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Jun.-Prof. Dr. Paul Marx | Universität Siegen
WIRTSCHAFTSWISSENSCHAFTEN
WIRTSCHAFTSINFORMATIK | WIRTSCHAFTSRECHT
Juniorprofessur für Betriebswirtschaftslehre, insb. Marketing
Jun.-Prof. Dr. Paul Marx | Universität Siegen
MARKETING
1
LECTURE: THEME 3: MARKET RESEARCH
SUMMER SEMESTER 2014
JUN.-PROF. DR. PAUL MARX
PRINCIPLES OF
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 2
3.Market Research 

as the basis of informed management decisions


contents
- The role of market research
- Sources of information for market research
- Quality criteria of market research
- The process of market research
- Survey as the most important method of market research
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
CASE BEECHCRAFT STARSHIP
3
First civilian aircraft with
- carbon fiber composite airframe
- canard (“duck”) design
- L-shaped wings with rudders in them
- Two turbo-prop engines mounted aft to pull
- R&D costs est. $500Mio
“For the pilot and passengers, it has really got everything...
...for the money, the performance just isn’t there...
...for $5Mio, you can buy a jet. Starship just doesn’t fit in today’s market”1
“The Starship was a $500Mio mistake because of a
lack of marketing research”2
1 Dennis Murphy, a sales person at Elliot Flying Services in Des Moines, Iowa
2 Russel Munson in “The Stock Market”, 1991
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
CASE ELECTROLUX
4
Electrolux - a scandinavian manufacturer of inexpensive vacuum cleaners - took its rhyming
phrase “Nothing Sucks Like an Electrolux” and brought it in the early 1970s to America from
English-speaking markets overseas. They didn’t know that the word “sucks” had become a
derogatory word in the US.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
CASE AMERICAN AIRLINES
5
American Airlines launched a new
leather first class seats ad campaign
(1977-78) in the Mexican market:
"Fly in Leather" (vuela encuero)
meant "Fly Naked"
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
CASE FOOD & BEVERAGES
6
In what must be one of the most bizarre
brand extensions ever Colgate decided to
use its name on a range of food products
called Colgate's Kitchen Entrees. Needless
to say, the products did not take off and
never left U.S. soil. The idea must have been
that consumers would eat their Colgate
meal, then brush their teeth with Colgate
toothpaste. The trouble was that for most
people the name Colgate does not exactly
get their taste buds tingling.
In the 1970s and early 80s, Coke began to
face stiff competition from other soft drink
producers. To remain in the number one
spot, Coke executives decided to cease
production on the classic cola in favor of New
Coke. The public was outraged, and Coca-
Cola was forced to re-launch its original
formula almost immediately. Lesson learned
-- don't mess with success.
Cocaine is a high-energy drink, containing
three and a half times the amount of
caffeine as Red Bull. It was pulled from U.S.
shelves in 2007, after the FDA declared that
its producers, Redux Beverages, were
"illegally marketing their drink as an
alternative to street drugs." The drink is still
available, however, online, in Europe and
even in select stores in the U.S. Despite the
controversy, Redux Beverages does not plan
to cease production any time soon. You
know what they say -- there's no such thing
as bad publicity.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
RETURNS ON MARKETING ACTIONS
60-95% of new products fail
50% of advertising has no effect
85% of price promotions loose money
97% brands create 37% $ (Unilever)
7
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 8
Marketing Research is there to prevent such things
from happening
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 9
Definition of Market Research
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
MARKETING RESEARCH: DEFINITION BY AMA
10
Marketing research
!
is the function that links the consumer, customer, and
public to the marketer through information --
information used to (1) identify and define marketing
opportunities and problems; (2) generate, refine, and
evaluate marketing actions; (3) monitor marketing
performance; and (4) improve understanding of
marketing as a process.
American Marketing Association (AMA), est. in 2007
Quelle: http://www.marketingpower.com/aboutama/pages/definitionofmarketing.aspx
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 11
MARKETING RESEARCH: A CONCISE DEFINITION
!
!
Marketing Research
The planning, collection, and analysis of data relevant
to marketing decision making and the communication
of the results of this analysis to management.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
Why market research?
GOALS OF MARKET RESEARCH
12
improve the quality of
decision-making
efficiently maintain customer
relationships
identify problems and
opportunities
detect changes in the market
and understand underlying
reasons
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 13
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 14
Source: Business Management Research Associates, Inc.
TOP 10 MARKET RESEARCH ACTIVITIES
Market measurement 18%
New Product development / concept testing 14%
Ad or Brand awareness monitoring / tracking 13%
Customer satisfaction (incl. Mystery Shopping) 10%
Usage and Attitude studies 7%
Media research & evaluation 6%
Advertising development and pre-testing 5%
Social Surveys for central/local governments 4%
Brand/corporate reputation 4%
Omnibus studies 3%
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
MONITORING AND MEASURING MARKETS
15
Source: http://holgerschmidt.tumblr.com/post/66555235834/deutscher-smartphone-markt-ist-fest-in-den-haenden-von
Smartphone Manufacturers
percentage of units in use
Smartphone Operating Systems
percentage of units in use
others
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
MONITORING AND MEASURING MARKETS
16
Source: http://holgerschmidt.tumblr.com/post/67876615759/der-medienwandel-beschleunigt-sich
Advertising: Internet vs. Newspaper
in billions of Euros in Germany
advertising on the internet
advertising in newspapers
News Media of Young Professionals
media used by 20-39yr. old graduates to inform themselves about current events (in percent)
TV
internet
radio
newspaper
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
ADS DEVELOPMENT AND PRETESTS
17
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
NEW PRODUCT DEVELOPMENT / CONCEPT-TESTS
18
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
NEW PRODUCT DEVELOPMENT / CONCEPT-TESTS
19
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
BASIC OBJECTS OF MARKET RESEARCH
20
market position
e.g.
company's position in considered
market
absolute and relative market share
(aggregated, per product, per
product group, per market segment)
brand awareness and image among
existing and prospective customers
general market
characteristics and trends
e.g.
market size
market growth rate
stage of the life cycle
seasonal fluctuations
development of average gains
…
customer segmentation
e.g.
general classification of customers
identification of customer segments
evaluation of segments
monitoring segments (esp. changes)
competitors
e.g.
identification of key competitors
market position of the key
competitors (e.g. market share,
earnings, cost structure, customer
base)
monitoring competitor behavior (e.g.
resources, strategies, objectives,
offerings, changes of behavior)
customer satisfaction
and loyalty
e.g.
analysis of customer satisfaction with
individual attributes of products and
services
analysis and monitoring of customer
satisfaction, loyalty, trust, lifetime
value, etc.
…
consumer behavior and
needs
e.g.
identification and evaluation of basic
customer needs and wants
analysis of information seeking
patterns, purchasing behavior,
choice-making strategies, etc.
monitoring changes of customer
needs and behavior
…
Source: based on Homburg/Krohmer 2009, p. 58.
analyze, identify, measure, evaluate, classify, monitor, report
Market Research
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 21
MARKET RESEARCH PROCESS
Define the
research
problem
Decide on
budget
data sources
research
approaches
sampling plan
contact methods
methods of data
analysis
Develop the
research plan
Collect
data
Analyze
data
Report
findings
identify and clarify
information needs
define research
problem and
questions
specify research
objectives
confirm
information value
collect data
according to the
plan or
employ an
external firm
The plan needs to be
decided upfront but
flexible enough to
incorporate changes
or iterations
This phase is the most
costly and the most
liable to error
If a problem is vaguely
defined, the results
can have little bearing
on the key issues
Overall conclusions
to be presented
rather than
overwhelming
statistical
methodologies
Formulate
conclusions and
implications from
data analysis
prepare finalized
research report
Analyze data
statistically or
subjectively
and infer answers
and implications
1 2 3 4 5
Type of data analysis
depends on type of
research
Comments
Contents
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 22
Types of Market Research
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 23
MARKET RESEARCH PROCESS
Define the
research
problem
Decide on
budget
data sources
research
approaches
sampling plan
contact methods
methods of data
analysis
Develop the
research plan
Collect
data
Analyze
data
Report
findings
identify and clarify
information needs
define research
problem and
questions
specify research
objectives
confirm
information value
collect data
according to the
plan or
employ an
external firm
The plan needs to be
decided upfront but
flexible enough to
incorporate changes
or iterations
This phase is the most
costly and the most
liable to error
If a problem is vaguely
defined, the results
can have little bearing
on the key issues
Overall conclusions
to be presented
rather than
overwhelming
statistical
methodologies
Formulate
conclusions and
implications from
data analysis
prepare finalized
research report
Analyze data
statistically or
subjectively
and infer answers
and implications
1 2 3 4 5
Type of data analysis
depends on type of
research
Comments
Contents
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 24
TYPES OF MARKET RESEARCH
By Objectives By Data Source By Methodology
Exploratory

