Marketing Engineering Basics
 Introduction
 Course Overview
 Software Review
ME Basics–1
Daily Marketing Decisions
ME Basics–2
How Do Managers Make
Marketing Decisions?








Intuition/judgment?
Strategic rationale?
Best practice benchmarks?
Internet search?
Consultant/Market Research results?
Sales force guesses?
Use decision models?
All of the above?
ME Basics–3
Introducing . . .
Marketing Engineering
 Course description and structure
 What is marketing engineering?
 Why learn marketing engineering?
 Introduction to software
 Introduce Conglom Promotions case
ME Basics–4
What’s Different About This
Course?

Integrates marketing concepts with practice.

Emphasizes “learning by doing.”

It is a capstone course.

Provides you software tools to help you apply marketing
concepts to real decision situations (even after you graduate!).
ME Basics–5
Takeaways

Gain an appreciation for the value of systematic marketing decision
making.

Learn the language of high-powered marketing consultants -- i.e., how
to put together analyses that tell a coherent story.

Understand how to critically evaluate analytical results presented to
you by others -- i.e., become a good customer of analytical models.

Learn how successful companies have integrated marketing
engineering within their organizations.

Develop skills to become a marketing engineer (i.e., to structure
marketing problems and issues analytically using decision models).
ME Basics–6
Marketing Engineering
Marketing engineering is the art and
science of developing and using interactive,
customizable, computer-decision models
for analyzing, planning, and implementing
marketing tactics and strategies.
ME Basics–7
Marketing Engineering
Marketing Environment
Automatic scanning, data entry,
subjective interpretation
Marketing
Engineering
Data
Database management, e.g..,
selection, sorting, summarization,
report generation
Information
Decision model; mental model
Insights
Judgment under uncertainty,
eg., modeling, communication,
introspection
Decisions
Financial, human, and other
organizational resources
Implementation
ME Basics–8
Trends Favoring
Marketing Engineering
 High-powered personal computers
connected to networks are becoming
ubiquitous.
 The volume of marketing data is exploding.
 Firms are re-engineering marketing for the
information age.
ME Basics–9
What is a Model?
A model is a stylized representation of
reality that is easier to deal with and explore
for a specific purpose than reality itself.
We will use the following types of models:

Verbal

Box and Arrow

Mathematical

Graphical
ME Basics–10
An Example of a Verbal Model
Sales of a new product often start slowly as
“innovators” in the population adopt the product.
The innovators influence “imitators,” leading to
accelerated sales growth. As more people in the
population purchase the product, sales continue to
increase but sales growth slows down.
ME Basics–11
Boxes and Arrows Model
Fixed
Population Size
Innovators
Imitators
Innovators
Influence
Imitators
Timing of Purchases by
Innovators
Timing of Purchases by
Imitators
Pattern of Sales Growth
of New Product
ME Basics–12
Graphical Model
Fixed
Population Size
Cumulative
Sales
of a
Product
Time
ME Basics–13
New York City’s Weather
ME Basics–14
Mathematical Model
dxt = (a + bx )(N – x )
t
t
dt
where:
xt = Total number of people who have adopted
product by time t
N = Population size
a,b = Constants to be determined. The actual path of
the curve will depend on these constants
ME Basics–15
Are Models Valuable?
Belief:
‘No mechanical prediction method can possibly
capture the complicated cues and patterns humans
use for prediction.’
Hard Fact:
A host of studies in medical diagnosis, loan
granting, auditing and production scheduling have
shown that even simple models out-perform expert
judgement.
Example:
Bowman and Kunreuther showed that simple
models based on managers’ past behaviour, (in
terms of production scheduling and inventory
decisions) out-perform the managers themselves in
the future.
ME Basics–16
How Good are You at Interpreting
Market Research Information?
Your firm has had the following record over the last 5 years:
85 of 100 new product developments failed.
Lilien Modelling Associates (LMA) did a $50,000 study on your new
product, Sheila Aftershave, and reports ‘Success’!
LMA’s record is pretty good: of the 125 field studies it has done, it had
80/100 accurate ‘success’ calls (80%)
20/25 accurate ‘failure’ calls (‘I told you so’) also 80%.
If you should introduce Sheila if P(S) > 50% and LMA says “success”,
should you introduce?
ME Basics–17
Introduce if P(S) > 50%?
S
F
G
P
=
=
=
=
Success (True)
Failure (True)
Good market research result
Poor market research result.
P(G|S)
P(P|F)
P(S)
P(F)
=
=
=
=
0.80
0.80
0.15
0.85
P(S|G) =
=
(80/100)
(20/25)
P(G|S) P(S)
P(G|S) P(S) + P(G|F) P(F)
0.80  0.15
0.80  0.15 + 0.20  0.85
=
41.3%
ME Basics–18
Are ‘Models’ the Whole Answer?
No!
The widespread availability of statistical packages has put
mathematical bazookas in the hands of those who would be
dangerous with an abacus.
—Barnett
To evaluate any decision aid, you need a proper baseline.
1. Intuitive judgement does not have an impressive track record.
2. When driving at night with your headlights on you do not
necessarily see too well. But turning them off will not improve
the situation.
3. ‘Decision aids do not guarantee perfect decisions but when
appropriately used they will yield better decisions on average
than intuition.’
—Hogarth, p.199
ME Basics–19
Models vs Intuition/Judgments
Types of
Judgments Experts
Had to Make
Mental
Model
Subjective
Decision
Model
Objective
Decision
Model
Academic performance of graduate students
Life expectancy of cancer patients
Changes in stock prices
Mental illness using personality tests
Grades and attitudes in psychology course
Business failures using financial ratios
Students’ rating of teaching effectiveness
Performance of life insurance salesman
IQ scores using Roschach tests
0.19
–0.01
0.23
0.28
0.48
0.50
0.35
0.13
0.47
0.25
0.13
0.29
0.31
0.56
0.53
0.56
0.14
0.51
0.54
0.35
0.80
0.46
0.62
0.67
0.91
0.43
0.54
Mean (across many studies)
0.33
0.39
0.64
ME Basics–20
Applicant Profile
(Academic performance of graduate students)
Personal
Essay
Selectivity
of Undergraduate
Undergraduate
Major
Institution
1
poor
highest
2
excellent
3
average
•
•
•
•
•
•
•
•
•
•
Applicant
College
Grade
Avg.
Work
Experience
GMAT
Verbal
GMAT
Quantitative
science
2.50
10
98%
60%
above avg.
business
3.82
0
70%
80%
below avg.
other
2.96
15
90%
80%
•
•
•
•
•
•
117
weak
least
business
3.10
100
98%
99%
118
strong
above avg
other
3.44
60
68%
67%
119
excellent
highest
science
2.16
5
85%
25%
120
strong
not very
business
3.98
12
30%
58%
ME Basics–21
Small Models Example:
Trial/Repeat Model
Share = % Aware 
% Available | Aware 
% Try | Aware, Available 
% Repeat | Try, Aware, Available  Usage Rate
ME Basics–22
Trial/Repeat Model
Target Population
Aware?
50%
Available?
80%
Try?
40%
Repeat?
50%
Market Share
=
?
ME Basics–23
Model Diagnostics
Trial
hi
hi
Repeat
low
J
low
L
ME Basics–24
Trial Dynamics
You never get
everyone to try
100%
% Population
Trying (Trial)
Time
ME Basics–25
 Repeat Dynamics
100%
Note—late triers
often do not become
regular users
% Repeaters
Among
Triers
(Repeat)
Time
ME Basics–26
= Share Dynamics!
100%
Fiona ‘the
brand manager’
gets promoted
Steve, her
replacement,
gets fired
Share =
(Trial  Repeat)
John, ‘the
caretaker’,
takes over
Time
ME Basics–27
New Phenomenon:
Retail Outlet Management
What People Observed
Sales/Outlet
What People Thought
# Company Outlets in Market
ME Basics–28
Why?
100
80
Market
Share
Market Share
= Outlet Share
60
40
20
20
40
60
80
100
Outlet Share
Typical outlet-share/market-share relationship
ME Basics–29
Retail Building Implications
1. Market Share = Outlet Share  Use
incremental analysis and spread resources
evenly.
But
2. Market Share/Outlet Share is S-shaped 

