analytics in public sector
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Overview of Business Intelligence Capabilities
Example BI Application – DC Court Services & Offender Supervision Agency
CSOSA Mission: Increase public safety, prevent crime, reduce recidivism,
and support the fair administration of justice
– A small team of case workers, criminologists, and statisticians leverage
behavioral, medical, and judicial data on 15,000 detainees and parolees
– Created the AUTO Screener, an ―intelligent‖ risk and needs assessment
analytics tool that determines the appropriate level of supervision for offenders
and generates an individualized prescriptive supervision plan, including
recommendations for treatment and support services.
– CSOSA leverages analytics to optimize scarce resource allocations – 20% of
offenders use a kiosk to check in, enabling more focus on riskier offenders
“We now have an analytical approach to reducing recidivism. We can figure out what works and what doesn’t. When you can show this, it is easier to secure appropriate funding,’’ says Calvin Johnson. “But ultimately, what this does is keep the citizens of Washington, DC, a lot safer.”
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Talent analytics
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US Air Force • Uses analytics to evaluate the impact of
any training or curriculum change and predict resource requirements more than five years out.
• Tracks 250 variables that can affect student outcomes.
• Maintains “impact statements for more than 400 courses and 100 skill levels, understanding results by cost, capacity, readiness and skill.
Houston Rockets • Using talent analytics, the Rockets were able to
identify the most undervalued player in the NBA – Shane Battier.
• By all conventional measures – points, rebounds, blocks, etc. – Battier is average at best.
• Every team he has ever played on seemingly acquires a magical ability to win.
• When he is on the court, his teammates get better, often much better, and his opponents get worse — often much worse.
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Analytics at Work:
Smarter Decisions, Better Results
• Not every organization is going to use
analytics as a means of competitive
differentiation
• But every organization can benefit by
improving how they:
– use data to gain deeper insights
– make smarter decisions
– execute decisions more consistently
– get better results
• Analytics at Work shows analytically-
oriented managers how to guide their
organizations toward greater analytical
maturity
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―I'm not interested in data; I'm interested in translating data into
information for decision making. . . . So you take data, you turn
it into information, you apply it, and you make better decisions
because you know more than anybody else. I think that's real
power—and that was our hidden advantage for years.‖ -- Leonard Schaeffer, Retired CEO, Wellpoint
―Having analytical capabilities and the best data in the world doesn’t create competitive advantage. Changing the way the business uses it is the only way to create advantage.‖
-- Steven Udvarhelyi, MD, Senior Vice President and Chief Medical Officer,
Independence Blue Cross
Executive Perspectives on building
an analytical capability.
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Analytical Maturity Model Stage 5
Analytical
Competitors
Stage 4
Analytical Companies
Stage 3
Analytical Aspirations
Stage 2
Localized Analytics
Stage 1
Analytically Impaired
Analytical Maturity Model
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The Analytical DELTA
Data . . . . . . . . . . . . . . . . breadth, integration, quality
Enterprise . . . . . . . .approach to managing analytics
Leadership . . . . . . . . . . . . .passion and commitment
Targets . . . . . . . . . . . . . . . . . . . first deep, then broad
Analysts . . . . . . . . . . . . .professionals and amateurs
DELTA = change
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Two Paths to Becoming an Analytical Organization
Prove It Detour
• Build success with pilot projects
• Measure benefits • Spread the word
Fast Path
• Hire people • Build systems • Establish processes
Which best describes your senior management?
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Sustaining an Analytical Culture
• Pervasive ―test and learn‖ emphasis
• Free pass for pushbacks
– ―Do you have data to support that
hypothesis?
• A focus on action after analysis
– At P&G, analysts evaluated not by
answers, but by breakthrough
results
• Meritocracy – where the best ideas win
• Management reward and compensation
based on real performance
• Never resting on your analytical laurels
• All hard to do without analytical
leadership
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Analytics for Decisions
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What are the top 10 decisions
your organization makes?
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Embed Customer Analytics into Decision Processes
Who are my target customers, what, where and why do they buy, and who else looks like them?
What do I offer them, through which channels, and how often?
How do I get my consumers/customers to buy more products?
How do we become more relevant beyond the transaction?