(a.k.a. diagnostic)
Descriptive
Causal

(a.k.a. predictive,
experimental)
Qualitative
Quantitative
Primary
Secondary
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 25
Exploratory

(a.k.a. diagnostic)
Explaining data or actions to help define the problem
What was the impact on sales after change 

in the package design?
Do promotions at POS influence brand awareness?
MARKET RESEARCH BY OBJECTIVES
Descriptive
Gathering and presenting factual statements: 

who, what, when, where, how
What is historic sales trend in the industry?
What are consumer attitudes toward our product?
Causal

(a.k.a. predictive,
experimental)
Probing cause-and-effect relationships; “What if?”
Specification of how to use the research to predict 

the results of planned marketing decisions
Does level of advertising determine level of sales?
small scale
surveys, focus
groups,
interviews
larger scale
surveys,
observation,
etc.
experiments,
consumer
panels
ProblemIdentificationProblemSolving
Uncertaintyinfluencesthetypeofresearch
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 26
UNCERTAINTY SHAPES THE TYPE OF RESEARCH
Problem Identification
Research
Problem Solving Research
Market Potential Research
Market Share Research
Image Research
Market Characteristics
Research
Sales Analysis Research
Forecasting Research
Business Trends Research
Segmentation Research
Product Research
Pricing Research
Promotion Research
Distribution Research
Exploratory
research
Descriptive
research
Causal
research
AwareUncertain Certain
degree of problem/decision certainty
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 27
MARKET RESEARCH PROCESS
Define the
research
problem
Decide on
budget
data sources
research
approaches
sampling plan
contact methods
methods of data
analysis
Develop the
research plan
Collect
data
Analyze
data
Report
findings
identify and clarify
information needs
define research
problem and
questions
specify research
objectives
confirm
information value
collect data
according to the
plan or
employ an
external firm
The plan needs to be
decided upfront but
flexible enough to
incorporate changes
or iterations
This phase is the most
costly and the most
liable to error
If a problem is vaguely
defined, the results
can have little bearing
on the key issues
Overall conclusions
to be presented
rather than
overwhelming
statistical
methodologies
Formulate
conclusions and
implications from
data analysis
prepare finalized
research report
Analyze data
statistically or
subjectively
and infer answers
and implications
1 2 3 4 5
Type of data analysis
depends on type of
research
Comments
Contents
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 28
MARKET RESEARCH BY DATA SOURCE
Primary
Secondary
Original research to collect new raw data for a
specific reason. This data is then analyzed and may
be published by the researcher.
Research data that has been previously collected,
analyzed and published in the form of books,
articles, etc.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 29
SECONDARY DATA: PROS-AND-CONS
Secondary
Data
Advantages Disadvantages
Saves time and money if on
target
Aids in determining direction for
primary data collection
Pinpoints the kinds of people to
approach
Serves as a basis for other data
May not give adequate
detailed information
May not be on target with the
research problem
Quality and accuracy of data
may pose a problem
Information previously collected for any purpose other than the one at hand
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 30
PRIMARY DATA: PROS-AND-CONS
Advantages Disadvantages
Answers a specific research
question
Data are current
Source of data is known
Secrecy can be maintained
Expensive
“Piggybacking” may confuse
respondents
Quality declines in interviews
are lengthy
Reluctance to participate in
lengthy interviews
Primary
Data
Information collected for the first time to solve the particular
problem under investigation
Disadvantages are usually offset by the advantages
of primary data
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
31
Exploratory
research
Causal
research
Descriptive
research
MARKET RESEARCH BY METHODOLOGY
Qualitative
Involves understanding
human behavior and the
reasons behind it
!
Focus is on individuals and
small groups
Objectivity is not the goal,
the aim is to understand one
point of view, not all points
of view.
Primary
Data
Secondary
Data
Quantitative
Involves collecting and
measuring data
!
Often requires large data
sets. For example, large
number of people.
Uses statistical methods to
analyze data
Aims to achieve objective/
scientific view of the subject
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 32
RESEARCH METHODOLOGY
research
methodology
The searching for and gathering of
information and ideas in response
to a specific question
The set of methods used to
address a specific research
problem at hand
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 33
MARKET RESEARCH METHODS
Primary
Secondary
Research
Approach
Society
Groups
Individuals
Research
Source
Library
Web
Database
Archive
Survey
Focus Group
Depth Interview
Projective Tech.
Observation
Research
Method
Literature
review
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 34
Evaluating Secondary Data
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 35
SOURCES OF SECONDARY DATA
Internal Corporate Information
Government Agencies
Trade and Industry Associations
Business Periodicals
News Media
Databases
Internet Sources
…
Secondary
Data
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 36
Secondary
Data
EVALUATING SECONDARY DATA SOURCES
Use the C.R.A.P. test
Currency
Reliability
Authority
Purpose
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 37
Secondary
Data
EVALUATING DATA SOURCES
Currency How recent is the information?
Are there more recent updates available?
Is it current enough for your topic?
Reliability
Is content of the resource primarily opinion?
Is it balanced and evidenced?
Does the creator provide references or sources for the
data?
Authority
Who is the creator or author?
What are his/her credentials?
Is s/he an expert?
Who is the publisher os sponsor? Are they reputable?
Purpose / 

