Winning with Analytics: Secrets of Data Charged Organizations

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.”

Copyright © 2011 Accenture All Rights Reserved. 3 3

Analytics Are Transforming Professional

Sports


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


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


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


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.

Copyright © 2011 Accenture All Rights Reserved. 8

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

http://www.glucpol.pl/wyp/logo_1.gif

http://images.google.com/imgres?imgurl=http://www.theiia.org/chapters/files/35/i/logo_capitalone2.gif&imgrefurl=http://www.theiia.org/chapters/index.cfm/view.news_detail/cid/35/newsid/9088&h=330&w=1228&sz=12&hl=en&start=1&sig2=ZuWAd7d8CcPQXYofNRN1MQ&um=1&tbnid=40C9kYNL6dA1SM:&tbnh=40&tbnw=150&ei=BlZNSJqvF4yipwTi7JTMBA&prev=/images?q=Capital+One+logo&um=1&hl=en&rls=com.microsoft:en-US&sa=N


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


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?


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


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


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


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.

14


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


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”)


―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


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.”


Talent analytics

19

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.


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


―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.


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


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


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?


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


Analytics for Decisions

26

What are the top 10 decisions

your organization makes?


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


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


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).


“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.‖


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

http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture_Counting_on_Analytical_Talent.pdf