Predicting the Future

Copyright 2003 Whittaker Associates, IncCopyright 2007 Whittaker Associates, Inc
Predicting the Future
Presented by:
Dean Whittaker, CEcD
Economic Development Institute
Kansas City 2010

Learning Objectives
 Broaden your understanding of the use of
Business Intelligence in economic
development.
 Be capable of applying new information
resources and tools to predict future events.
 Share knowledge among participants.

Economic Development –
Making the Pie Bigger

Economic Development Context
 Changing nature of work – work moves,
workers stay put
 Globalized, automated, outsourced, and off-
shored
 Increasing concentration of wealth in the
hands of a few

The History of Prediction
 Prophets
 Astrologers
 Soothsayers
 Fortune Tellers
 Palm Readers
 Economic Developers

Business Intelligence
 Using information to make better decisions
 Reduce risk and predict outcome
 Focus your effort – “biggest bang for the buck”
 Brains vs. Muscle
 Abundance of information, scarcity of attention

The Information Cycle

The Role of Information
 Reduce risk
 Increase certainty
 Predict outcome

Seeing the Future
 Look to the past
 Repetitive pattern
 Cycles
 1,000,000 year
 1,000 year
 300 year
 100 year

Measure Change Over Time to
Predict the Future
 Barometric Pressure
 Length of Daylight
 Solar Flares
 Waves
 Light
 Sound

Time
Event
Linear Thinking in a Cyclical World

Time
Frequency
The Long Tails of the Black Swan

Exercise I: Build a Community
Economic Barometer
 Identify five “real time” factors that could
be measured to create a real time view of
the local economy
 Identify five “leading” indicators that
would forecast future economic
performance.

Economic Barometer
 Measuring the Economy
 Usage of Resources
 Electricity
 Gas
 People
 Raw materials
 Transportation
 Movement of goods and people

Types of Cycles
 Tipping Point
 Precipitating events
 Outliers
 The long tail
 Cycles
 Repeating pattern

Predictive Analytics
 What is it?
 Why should you care?

 Predictive analytics is an area of statistical
analysis that deals with extracting information
from data and using it to predict future trends and
behavior patterns. The core of predictive analytics
relies on capturing relationships between
explanatory variables and the predicted variables
from past occurrences, and exploiting it to predict
future outcomes.
Source: http://en.wikipedia.org/wiki/Predictive_analytics

 Statistics
 Correlation vs. causal
 Pattern recognition
 Knowledge mapping

Ranking Predictive Model
 Use of Weighted Matrix
 Control Group
 Variables
 Weighting

Examples of Predictive Models
 Corporate Behavior
 Sporting Events
 Return on Investment
 Loan Repayment

Business Intelligence
 Predicting Corporate Behavior
 Behavior linked to history
 Accuracy - function of data available
 Giving Information Meaning
 Data…information…knowledge…action
 Assumptions
 Rational Behavior?
 Cyclical

Economic Cycle
 Boom/Bust
 36,000 year economic cycle
 Generational
 Baby Boomers
 Generation X, Y, and Z
 Corporate life cycle
 Product life cycle

Product Life Cycle

Predicting Corporate Behavior
 Probability to close or have major layoff
 Probability to relocate, expand, or
consolidate facilities

Exercise II: Business at Risk
An Early Warning System
 Identify five “leading” factors in their
business environment and five within the
company that would indicate a company
is at risk of closing or laying off a
significant number of employees.
 Weight each of the factors by its relative
importance in being predictive.

Exercise III:
Targeting Businesses for Attraction
 Identify five “leading” factors in their
business environment and five within the
company that would indicate a company
that is likely to relocate or expand
 Weight each of the factors by its relative
importance in being predictive.

Why Target Companies?
 Focus energy and effort
 Limited resources of time and money
 Maximize results
 Brains vs. muscle

Why do Companies Relocate
or Expand?
 Internal changes
 Ownership
 Leadership
 Others
 External Changes
 Business Environment
 Regulatory Environment
 Others

Predicting Corporate Behavior
 Key Drivers
 Change
 Leadership
 Ownership
 Product/Service
 Client Preference
 Metrics
 Sales
 Employment

How to Target Companies
 Creating a target company profile
 Target industry
 Size
 Location
 Corporate Changes/Events
 Others

Where to Find Company Information?
 Primary Company Information
 Secondary Information

Google
 http://www.google.com
 http://www.new.google.com
 http://finance.google.com/finance
 http://labs.google.com/
 http://google.com/trends

People
 Linkedin.com
 Facebook.com
 Others

Places
 www.ZipSkinny.com – a zip code-based
demographic comparison tool
 www.earth.google.com – a bird’s eye view
of earth and beyond
 ZoomProspector.com – a geographic
information site selection tool

Fee-Based Internet Resources
 www.Hoovers.com - excellent source of
company information
 www.OneSource.com – broad multiple
sources of news and company information
 www.Nexis.com - news sources for events

Web 2.0 – The New Media
 Social Networks
 www.LinkedIn.com
 www.Facebook.com
 www.MySpace.com
 Streaming Video
 www.YouTube.com
 Podcasting

Websites of Interest
 www.factcheck.org
 www.wikipedia.com
 www.whittakerassociates.com
 www.ceoexpress.com

Summary
 Context
 Role of Information
 Information Cycle
 Predictive Analytics
 Predicting Behavior
 Weighted Matrix
 For More Information

Thank you for your participation
Dean Whittaker
Dean@whittakerassociates.com
616-786-2500
www.whittakerassociates.com

Predicting the Future

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