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ME Basics–1
Marketing Science 1
University of Tsukuba,
Grad. Sch. of Sys. and Info. Eng.
Instructor: Fumiyo Kondo
Room: 3F1131
ME Basics–2
Introduction toMarketing Science
Course description and structure
What is marketing engineering?
Why learn marketing engineering?
Introduction to software
Introduce Conglom Promotions case
ME Basics–3
Marketing Engineering Basics
Introduction
Course Overview
Software Review
ME Basics–4
How Does This Course Differ from Other Marketing Courses?
Integrates marketing concepts and practice.
Emphasizes “learning by doing”.
Provides software tools to apply marketing concepts to real decision situations.
ME Basics–5
Transition of Marketing Definition
1. Age of No Need for Marketing2. Mass Marketing that target all consumers3. ( Traditional ( Segmentation Marketing Concept of Exchange (Kotler)4. One-to-One Marketing Concept of Relationship
ME Basics–6
Definition of Segmentation Marketing
Concept of Exchange by Kotler ( 1976 ) Societal and managerial process.. Exchange ..
Needs and wants of individuals and organizations
Marketing Management
Facilitates proactively the exchange process viewed as
a management philosophy for desirable exchanges
Ability to understand customers and Markets
ME Basics–7
Recent Definition of Marketing by AMA (American Marketing
Association)
Marketing is
an organizational function and
a set of processes
for creating, communicating, and delivering value to customers and
for managing customer relationships in ways that benefit the organization and its stakeholders.
ME Basics–8
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–9
Trends FavoringMarketing 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–10
Managers’ Typical Approachin Marketing Decision Making
Rely on experience and wisdom
… based on mental models
Use practice standards
Alternative approach
… based on decision models
This course uses decision models
ME Basics–11
Strength and Weakness of Mental models
Psychologically comfortable with the decisions
Prone to systematic errors
Experience can be confounded with
responsibility biases, for example,
Sales managers ... lower advertising budgets &
higher expenditures on personal selling
Advertising managers ... larger advertising budget
ME Basics–12
Strength and Weakness of Practice of Standards
Good on average
Ignore idiosyncratic elements in decision context
e.g., a new competitor enters the market
with an aggressive advertising program,
resulting in a decrease in the firm’s sales.
A fixed advertising-to-sales-ratio based on practice of
stabdards would prescribe a decrease in advertising.
Other reasonable mental model would suggest some
form of retaliation based on increased advertising.
ME Basics–13
Conceptual Marketing vs. Marketing Engineering
Third approach …
build a spreadsheet decision model
called marketing engineering (ME)
First approach (mental model)
referred to as conceptual marketing
ME complements conceptual marketing.
ME Basics–14
Marketing Engineering
Marketing Environment
MarketingEngineering Data
Information
Insights
Decisions
Implementation
Automatic scanning, data entry,subjective interpretation
Financial, human, and otherorganizational resources
Judgment under uncertainty,eg., modeling, communication,introspection
Decision model; mental model
Database management, e.g..,selection, sorting, summarization,report generation
ME Basics–15
Data are facts, beliefs, or observations used in making decisions.
A common misconception is that decision models require objective data.
Information refers to summarized or categorized data.
Insights provide meaning to the data or information, and they help manager gain a better understanding of the decision situation.
A decision is a judgement favoring a particular insight as offering the most plausible explanation or favoring a particular course of action. (Decision provides purpose to information.)
Implimentation is the set of actions the manager or the organization takes to commit resources toward physically realizing a decision.
ME Basics–16
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–17
Stylized
Models do not capture reality fully,
but focus only on some aspects.
ME Basics–18
Representation
A model is only a convenient analogy
that may bear little resemblance to the
physical characteristics of the reality
it is trying to capture.
ME Basics–19
Specific purpose
People develop models with a specific purpose in mind.
The purpose of a marketing model could be to understand or influence
certain types of behavior in the market place(e.g. repeat purchase of the firm’s product)
ME Basics–20
An Example of a Verbal Model- Example of Diffusion 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–21
Boxes and Arrows Model
Fixed Population Size
Imitators
Timing of Purchases byInnovators
Timing of Purchases byImitators
Pattern of Sales Growthof New Product
Innovators
InfluenceImitators
Innovators
ME Basics–22
Graphical Model
Cumulative Salesof a
Product
Time
FixedPopulation Size
ME Basics–23
New York City’s Weather
ME Basics–24
Mathematical Model
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
dxt
dt= (a + bxt)(N – xt)
ME Basics–25
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–26
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–27
Are ‘Models’ the Whole Answer? No!