Point of View
Is it promotional or educational material?
Are there advertisements on the website?
is this fact or opinion?
Who is the intended audience?
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 38
Primary Data
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 39
Quantitative
Survey
Focus Groups
Depth Interview
Projective Techniques
Observation
Qualitative
Primary
Approaches
Survey
Observation
Depth Interview
Projective Tech.

Focus Groups

Survey

Observation
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 40
Robson (1998), Visocky & Visocky (2009)
APPARENT
TRUTH
Literature Review
InterviewSurvey
Triangulation
The combination of
methods in the study
of the same topic
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 41
BUT IT IS
MESSIER
THAN THAT
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 42
Survey Research
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 43
SURVEY RESEARCH
The most popular
technique for gathering
primary data in which a
researcher interacts with
people to obtain facts,
opinions, and attitudes.
Survey Research
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
SURVEY METHODS
Telephone
Interviewing
traditional (outdated)
computer assisted (CATI)
Mail
Interviewing
mail
mail panel
Personal
Interviewing
in-home
mall intercept
computer assisted (CAPI)
Electronic
Interviewing
e-mail
internet
internet panel
SurveyMethods
panelizable
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
QUESTIONING TACTICS
45
direct
vs.
indirect questions
Do you drink alcohol every day?
vs.
What kind of drinks do you prefer at mealtimes?
open-ended
vs.
closed-ended questions
Respondents can express themselves freely
vs.
Predefined response options
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 46
MEASUREMENT
Measurement
assigning numbers or other symbols
to characteristics of objects according
to certain pre-specified rule.
one-to-one correspondence
between the numbers and
characteristics being measured
the rules for assigning numbers
should be standardized and
applied uniformly
rules must not change over objects
or time
Measurement
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 47
SCALING
involves creating a
continuum upon which
measured objects are
located.
Scaling
Extremely
unfavorable
Extremely
favorable
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 48
PRIMARY SCALES OF MEASUREMENT
differences between objects can
be compared
zero point is arbitrary
numbers indicate the relative
positions of objects
but not the magnitude of difference
between them
Ordinal
Interval
numbers serve as labels for
identifying and classifying objects
not continuos
Nominal
zero point is fixed
ratios of scale values can be
computed
Ratio
NOT
1 2
or
1 2 1 2
3
1
2
My preference as a snack food
less more
1 2 3
a.k.a. metric
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 49
PRIMARY SCALES OF MEASUREMENT
Scale Basic Characteristics Common Examples Marketing Examples
Nominal Numbers identify and classify
objects
Social security numbers,
numbering of football players
Brand numbers, store types
sex, classification
Ordinal Numbers indicate the relative
positions of the objects but not
the magnitude of differences
between them
Quality rankings, ranking of teams
in tournament
Preference rankings, market
position, social class
Interval Differences between objects can
be compared; zero point is
arbitrary
Temperature (Fahrenheit,
Centigrade)
Attitudes, opinions, index
numbers
Ratio Zero point is fixed; ratios of
scale values can be compared
Length, weight, time, money Age, income, costs, sales,
market shares
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
IMPORTANT SCALE TYPES: LIKERT SCALE
50
Requires respondents to indicate a degree of agreement or disagreement with each of a series of
statements about the stimulus object within typically five to seven response categories.
Listed below are different opinions about Walmart. Please indicate how strongly you agree
or disagree with each by using the following scale:
Strongly
disagree Disagree
Neither
agree
nor
disagree Agree
Strongly
agree
1 Walmart sells high-quality merchandise [1] [x] [3] [4] [5]
2 Walmart has poor in-store service [1] [x] [3] [4] [5]
3 I like to shop in Walmart [1] [2] [x] [4] [5]
4
Walmart does not offer a good mix of
different brands within a product category
[1] [2] [3] [x] [5]
5 The credit policies at Walmart are terrible [1] [2] [3] [x] [5]
6 Walmart is where America shops [x] [2] [3] [4] [5]
7 I do not like advertising done by Walmart [1] [2] [3] [x] [5]
8 Walmart sells a wide variety of merchandise [1] [2] [3] [x] [5]
9 Walmart charges fair prices [1] [x] [3] [4] [5]
1 = Strongly agree
2 = Disagree
3 = Neither agree nor disagree
4 = Agree
5 = Strongly agree
NOTE the reversed scoring of items 2,4,5, and 7. Reverse the scale for these items prior analyzing to be consistent with the whole set of items, i.e. a higher score should denote a more favorable attitude.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 51
EXAMPLES Likert
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
SOME COMMONLY USED SCALES IN MARKETING
52
Construct Scale Descriptors
Attitude Very bad Bad Neither Bad
Nor Good
Good Very Good
Importance Not at All
