ME Basics – 1 Marketing Science 1 University of Tsukuba, Grad. Sch. of Sys. and Info. Eng....

ME Basics–1

Marketing Science 1

University of Tsukuba,

Grad. Sch. of Sys. and Info. Eng.

Instructor: Fumiyo Kondo

Room: 3F1131

kondo@sk.tsukuba.ac.jp

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