The widespread availability of statistical packages has put mathematical bazookas in the hands of those who would bedangerous 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–28
Models vs Intuition/Judgments
Types of SubjectiveObjective
Judgments Experts Mental Decision DecisionHad to Make Model Model Model
Academic performance of graduate students 0.19 0.25 0.54
Life expectancy of cancer patients –0.01 0.13 0.35
Changes in stock prices 0.23 0.29 0.80
Mental illness using personality tests 0.28 0.31 0.46
Grades and attitudes in psychology course 0.48 0.56 0.62
Business failures using financial ratios 0.50 0.53 0.67
Students’ rating of teaching effectiveness 0.35 0.56 0.91
Performance of life insurance salesman 0.13 0.14 0.43
IQ scores using Roschach tests 0.47 0.51 0.54
Mean (across many studies) 0.33 0.39 0.64
ME Basics–29
Applicant Profile(Academic performance of graduate students)
Under-Appli- Personal Selectivity graduate College Work GMAT GMAT cant Essay of Under- Major Grade Exper- Verbal Quanti-
graduate Institution Avg. ience tative
1 poor highest science 2.50 10 98% 60%
2 excellent above avg. business 3.82 0 70% 80%
3 average 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–30
Small Models Example:Trial/Repeat Model
Share =% Aware ×
% Available | Aware ×
% Try | Aware, Available ×
% Repeat | Try, Aware, Available × Usage Rate
ME Basics–31
Target Population
Aware?
Available?
Try?
Repeat?
Market Share = ?
50%
80%
40%
50%
Trial/Repeat Model
ME Basics–32
Repeat
Trial
low
hi
lowhi
Model Diagnostics
ME Basics–33
Trial Dynamics
% Population Trying (Trial)
100%
Time
You never geteveryone to try
ME Basics–34
% Repeaters Among Triers
(Repeat)
100%
Time
Note—late triers often do not become
regular users
Repeat Dynamics
ME Basics–35
Fiona ‘the brand manager’ gets promoted
Steve, her replacement, gets fired
John, ‘the caretaker’, takes over
Share =(Trial Repeat)
100%
= Share Dynamics!
Time
ME Basics–36
New Phenomenon:Retail Outlet Management
Sales/Outlet
# Company Outlets in Market
What People Observed
What People Thought
ME Basics–37
Why?
Typical outlet-share/market-share relationship
MarketShare
Outlet Share
20 40 60 80 100
20
40
60
80
100
Market Share= Outlet Share
ME Basics–38
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–39
Model Benefits
Small models can offer insight
Models can identify phenomena
Operational models can provide long-term benefits
ME Basics–40
More on Benefits ofDecision 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–41
Value of Models
ME Basics–42
Why Don’t More ManagersUse 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–43
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–44
We Focus on End-User Models
* Low for one-time studiesHigh for models in continuous use
End-User Models High-End Models
Scale of problem Small/Medium Small/Large
Time Availability Short Long(for setting up model)
Costs/Benefits Low/Medium High
User Training Moderate/High Low/Moderate
Technical Skills Low/Moderate High
Recurrence of problem Low Low or High*
ME Basics–45
Marketing Engineering Software
Excel Models Non-Excel ModelsNon-Excel Models by Commercial Vendors
ME Basics–46
Marketing Engineering Software
Excel Models
AdbudgAdvisorAssessorCallplanChoice-based segmentationCompetitive advertisingCompetitive biddingConglomerate, Inc.
promotional analysis GE: Portfolio analysis
Generalized Bass ModelLearning curve pricingPIMS:Strategy modelPromotional spending AnalysisSales resource allocation
modelValue-in-use pricingVisual response modelingYield management for
hotels
ME Basics–47
Marketing Engineering Software
Non-Excel Models
ADCAD: Ad copy designCluster AnalysisConjoint AnalysisMultinomial logit analysisPositioning Analysis
Non-Excel Models by Commercial Vendors
Analytic hierarchyprocess
Decision tree analysisGeodemographic site
planningNeural net for forecasting
ME Basics–48