Important
Not Important Neutral Important Very Important
Satisfaction Very Dissatisfied (Somewhat)
Dissatisfied
Neither
Dissatisfied Nor
Satisfied /
Neutral
(Somewhat)
Satisfied
Very Satisfied
Purchase Intention Definitely Will
Not Buy
Probably will
Not Buy
Might or Might
Not Buy
Probably Will
Buy
Definitely Will
Buy
Purchase Frequency Never Rarely Sometimes Often Very Often
Agreement Strongly
Disagree
Disagree Neither Agree
Nor Disagree
Agree Strongly Agree
Likert
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 53
EXAMPLES OF LABELING OF 7 AND 9 POINT SCALES
 Strongly agree
 Agree to a large extent
 Rather agree
 50/50
 Rather disagree
 Disagree to a large extent
 Strongly disagree
Like extremely
Like very much
Like moderately
Like slightly
Neither like nor dislike
Dislike slightly
Dislike moderately
Dislike very much
Dislike extremely
Likert
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
IMPORTANT SCALE TYPES: SEMANTIC DIFFERENTIAL
54
NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those
with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels.
A rating scale with end point associated with bipolar labels that have semantic meaning.
Respondents are to indicate how accurately or inaccurately each term describes the object.
This part of the study measures what certain department stores mean to you by having you
judge them on a series of descriptive scales bounded at each end by one of two bipolar
adjectives. Please mark (X) the blank that best indicates how accurately one or the other
adjective describes what the store means to you. Please be sure to mark every scale; do not
omit any scale.
NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those
with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels.
Powerful [ ] [ ] [ ] [ ] [X] [ ] [ ] Weak
Unreliable [ ] [ ] [ ] [ ] [ ] [X] [ ] Reliable
Modern [ ] [ ] [ ] [ ] [ ] [ ] [X] Old fashioned
Cold [ ] [ ] [ ] [ ] [ ] [X] [ ] Warm
Careful [ ] [X] [ ] [ ] [ ] [ ] [ ] Careless
Walmart is:
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
A SEMANTIC DIFFERENTIAL SCALE FOR MEASURING
SELF-CONCEPTS, PERSON CONCEPTS, AND PRODUCT CONCEPTS
55
Semantic
Diff.
Rugged [ ] [ ] [ ] [ ] [ ] [ ] [ ] Delicate
Excitable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Calm
Uncomfortable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Comfortable
Dominating [ ] [ ] [ ] [ ] [ ] [ ] [ ] Submissive
Thrifty [ ] [ ] [ ] [ ] [ ] [ ] [ ] Indulgent
Pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unpleasant
Contemporary [ ] [ ] [ ] [ ] [ ] [ ] [ ] Non-contemporary
Organized [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unorganized
Rational [ ] [ ] [ ] [ ] [ ] [ ] [ ] Emotional
Youthful [ ] [ ] [ ] [ ] [ ] [ ] [ ] Mature
Formal [ ] [ ] [ ] [ ] [ ] [ ] [ ] Informal
Orthodox [ ] [ ] [ ] [ ] [ ] [ ] [ ] Liberal
Complex [ ] [ ] [ ] [ ] [ ] [ ] [ ] Simple
Colorless [ ] [ ] [ ] [ ] [ ] [ ] [ ] Colorful
Modest [ ] [ ] [ ] [ ] [ ] [ ] [ ] Vain
Rating profiles of different objects / respondents / segments.
Each point corresponds to a mean or median of the respective scale.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
SEMANTIC PROFILES
56
Semantic
Diff.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
EXAMPLE
57
Semantic
Diff.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
LATENT CONSTRUCTS & MULTI-ITEM SCALES
58
A Latent Construct
is a variable that cannot be observed or
measured directly but can be inferred from other
observable measurable variables.
!
Thus, the researcher must capture the variable
through questions representing the presence/
level of the variable in question.
!
!
!
!
!
!
!
A Latent Construct
satisfied [ ] [ ] [ ] [ ] [ ] [ ] [ ] dissatisfied
pleased [ ] [ ] [ ] [ ] [ ] [ ] [ ] displeased
favorable [ ] [ ] [ ] [ ] [ ] [ ] [ ] unfavorable
pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] unpleasant
I like it very much [ ] [ ] [ ] [ ] [ ] [ ] [ ] I didn't like it at all
contented [ ] [ ] [ ] [ ] [ ] [ ] [ ] frustrated
delighted [ ] [ ] [ ] [ ] [ ] [ ] [ ] terrible
Please indicate how satisfied you were with your purchase of _____
by checking the space that best gives your answer.
α=.84
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
LATENT CONSTRUCTS & MULTI-ITEM SCALES
59
Construct Dimensions Factors Items Scale
customer
satisfaction
satisfaction
with product
satisfaction
with service
friendli-

ness
expertise
liability
the salesperson 

was appealing
the salesperson 

smiled to me
the salesperson
was courteous
strongly
agree
strongly
disagree
largely 

agree
largely
disagree
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
LATENT CONSTRUCTS & MULTI-ITEM SCALES
60
Advantages
allow to assess abstract concepts
make it easier to understand the
data and phenomenon
reduce dimensionality of data
through aggregating a large
number of observable variables in
a model to represent an
underlying concept
link observable (“sub-symbolic”)
data of the real world to symbolic
data in the modeled world
Satisfaction
Loyalty
Trust
Service Quality
Purchase intention
Attitude Toward the Brand
Involvement
Price Perception
Website Ease-of-Use
...
Examples
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 61
Quality Criteria of Market Research: 

