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© 2011 SAP AG. All rights reserved. 1

Predictive Analytics: Gaining Advantage
by Using Analytics to Predict the Future

Tom Davenport
President’s Distinguished Professor of Management
and Information Technology
Babson College

October 3, 2011 Brought to you by

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1
Copyright © 2011, SAS Institute Inc. All rights reserved.

Predictive
Analytics at Work

Tom Davenport
Babson College

Harvard Business Review Webcast
October 3, 2011

What Are Analytics?

Optimization “What’s the best that can happen?”

Predictive Modeling/ “What will happen next?” Predictive and
Forecasting
Prescriptive
Randomized Testing “What happens if we try this?”
Analytics
Degree Statistical analysis “Why is this happening?”
(the “so what”)
of Intelligence
Alerts “What actions are needed?”

Query/drill down “What exactly is the problem?” Descriptive
Analytics
Ad hoc reports “How many, how often, where?”
(the “what”)
Standard Reports “What happened?”

4 | 2011 © All Rights Reserved. Thomas H. Davenport – Predictive Analytics

2

Types of Analytics

Timeframe
Past Present Future
Information

What is happening
What happened? What will happen?
now?
(Reporting) (Prediction)
Content Type

(Alerts)

What’s the best
How and why did It What’s the next best
that can happen?
Insight

happen? action?
(Optimization/simulatio
(Modeling, testing) (Recommendation)
n)


Applications of Predictive
Analytics

What offers will customers
accept?
What price will they pay?
Which recruit will become a
high performer?
How likely is it that this
customer will leave?
Which supplier is most likely to
fail to deliver?


3

Levels of Analytical Capability

Stage 5
Analytical
Competitors

Stage 4
Analytical Companies

Stage 3
Analytical Aspirations

Stage 2
Localized Analytics

Stage 1
Analytically Impaired

Thomas H. Davenport – Predictive Analytics
7

Masters of Prediction

Marriott — optimal pricing
Nextel—customer attrition
Cisco—forecasting
Tesco—offers
eBay—web site testing
Netflix—movies you’ll like
Zappos—shoes you’ll like
Google—page rank, advertising, HR


4

The Analytical DELTA

Data . . . . . . . . . . breadth, integration, quality, technology
Enterprise . . . . . . . . . .approach to managing analytics
Leadership . . . . . . . . . . . . . . . passion and commitment
Targets . . . . . . . . . . . . . first deep, then broad
Analysts . . . . . professionals and amateurs


Data

The prerequisite for everything analytical
Clean, common, integrated
Accessible in a warehouse
Measuring something new and important


5

New Metrics / Data

Wine Chemistry Defensive moves Smile Frequency


Some Current Data and
Technology Dilemmas

Analytics on premise, private cloud, public cloud?
Different tools for “big data”?
Is a data warehouse still necessary?
Will “analytical apps” take off?
How can analytics be embedded?


6

The Changing World of Analytics

Analyst
Multi- Old BI Sandbox
Purpose
Application Breadth

Single- Analytical Embedded
Purpose Apps Analytics

Business Professional
Users Analysts
Primary Users


Some Actual Analytical Apps

Spend analysis in life sciences
Aftermarket services revenue growth for
equipment manufacturers
Analyzing mortgage portfolios
Financial planning and modeling in the public
sector
Enterprise risk and solvency management for
insurance
Contract compliance in transportation
Nursing productivity in health care
Field sales hiring analysis in pharma
Employee attrition analysis in telecom
Employee satisfaction and store performance
analysis in retail


7

Linking Data and Decisions


Embedding Analytics in Processes

Defection Risk
Creation
Purchase Order “What is the customer status?”

Creation
Request Global ATP Inventory Forecast
Sales Order “Will this be back in inventory?”
Global ATP Check
Fulfillment Request

Creation &
Release Delivery
Request
Returns per Customer
“What is the customer history?” CLTV
“Does this order justify extra
Delivery
Execution efforts?”

Update
Update
Releases ASN Inventory
Inventory
Accounting

Delivery Performance
Receives ASN “How effective is our fulfillment
process?”

Source: SAP AG 2006


8

Enterprise

If you’re competing on analytics, it doesn’t make
sense to manage them locally
No fiefdoms of data, technology, or organization
A centralized organization or CoE is increasingly
common
P&G, Caesars, Walmart, etc.


Under Enterprise Management

=
Predictive + +
HR analytics Actuarial + Enterprise
Analytics!

Web analytics + Marketing + Supply chain/OR


9

Leadership

CEOs—Google, Netflix, Capital One
CFOs—Caesars, Humana
CIOs—P&G, Schneider
COOs—Ebay, Chicos


The Best Targets…

Support a key strategic capability
Engage top management commitment
Create momentum for analytics across the
enterprise
Have ambitious, yet pragmatic scope
Are data rich — or have the potential to be
Dramatically improve effectiveness of asset and/or
labor-intensive activities
Have broad implications across functions,
processes, geographies, or business units


10

Are You Ready for Prediction/Optimization?

Real-Time Optimization
Optimal
response
embedded in
real-time
process Institutional Action
Prediction and
differentiated
action
Predictive Action embedded in
process
Predictions of
response by
target/ segment
Differentiated Action
Different
approaches for
different
targets/
Key Targets/Segments segments
Key targets and
segments
defined
Data in Order
Well-defined,
common, clean,
and integrated
data


Analysts

Analytical Champions--Own
1%
Lead analytical initiatives
“Data Scientists”—Own/Rent
5-10% Can create new algorithms

Analytical Semi-Professionals—Own/Rent
15-20% Can use visual and basic statistical tools,
create simple models

Analytical Amateurs--Own
Can use spreadsheets, use
70-80% analytical transactions

* percentages will vary based upon industry and strategy


11

Roles for IT and CIOs in All This
Restructure the entire IT organization to emphasize
decision-making
e.g., P&G’s “Information and Decision Solutions”
Establish a COE, competency center, or consulting
group around analysis and decisions
e.g, Kimberly-Clark’s BICC
Include analytics and decision processes in the
broader information provision process
E.g., Cisco Advanced Services “Production Analytics”


Keep in Mind

► Five levels, five factors for building
predictive analytical capability
► Data and leadership are the most
important prerequisites
► Make sure your targets are strategic
► Tie all your predictive analytics work to
specific decisions
► This is not business as usual—there is a
historic opportunity to transform your
industry!


12

Questions?

To ask a question … click on the “question icon” in
the upper-left corner of your screen.

Type your question and name, and additional
information if you wish, and click on the send
button.

Brought to you by

Thank you for participating

This presentation was made possible by the
generous support of SAP.

Learn more at SAP.com/PredictiveAnalytics

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Davenport Webinar Predictive Analytics

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