Revenue Management and the Evolution of Decision Making

1
Revenue Management andthe Evolution of Decision
Making

• MIST 8990
• Prof. Jay E. Aronson
• University of Georgia
• Terry College of Business
• February 20, 2004

Presentation by Robert G. Cross
2
Overview

• Using knowledge rather than supposition in
  decision making
• The evolution of the decision making process in
  business
• Humans vs. machines in decision making

3
The End of Marketing as We Know It

• Marketing is a science. If you think that
  marketing is just making eye-catching
  commercialsyoure fried.
• Sergio Zyman

4
Technology as aKnowledge Enabler
5
Revenue Management Core Concept 5 Make
Decisions Based on Knowledge, not Supposition
6
Rocket Science 101
ORBIT
Velocity/Altitude

TIME
7
Revenue Optimization
Revenue

Time
8
The Evolution of Decision Making
Data
9
The Evolution of Decision Making

• Raw facts
• Observations
• Assumption of accuracy
• Volume of data grows exponentially

Data
10
The Evolution of Decision Making
Information
Data
11
The Evolution of Decision Making

• Data sorted and reorganized
• Relationships identified
• An interpretation of data
• A better basis for decision making

Information
12
The Evolution of Decision Making
Knowledge
Information
Data
13
The Evolution of Decision Making

• Evaluation of information
• Construction of concepts
• Definition of principles
• Prediction of future

Knowledge
14
The Evolution of Decision Making
Wisdom
Knowledge
Information
Data
15
The Evolution of Decision Making

• Accumulation of knowledge
• Understand situations
• Evaluate alternatives
• Judge consequences of actions
• Optimize decision

Wisdom
16
Steps in the Decision Making Process
Wisdom
Knowledge
Information
Data
17
Revenue Management-Process Flow
?
(Information)
MarketSegmentation
(Data)
(Knowledge)
Customer Demand Data
Forecasters
!
Constraints

Optimization
Market
(Wisdom)
Price and Availability Recommendations
18
Human Biases in Decision Making

• Transmission Error
• Gamblers Fallacy
• Data Presentation
• Availability Bias
• Perceptual Bias

19
Human Biases

• Transmission Error

20
Human Biases

• Gamblers Fallacy

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Human Bias

• DATA PRESENTATION
• 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8
• vs.
• 8 x 7 x 6 x 5 x 4 x 3 x 2 x 1

22
Human Biases

• Availability Bias

23
Perceptual Bias
We see what we want to see
24
Perceptual Bias
25
Perceptual Bias
26
Perceptual Bias
27
Perceptual Bias
28
Perceptual Bias
29
Perceptual Bias
30
Humans vis-à-vis Machines
HUMANS
COMPUTERS

• Logical
• Mindless
• Physical Limits
• Indifferent
• Tireless

• Intuitive
• Creative
• Cognitive Limits
• Biased
• Fatigue-Prone

31
Review

• Knowledge is the ability to learn from the past
  and apply lessons to the future
• Knowledge is the basis of accurate decisions and
  wealth creation
• Decision support systems aid in the creation of
  knowledge from information
• There are appropriate roles for both humans and
  machines in decision making

32
Revenue Management andthe Evolution of Decision
Making

• MIST 8990
• Prof. Jay E. Aronson
• University of Georgia
• Terry College of Business
• February 20, 2004

Presentation by Robert G. Cross