Reliability and Validity
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
MULTI-ITEM SCALES: MEASUREMENT ACCURACY
62
Measurement
A measurement is not the true value
of the characteristic of interest but
rather an observation of it.
!
XO = XT + XS + XR
!
where
XO = the observed score of measurement
XT = the true score of characteristic
XS = systematic error
XR = random error
The True Score Model
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
!
extent to which a scale produces consistent
results in repeated measurements
RELIABILITY
63
1st
Measurement (9:15h) 85kg
!
2nd
Measurement (9:16h) 85kg
!
3rd
Measurement (9:17h) 85kg
Reliability
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
!
extent to which differences in observed scale
scores reflect true differences among objects
on the characteristic being measured
VALIDITY
64
Validity
1st
Measurement (9:15h) 85kg
!
2nd
Measurement (9:16h) 85kg
!
3rd
Measurement (9:17h) 85kg
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 65
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
RELIABILITY & VALIDITY
66
XO = XT + XS + XR
Reliability
extent to which a scale produces
consistent results in repeated
measurements
absence of random error 

(XR → 0 | XO → XR + XT)
Validity
extent to which differences in observed
scale scores reflect true differences
among objects on the characteristic
being measured
no measurement error 

( XO → XT, XS → 0, XR → 0)
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
RELATIONSHIP BETWEEN RELIABILITY & VALIDITY
67
XO = XT + XS + XR
validity implies reliability

( XO = XT | XS = 0, XR = 0)
unreliability implies invalidity

( XR ≠ 0 | XO = XT +XR ≠ XT)
reliability does not imply validity

( XR = 0, XS ≠ 0 | XO = XT +XS ≠ XT)
!
reliability is a necessary, but not
sufficient, condition of validity
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 68
Sampling
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 69
MARKET RESEARCH PROCESS
Define the
research
problem
Decide on
budget
data sources
research
approaches
sampling plan
contact methods
methods of data
analysis
Develop the
research plan
Collect
data
Analyze
data
Report
findings
identify and clarify
information needs
define research
problem and
questions
specify research
objectives
confirm
information value
collect data
according to the
plan or
employ an
external firm
The plan needs to be
decided upfront but
flexible enough to
incorporate changes
or iterations
This phase is the most
costly and the most
liable to error
If a problem is vaguely
defined, the results
can have little bearing
on the key issues
Overall conclusions
to be presented
rather than
overwhelming
statistical
methodologies
Formulate
conclusions and
implications from
data analysis
prepare finalized
research report
Analyze data
statistically or
subjectively
and infer answers
and implications
1 2 3 4 5
Type of data analysis
depends on type of
research
Comments
Contents
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 70
The world’s most famous
newspaper error
President Harry Truman against
Thomas Dewey
Chicago Tribute prepared an
incorrect headline without first
getting accurate information
Reason?
→ bias
→ inaccurate opinion polls
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 71
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 72
Yes, dear Dilbert, it was the wrong Sample
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
SAMPLING
73
Population
the group of people we wish to
understand. Populations are often
segmented by demographic or
psychographic features (age, gender,
interests, lifestyles, etc.)
Sample
a subset of population
that represents the whole
group
Most research cannot test
everyone. Instead a sample of
the whole population is
selected and tested.
!
If this is done well, the results
can be applied to the whole
population.
!
This selection and testing of a
sample is called sampling.
!
If a sample is poorly chosen, all
the data may be useless.
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 74
SAMPLING: TWO GENERAL METHODS
This relies on personal judgement of theresearcher (often on people available, e.g.,people passing in the street or walkingthrough a mall).
!
This may yield good estimates of populationcharacteristics, however, doesn’t allow forobjective evaluation of the precision ofsample results. That is, the results are notprojectable to the population.
Non-
probability
Sampling
Here, sampling units are selected by
chance, i.e., randomly.
!
This randomness allows applying
statistical techniques to determine the
precision of the sample estimates and
their confidence intervals. The results
are generalizable and projectable to
the population from which the sample
is drawn.
Probability
Sampling
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
CLASSIFICATION OF SAMPLING TECHNIQUES
75
Sampling Techniques
Non-probability Probability
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Stratified
Sampling
Cluster
Sampling
Other Samp-
ling Techniques
Systematic
Sampling
Simple Random
Sampling
Proportionate Disproportionate
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
QUOTA SAMPLING
76
Control
Characteristic
Population
Composition Sample Composition
Percentage Percentage Number
Sex
Male

Female


48

52

-------
100


48

52

-------
100


480

520

-------
1000
Age

18-30
31-45
45-60

Over 60


27

39

16

18
-------
100


27

39

16

18
-------
100


270

390

160

180
-------
1000
!
develop control categories, or quotas, of
population elements (e.g., sex, age, race,
income, company size, turnover, etc.) so that
the proportion of the elements possessing
these characteristics in the sample reflects
their distribution in the population.
!
The elements themselves are selected based
on convenience or judgment. The only
requirement, however, is that the elements
selected fit the control characteristics (quota).
!
Quota Sampling
Often used in
online
surveys
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 77
MARKET RESEARCH PROCESS
Define the
research
problem
Decide on
budget
data sources
research
approaches
sampling plan
contact methods
methods of data
analysis
Develop the
research plan
Collect
data
Analyze
data
Report
findings
identify and clarify
information needs
define research
problem and
questions
specify research
objectives
confirm
information value
collect data
according to the
plan or
employ an
external firm
The plan needs to be
decided upfront but
flexible enough to
incorporate changes
or iterations
This phase is the most
costly and the most
liable to error
If a problem is vaguely
defined, the results
can have little bearing
on the key issues
Overall conclusions
to be presented
rather than
overwhelming
statistical
methodologies
Formulate
conclusions and
implications from
data analysis
prepare finalized
research report
Analyze data
statistically or
subjectively
and infer answers
and implications
1 2 3 4 5
Type of data analysis
depends on type of
research
Comments
Contents
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
DATA ANALYSIS
78
Products
Blue Red Yellow Choice
Respondent #1 50 40 10 Blue
Respondent #2 0 65 75 Yellow
Respondent #3 40 30 20 Blue
Average 30 45 35 Red
Given the following preferences, which product should we offer to this market?
Red exhibits the highest overall preference
But no one in the market prefers Red
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
DATA ANALYSIS
79
Given the following individual preference structures,
how does the collective preference structure looks like?
> >
> >
> >
> > >
respondent #1
respondent #2
respondent #3
let’s count the “votes”:
vs
vs
vs
number of votes
2 vs 1
2 vs 1
2 vs 1
✔
✔
✔
Result:
apple is the most and the least preferred item
aggregate preferences are inconsistent!
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
METHODS OF DATA ANALYSIS
80
Methods of data analysis
Univariate methods
Bi- and multivariate
methods
Interdependence

