Response–1 Market Response Modeling G Response Modeling Basics.
ME Basics–1 Marketing Engineering Basics G Introduction G Course Overview G Software Review.
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Transcript of ME Basics–1 Marketing Engineering Basics G Introduction G Course Overview G Software Review.
ME Basics–2
Daily Marketing Decisions
Segmentation
Targeting
Positioning
BudgetsMarketing Mix
Market sizeMarket share
Campaign effectiveness
Pricing structure
Portfolio
Man
agem
ent
Advertising design
Sales channels
ME Basics–3
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–4
Introducing . . .Marketing Engineering
Course description and structure
What is marketing engineering?
Why learn marketing engineering?
Introduction to software
Introduce Conglom Promotions case
ME Basics–5
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–6
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–7
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–8
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–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
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–11
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–12
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–15
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–16
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–17
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–18
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
ME Basics–19
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–20
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–21
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–22
Small Models Example:Trial/Repeat Model
Share =% Aware
% Available | Aware
% Try | Aware, Available
% Repeat | Try, Aware, Available Usage Rate
ME Basics–23
Target Population
Aware?
Available?
Try?
Repeat?
Market Share = ?
50%
80%
40%
50%
Trial/Repeat Model
ME Basics–26
% Repeaters Among Triers
(Repeat)
100%
Time
Note—late triers often do not become
regular users
Repeat Dynamics
ME Basics–27
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–28
New Phenomenon:Retail Outlet Management
Sales/Outlet
# Company Outlets in Market
What People Observed
What People Thought
ME Basics–29
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–30
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–31
Model Benefits
Small models can offer insight
Models can identify phenomena
Operational models can provide long-term benefits
ME Basics–32
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–33
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–34
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–35
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–36
Marketing Engineering Software
Excel Models Non-Excel ModelsNon-Excel Models by Commercial Vendors
ME Basics–37
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–38
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