Concentrate in few areas

Invest or divest
ME Basics–30
Model Benefits
 Small models can offer insight
 Models can identify phenomena
 Operational models can provide
long-term benefits
ME Basics–31
More on Benefits of
Decision Models
 Improves consistency of decisions.
 Allows you to explore more decision options.
 Allows you to assess the relative impact of
variables.
 Facilitates group decision making.
 (Most important) It updates your subjective
mental model.
ME Basics–32
Why Don’t More Managers
Use Decision Models?
 Mental models are often good enough.
 Models are incomplete.
 Managers cannot typically observe the opportunity
costs of their decisions.
 Models require precision.
 Models emphasize analysis; Managers prefer actions.
 They haven’t been exposed to Marketing Engineering.
All models are wrong. Some are useful!
ME Basics–33
Some Course Objectives
 Gain an appreciation for the value of systematic
marketing decision making.
 Learn the language and tools of marketing consultants.
 Learn how successful companies have integrated
marketing engineering within their organizations.
 Understand how to critically evaluate analytical results
presented to you.
 Develop skills to become a marketing engineer (ie, to
structure marketing problems and issues analytically
using decision models).
ME Basics–34
We Focus on End-User Models
End-User Models
High-End Models
Scale of problem
Small/Medium
Small/Large
Time Availability
Short
Long
Costs/Benefits
Low/Medium
High
User Training
Moderate/High
Low/Moderate
Technical Skills
Low/Moderate
High
Recurrence of problem
Low
Low or High*
(for setting up model)
* Low for one-time studies
High for models in continuous use
ME Basics–35
Marketing Engineering Software
Excel Models
Non-Excel Models
Non-Excel Models by
Commercial Vendors
ME Basics–36
Marketing Engineering Software

Excel Models
Adbudg
Advisor
Assessor
Callplan
Choice-based segmentation
Competitive advertising
Competitive bidding
Conglomerate, Inc.
promotional analysis
GE: Portfolio analysis
Generalized Bass Model
Learning curve pricing
PIMS:Strategy model
Promotional spending
Analysis
Sales resource allocation
model
Value-in-use pricing
Visual response modeling
Yield management for
hotels
ME Basics–37
Marketing Engineering Software

Non-Excel Models
ADCAD: Ad copy design
Cluster Analysis
Conjoint Analysis
Multinomial logit analysis
Positioning Analysis

Non-Excel Models by
Commercial Vendors
Analytic hierarchy
process
Decision tree analysis
Geodemographic site
planning
Neural net for forecasting
ME Basics–38