Strategic Segmentation & Profiling
Consumption
Economic
Psychographic
Micro/Tactical Segment Basket Analysis
Store Basket Online Basket
Marketing Analytics & Optimization
Promotional Analytics & Optimization
Personal
Care
Skin & Hare
Care
Dispensary
OTC
First Aid /
Non Medical
Vitamins
Dietary / Health
Foods
Medicated
Skin
Medicated
Hair
Medicated FootEye & Ear
Family
Planning
Health & Fitness
Equipment
Homecare
Sanitary Protection
Men’s Grooming
Bath / Deo / Talc
Baby
Personal Care Electrical
Haircare
Paper Goods /
Cotton
Oral
Facial
Hand / Body /
Depilatories
Non Demo
Demo
Services & Concession
Gift Confectionary
Choc / Candies / CountlinesSnacks / Biscuits
Beverages
Sundries
Fashion
Home
Health &
Wellness
General
MerchandiseCosmetics
Food &
Sundries
0%
5%
10%
15%
20%
25%
6 8 10 12 14 16 18 20 22 24
Experimental Designs Financial & Statistical Analyses Predictive Modeling
Complementary vs Substitutes`
Store vs Long Tail
Discounts & Rebates Merchant Tie-Ins Loyalty Points Promo
Zone Pricing, & Price Elasticity Promotional Pricing Relationship-based Pricing
Space Analytics & Optimization
Sales: Net Impact to Overall Satisfaction by Interaction Type
3.81 3.56
000.450
01.47
5.99
1.43
0
1
2
3
4
5
6
Aware Consider Purchase Deliver &
Install
Learn Support &
Repair
Customer Interaction
Net
Im
pact
to
Ove
rall
Inte
ract
ion
Sales All
Placement & Banner Ads
Macro/Micro Adjacencies
Pricing Analytics & Optimization
Customer Experience Analytics
Brand Dissatisfiers & Drivers Net Satisfaction Impact Treatment Contribution
Affinity Analysis
15.6%
12.9% 12.7%
11.0%
8.4% 8.1%
5.4%
2.1%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
A B C D E F G H
Aff
init
y R
ate
$-
$5.00
$10.00
$15.00
$20.00
$25.00
Av
g.
Ma
rgin
$ p
er
Tra
ns
ac
tio
n
% of Transactions Avg $ per Transaction
Customer Loyalty Analytics
Brand Advocacy & Net Promoter Accumulation/Redemption Analyses Loyalty Driver & Value Analyses
Contact Ctr Basket
Assortment Analytics & Optimization
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We’re all headed towards a more analytical future.
• More organizations will build enterprise-wide analytical capability
• Recognition that analytics are only valuable when they result in
smarter decisions that lead to better results
• Key challenges on the way:
– Securing sponsorship and operational alignment
– Building an analytical capability using the DELTA framework
– Understanding the causal factors that lead to high performance
– Judiciously combining the science of quantitative analysis with the
art of sound reasoning
– Sustaining an analytical capability by:
• Embedding analytics in major business processes
• Reinforcing an analytical culture
• Continually reviewing assumptions, market conditions
and analytical models
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Full disclosure
If you build an analytical capability, you will:
– Make better strategic, tactical and operational decisions
– Be better able to solve problems
– Have better business processes
– Make faster decisions and get more consistent (and predictable) results
– Be able to anticipate shifting trends and market conditions
– Achieve better business results and improved service delivery
An analytical capability will NOT:
– Ensure that every decision will be right every time
– Be the only thing you need to make good decisions
– Be the only way to be successful
– Prevent (or insulate you from) changing market conditions
– Necessarily make you a big movie star
– Be built in a day (but neither is any other sustainable advantage).
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“In business, as in baseball, the question isn’t
whether or not you’ll jump into analytics. The
question is when. Do you want to ride the
analytics horse to profitability…or follow it
with a shovel?”
Rob Neyer, ESPN
Building an Analytical Capability Doesn’t
Happen Overnight . . .So Start Now.
• It takes a while to put data and IT in place, and even
longer to develop human capabilities, a fact-based
culture, and ―success stories‖
• 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.‖
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To learn more . . .
• Competing on Analytics: The New Science of Winning (HBP, 2007)
• Analytics at Work: Smarter Decisions, Better Results (HBP, 2010)
• Competing on Talent Analytics, Harvard Business Review, October 2010
• The Allure of Pricing Predictively, Outlook Journal, May 2011
• What People Want (and How to Predict It) and Prediction Lover’s Handbook, Sloan Management Review, Jan 2009
• How to turn data into a strategic asset, Outlook Journal, June 2010
• Counting on Analytical Talent, Accenture Research Report, March 2010