Marketing Science 1

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Marketing Science 1

• University of Tsukuba,
• Grad. Sch. of Sys. and Info. Eng.
• Instructor Fumiyo Kondo
• Room 3F1131
• kondo_at_sk.tsukuba.ac.jp

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Introduction to Marketing Science

• Course description and structure
• What is marketing engineering?
• Why learn marketing engineering?
• Introduction to software
• Introduce Conglom Promotions case

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Marketing Engineering Basics

• Introduction
• Course Overview
• Software Review

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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.

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Transition of Marketing Definition

• Age of No Need for Marketing
• Mass Marketing that target all consumers
• (Traditional)Segmentation Marketing
• Concept of Exchange (Kotler)
• One-to-One Marketing
• Concept of Relationship

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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

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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.

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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.

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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.

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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

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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

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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 firms
  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.

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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.

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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
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• 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.

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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

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Stylized

• Models do not capture reality fully,
• but focus only on some aspects.

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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.

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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 firms product)

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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.

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Boxes and Arrows Model
Fixed Population Size
Imitators
Innovators
Innovators Influence Imitators
Timing of Purchases by Innovators
Timing of Purchases by Imitators
Pattern of Sales Growth of New Product
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Graphical Model
Fixed Population Size
Cumulative Sales of a Product
Time
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New York Citys Weather
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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

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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.

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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!
• LMAs 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) gt 50 and
  LMA says success, should you introduce?

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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

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Models vs Intuition/Judgments

• Types of Subjective Objective
• Judgments Experts Mental Decision Decision
• Had 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

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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

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Small Models ExampleTrial/Repeat Model

• Share Aware
• Available Aware
• Try Aware, Available
• Repeat Try, Aware, Available Usage Rate

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Trial/Repeat Model
Target Population
Aware?
Available?
Try?
Repeat?
Market Share

?
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Model Diagnostics
L
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Trial Dynamics
You never get everyone to try
100
Population Trying (Trial)
Time
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Repeat Dynamics
100
Notelate triers often do not become regular users
Repeaters Among Triers (Repeat)
Time
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Share Dynamics!
Fiona the brand manager gets promoted
100
Steve, her replacement, gets fired
Share (Trial Repeat)
John, the caretaker, takes over
Time
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New PhenomenonRetail Outlet Management
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Why?
100
80
Market Share Outlet Share
60
Market Share
40
20
20
40
60
80
100
Outlet Share
Typical outlet-share/market-share relationship
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Retail Building Implications

• 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

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Model Benefits

• Small models can offer insight
• Models can identify phenomena
• Operational models can provide long-term benefits

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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.

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Value of Models
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Why Dont 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 havent been exposed to Marketing
  Engineering.
• All models are wrong. Some are useful!

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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).

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We Focus on End-User Models
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
Low for one-time studies High for models in
continuous use
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Marketing Engineering Software
Non-Excel Models by Commercial Vendors
Excel Models
Non-Excel Models
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Marketing Engineering Software

• Excel Models
• AdbudgAdvisorAssessorCallplanChoice-based
  segmentationCompetitive advertisingCompetitive
  biddingConglomerate, Inc. promotional analysis
  GE Portfolio analysis

• Generalized Bass ModelLearning curve
  pricingPIMSStrategy modelPromotional spending
  AnalysisSales resource allocation modelValue-in
  -use pricingVisual response modelingYield
  management for hotels

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Marketing Engineering Software

• Non-Excel Models
• ADCAD Ad copy designCluster AnalysisConjoint
  AnalysisMultinomial logit analysisPositioning
  Analysis

• Non-Excel Models by Commercial Vendors
• Analytic hierarchy processDecision tree
  analysisGeodemographic site planningNeural net
  for forecasting

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