ME Basics–1

Marketing Engineering Basics

Introduction

Course Overview

Software Review

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Daily Marketing Decisions

Segmentation

Targeting

Positioning

BudgetsMarketing Mix

Market sizeMarket share

Campaign effectiveness

Pricing structure

Portfolio

Man

agem

ent

Advertising design

Sales channels

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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?

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Introducing . . .Marketing Engineering

Course description and structure

What is marketing engineering?

Why learn marketing engineering?

Introduction to software

Introduce Conglom Promotions case

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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!).

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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).

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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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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

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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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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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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.

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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

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Graphical Model

Cumulative Salesof a

Product

Time

FixedPopulation Size

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New York City’s 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

dxt

dt= (a + bxt)(N – xt)

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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’!

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?

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Introduce if P(S) > 50%?

S = Success (True)F = Failure (True)G = Good market research resultP = Poor market research result.

P(G|S) = 0.80 (80/100)P(P|F) = 0.80 (20/25)

P(S) = 0.15P(F) = 0.85

P(S|G) = P(G|S) P(S) P(G|S) P(S) + P(G|F) P(F)

= 0.80 0.15 = 41.3%0.80 0.15 + 0.20 0.85

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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 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

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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

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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 Example:Trial/Repeat Model

Share =% Aware

% Available | Aware

% Try | Aware, Available

% Repeat | Try, Aware, Available Usage Rate

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Target Population

Aware?

Available?

Try?

Repeat?

Market Share = ?

50%

80%

40%

50%

Trial/Repeat Model

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Repeat

Trial

low

hi

lowhi

Model Diagnostics

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Trial Dynamics

% Population Trying (Trial)

100%

Time

You never geteveryone to try

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% Repeaters Among Triers

(Repeat)

100%

Time

Note—late triers often do not become

regular users

Repeat Dynamics

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Fiona ‘the brand manager’ gets promoted

Steve, her replacement,

gets fired

John, ‘the caretaker’, takes over

Share =(Trial Repeat)

100%

= Share Dynamics!

Time

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New Phenomenon:Retail Outlet Management

Sales/Outlet

# Company Outlets in Market

What People Observed

What People Thought

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Why?

Typical outlet-share/market-share relationship

MarketShare

Outlet Share

20 40 60 80 100

20

40

60

80

100

Market Share= Outlet Share

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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

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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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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!

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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

* 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*

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Marketing Engineering Software

Excel Models Non-Excel ModelsNon-Excel Models by Commercial Vendors

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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

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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 hierarchyprocess

Decision tree analysisGeodemographic site

planningNeural net for forecasting