analysis
Dependence
analysis
regression analysis
…
cluster analysis
…
average
standard error / variance
…
Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing”
WHEN NOT TO CONDUCT MARKET RESEARCH
81
Occasion Comments
Lack of resources
If quantitative research is needed, it is not worth doing unless a
statistically significant sample can be used. When funds are
insufficient to implement any decisions resulting from the research.
Closed mindset
When decision has already been made. Research is used only as a
rubber stamp of a preconceived idea.
Information not needed When decision-making information already exists.
Vague objectives When managers cannot agree on what they need to know to make a decision.
Market research cannot be helpful unless it is probing a particular issue.
Results not actionable
Where, e.g., psychographic data is used which will not help he
company form firm decisions.
Late timing When research results come too late to influence the decision.
Poor timing
If a product is in a “decline” phase there is little point in
researching new product varieties
Costs outweigh benefits
The expected value of information should outweigh the costs
of gathering an analyzing the data.

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3. Principles of Marketing - SS2014 - University of Siegen - Paul Marx: Chapter 3. Market Research

  • 1. Jun.-Prof. Dr. Paul Marx | Universität Siegen WIRTSCHAFTSWISSENSCHAFTEN WIRTSCHAFTSINFORMATIK | WIRTSCHAFTSRECHT Juniorprofessur für Betriebswirtschaftslehre, insb. Marketing Jun.-Prof. Dr. Paul Marx | Universität Siegen MARKETING 1 LECTURE: THEME 3: MARKET RESEARCH SUMMER SEMESTER 2014 JUN.-PROF. DR. PAUL MARX PRINCIPLES OF
  • 2. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 2 3.Market Research 
 as the basis of informed management decisions 
 contents - The role of market research - Sources of information for market research - Quality criteria of market research - The process of market research - Survey as the most important method of market research
  • 3. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE BEECHCRAFT STARSHIP 3 First civilian aircraft with - carbon fiber composite airframe - canard (“duck”) design - L-shaped wings with rudders in them - Two turbo-prop engines mounted aft to pull - R&D costs est. $500Mio “For the pilot and passengers, it has really got everything... ...for the money, the performance just isn’t there... ...for $5Mio, you can buy a jet. Starship just doesn’t fit in today’s market”1 “The Starship was a $500Mio mistake because of a lack of marketing research”2 1 Dennis Murphy, a sales person at Elliot Flying Services in Des Moines, Iowa 2 Russel Munson in “The Stock Market”, 1991
  • 4. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE ELECTROLUX 4 Electrolux - a scandinavian manufacturer of inexpensive vacuum cleaners - took its rhyming phrase “Nothing Sucks Like an Electrolux” and brought it in the early 1970s to America from English-speaking markets overseas. They didn’t know that the word “sucks” had become a derogatory word in the US.
  • 5. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE AMERICAN AIRLINES 5 American Airlines launched a new leather first class seats ad campaign (1977-78) in the Mexican market: "Fly in Leather" (vuela encuero) meant "Fly Naked"
  • 6. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE FOOD & BEVERAGES 6 In what must be one of the most bizarre brand extensions ever Colgate decided to use its name on a range of food products called Colgate's Kitchen Entrees. Needless to say, the products did not take off and never left U.S. soil. The idea must have been that consumers would eat their Colgate meal, then brush their teeth with Colgate toothpaste. The trouble was that for most people the name Colgate does not exactly get their taste buds tingling. In the 1970s and early 80s, Coke began to face stiff competition from other soft drink producers. To remain in the number one spot, Coke executives decided to cease production on the classic cola in favor of New Coke. The public was outraged, and Coca- Cola was forced to re-launch its original formula almost immediately. Lesson learned -- don't mess with success. Cocaine is a high-energy drink, containing three and a half times the amount of caffeine as Red Bull. It was pulled from U.S. shelves in 2007, after the FDA declared that its producers, Redux Beverages, were "illegally marketing their drink as an alternative to street drugs." The drink is still available, however, online, in Europe and even in select stores in the U.S. Despite the controversy, Redux Beverages does not plan to cease production any time soon. You know what they say -- there's no such thing as bad publicity.
  • 7. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” RETURNS ON MARKETING ACTIONS 60-95% of new products fail 50% of advertising has no effect 85% of price promotions loose money 97% brands create 37% $ (Unilever) 7
  • 8. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 8 Marketing Research is there to prevent such things from happening
  • 9. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 9 Definition of Market Research
  • 10. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MARKETING RESEARCH: DEFINITION BY AMA 10 Marketing research
! is the function that links the consumer, customer, and public to the marketer through information -- information used to (1) identify and define marketing opportunities and problems; (2) generate, refine, and evaluate marketing actions; (3) monitor marketing performance; and (4) improve understanding of marketing as a process. American Marketing Association (AMA), est. in 2007 Quelle: http://www.marketingpower.com/aboutama/pages/definitionofmarketing.aspx
  • 11. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 11 MARKETING RESEARCH: A CONCISE DEFINITION ! ! Marketing Research The planning, collection, and analysis of data relevant to marketing decision making and the communication of the results of this analysis to management.
  • 12. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” Why market research? GOALS OF MARKET RESEARCH 12 improve the quality of decision-making efficiently maintain customer relationships identify problems and opportunities detect changes in the market and understand underlying reasons
  • 13. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 13
  • 14. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 14 Source: Business Management Research Associates, Inc. TOP 10 MARKET RESEARCH ACTIVITIES Market measurement 18% New Product development / concept testing 14% Ad or Brand awareness monitoring / tracking 13% Customer satisfaction (incl. Mystery Shopping) 10% Usage and Attitude studies 7% Media research & evaluation 6% Advertising development and pre-testing 5% Social Surveys for central/local governments 4% Brand/corporate reputation 4% Omnibus studies 3%
  • 15. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MONITORING AND MEASURING MARKETS 15 Source: http://holgerschmidt.tumblr.com/post/66555235834/deutscher-smartphone-markt-ist-fest-in-den-haenden-von Smartphone Manufacturers percentage of units in use Smartphone Operating Systems percentage of units in use others
  • 16. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MONITORING AND MEASURING MARKETS 16 Source: http://holgerschmidt.tumblr.com/post/67876615759/der-medienwandel-beschleunigt-sich Advertising: Internet vs. Newspaper in billions of Euros in Germany advertising on the internet advertising in newspapers News Media of Young Professionals media used by 20-39yr. old graduates to inform themselves about current events (in percent) TV internet radio newspaper
  • 17. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” ADS DEVELOPMENT AND PRETESTS 17
  • 18. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” NEW PRODUCT DEVELOPMENT / CONCEPT-TESTS 18
  • 19. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” NEW PRODUCT DEVELOPMENT / CONCEPT-TESTS 19
  • 20. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” BASIC OBJECTS OF MARKET RESEARCH 20 market position e.g. company's position in considered market absolute and relative market share (aggregated, per product, per product group, per market segment) brand awareness and image among existing and prospective customers general market characteristics and trends e.g. market size market growth rate stage of the life cycle seasonal fluctuations development of average gains … customer segmentation e.g. general classification of customers identification of customer segments evaluation of segments monitoring segments (esp. changes) competitors e.g. identification of key competitors market position of the key competitors (e.g. market share, earnings, cost structure, customer base) monitoring competitor behavior (e.g. resources, strategies, objectives, offerings, changes of behavior) customer satisfaction and loyalty e.g. analysis of customer satisfaction with individual attributes of products and services analysis and monitoring of customer satisfaction, loyalty, trust, lifetime value, etc. … consumer behavior and needs e.g. identification and evaluation of basic customer needs and wants analysis of information seeking patterns, purchasing behavior, choice-making strategies, etc. monitoring changes of customer needs and behavior … Source: based on Homburg/Krohmer 2009, p. 58. analyze, identify, measure, evaluate, classify, monitor, report Market Research
  • 21. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 21 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  • 22. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 22 Types of Market Research
  • 23. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 23 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  • 24. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 24 TYPES OF MARKET RESEARCH By Objectives By Data Source By Methodology Exploratory
 (a.k.a. diagnostic) Descriptive Causal
 (a.k.a. predictive, experimental) Qualitative Quantitative Primary Secondary
  • 25. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 25 Exploratory
 (a.k.a. diagnostic) Explaining data or actions to help define the problem What was the impact on sales after change 
 in the package design? Do promotions at POS influence brand awareness? MARKET RESEARCH BY OBJECTIVES Descriptive Gathering and presenting factual statements: 
 who, what, when, where, how What is historic sales trend in the industry? What are consumer attitudes toward our product? Causal
 (a.k.a. predictive, experimental) Probing cause-and-effect relationships; “What if?” Specification of how to use the research to predict 
 the results of planned marketing decisions Does level of advertising determine level of sales? small scale surveys, focus groups, interviews larger scale surveys, observation, etc. experiments, consumer panels ProblemIdentificationProblemSolving Uncertaintyinfluencesthetypeofresearch
  • 26. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 26 UNCERTAINTY SHAPES THE TYPE OF RESEARCH Problem Identification Research Problem Solving Research Market Potential Research Market Share Research Image Research Market Characteristics Research Sales Analysis Research Forecasting Research Business Trends Research Segmentation Research Product Research Pricing Research Promotion Research Distribution Research Exploratory research Descriptive research Causal research AwareUncertain Certain degree of problem/decision certainty
  • 27. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 27 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  • 28. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 28 MARKET RESEARCH BY DATA SOURCE Primary Secondary Original research to collect new raw data for a specific reason. This data is then analyzed and may be published by the researcher. Research data that has been previously collected, analyzed and published in the form of books, articles, etc.
  • 29. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 29 SECONDARY DATA: PROS-AND-CONS Secondary Data Advantages Disadvantages Saves time and money if on target Aids in determining direction for primary data collection Pinpoints the kinds of people to approach Serves as a basis for other data May not give adequate detailed information May not be on target with the research problem Quality and accuracy of data may pose a problem Information previously collected for any purpose other than the one at hand
  • 30. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 30 PRIMARY DATA: PROS-AND-CONS Advantages Disadvantages Answers a specific research question Data are current Source of data is known Secrecy can be maintained Expensive “Piggybacking” may confuse respondents Quality declines in interviews are lengthy Reluctance to participate in lengthy interviews Primary Data Information collected for the first time to solve the particular problem under investigation Disadvantages are usually offset by the advantages of primary data
  • 31. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 31 Exploratory research Causal research Descriptive research MARKET RESEARCH BY METHODOLOGY Qualitative Involves understanding human behavior and the reasons behind it ! Focus is on individuals and small groups Objectivity is not the goal, the aim is to understand one point of view, not all points of view. Primary Data Secondary Data Quantitative Involves collecting and measuring data ! Often requires large data sets. For example, large number of people. Uses statistical methods to analyze data Aims to achieve objective/ scientific view of the subject
  • 32. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 32 RESEARCH METHODOLOGY research methodology The searching for and gathering of information and ideas in response to a specific question The set of methods used to address a specific research problem at hand
  • 33. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 33 MARKET RESEARCH METHODS Primary Secondary Research Approach Society Groups Individuals Research Source Library Web Database Archive Survey Focus Group Depth Interview Projective Tech. Observation Research Method Literature review
  • 34. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 34 Evaluating Secondary Data
  • 35. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 35 SOURCES OF SECONDARY DATA Internal Corporate Information Government Agencies Trade and Industry Associations Business Periodicals News Media Databases Internet Sources … Secondary Data
  • 36. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 36 Secondary Data EVALUATING SECONDARY DATA SOURCES Use the C.R.A.P. test Currency Reliability Authority Purpose
  • 37. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 37 Secondary Data EVALUATING DATA SOURCES Currency How recent is the information? Are there more recent updates available? Is it current enough for your topic? Reliability Is content of the resource primarily opinion? Is it balanced and evidenced? Does the creator provide references or sources for the data? Authority Who is the creator or author? What are his/her credentials? Is s/he an expert? Who is the publisher os sponsor? Are they reputable? Purpose / 
 Point of View Is it promotional or educational material? Are there advertisements on the website? is this fact or opinion? Who is the intended audience?
  • 38. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 38 Primary Data
  • 39. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 39 Quantitative Survey Focus Groups Depth Interview Projective Techniques Observation Qualitative Primary Approaches Survey Observation Depth Interview Projective Tech.
 Focus Groups
 Survey
 Observation
  • 40. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 40 Robson (1998), Visocky & Visocky (2009) APPARENT TRUTH Literature Review InterviewSurvey Triangulation The combination of methods in the study of the same topic
  • 41. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 41 BUT IT IS MESSIER THAN THAT
  • 42. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 42 Survey Research
  • 43. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 43 SURVEY RESEARCH The most popular technique for gathering primary data in which a researcher interacts with people to obtain facts, opinions, and attitudes. Survey Research
  • 44. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SURVEY METHODS Telephone Interviewing traditional (outdated) computer assisted (CATI) Mail Interviewing mail mail panel Personal Interviewing in-home mall intercept computer assisted (CAPI) Electronic Interviewing e-mail internet internet panel SurveyMethods panelizable
  • 45. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” QUESTIONING TACTICS 45 direct vs. indirect questions Do you drink alcohol every day? vs. What kind of drinks do you prefer at mealtimes? open-ended vs. closed-ended questions Respondents can express themselves freely vs. Predefined response options
  • 46. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 46 MEASUREMENT Measurement assigning numbers or other symbols to characteristics of objects according to certain pre-specified rule. one-to-one correspondence between the numbers and characteristics being measured the rules for assigning numbers should be standardized and applied uniformly rules must not change over objects or time Measurement
  • 47. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 47 SCALING involves creating a continuum upon which measured objects are located. Scaling Extremely unfavorable Extremely favorable
  • 48. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 48 PRIMARY SCALES OF MEASUREMENT differences between objects can be compared zero point is arbitrary numbers indicate the relative positions of objects but not the magnitude of difference between them Ordinal Interval numbers serve as labels for identifying and classifying objects not continuos Nominal zero point is fixed ratios of scale values can be computed Ratio NOT 1 2 or 1 2 1 2 3 1 2 My preference as a snack food less more 1 2 3 a.k.a. metric
  • 49. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 49 PRIMARY SCALES OF MEASUREMENT Scale Basic Characteristics Common Examples Marketing Examples Nominal Numbers identify and classify objects Social security numbers, numbering of football players Brand numbers, store types sex, classification Ordinal Numbers indicate the relative positions of the objects but not the magnitude of differences between them Quality rankings, ranking of teams in tournament Preference rankings, market position, social class Interval Differences between objects can be compared; zero point is arbitrary Temperature (Fahrenheit, Centigrade) Attitudes, opinions, index numbers Ratio Zero point is fixed; ratios of scale values can be compared Length, weight, time, money Age, income, costs, sales, market shares
  • 50. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” IMPORTANT SCALE TYPES: LIKERT SCALE 50 Requires respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus object within typically five to seven response categories. Listed below are different opinions about Walmart. Please indicate how strongly you agree or disagree with each by using the following scale: Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree 1 Walmart sells high-quality merchandise [1] [x] [3] [4] [5] 2 Walmart has poor in-store service [1] [x] [3] [4] [5] 3 I like to shop in Walmart [1] [2] [x] [4] [5] 4 Walmart does not offer a good mix of different brands within a product category [1] [2] [3] [x] [5] 5 The credit policies at Walmart are terrible [1] [2] [3] [x] [5] 6 Walmart is where America shops [x] [2] [3] [4] [5] 7 I do not like advertising done by Walmart [1] [2] [3] [x] [5] 8 Walmart sells a wide variety of merchandise [1] [2] [3] [x] [5] 9 Walmart charges fair prices [1] [x] [3] [4] [5] 1 = Strongly agree 2 = Disagree 3 = Neither agree nor disagree 4 = Agree 5 = Strongly agree NOTE the reversed scoring of items 2,4,5, and 7. Reverse the scale for these items prior analyzing to be consistent with the whole set of items, i.e. a higher score should denote a more favorable attitude.
  • 51. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 51 EXAMPLES Likert
  • 52. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SOME COMMONLY USED SCALES IN MARKETING 52 Construct Scale Descriptors Attitude Very bad Bad Neither Bad Nor Good Good Very Good Importance Not at All Important Not Important Neutral Important Very Important Satisfaction Very Dissatisfied (Somewhat) Dissatisfied Neither Dissatisfied Nor Satisfied / Neutral (Somewhat) Satisfied Very Satisfied Purchase Intention Definitely Will Not Buy Probably will Not Buy Might or Might Not Buy Probably Will Buy Definitely Will Buy Purchase Frequency Never Rarely Sometimes Often Very Often Agreement Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree Likert
  • 53. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 53 EXAMPLES OF LABELING OF 7 AND 9 POINT SCALES  Strongly agree  Agree to a large extent  Rather agree  50/50  Rather disagree  Disagree to a large extent  Strongly disagree Like extremely Like very much Like moderately Like slightly Neither like nor dislike Dislike slightly Dislike moderately Dislike very much Dislike extremely Likert
  • 54. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” IMPORTANT SCALE TYPES: SEMANTIC DIFFERENTIAL 54 NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. A rating scale with end point associated with bipolar labels that have semantic meaning. Respondents are to indicate how accurately or inaccurately each term describes the object. This part of the study measures what certain department stores mean to you by having you judge them on a series of descriptive scales bounded at each end by one of two bipolar adjectives. Please mark (X) the blank that best indicates how accurately one or the other adjective describes what the store means to you. Please be sure to mark every scale; do not omit any scale. NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. Powerful [ ] [ ] [ ] [ ] [X] [ ] [ ] Weak Unreliable [ ] [ ] [ ] [ ] [ ] [X] [ ] Reliable Modern [ ] [ ] [ ] [ ] [ ] [ ] [X] Old fashioned Cold [ ] [ ] [ ] [ ] [ ] [X] [ ] Warm Careful [ ] [X] [ ] [ ] [ ] [ ] [ ] Careless Walmart is:
  • 55. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” A SEMANTIC DIFFERENTIAL SCALE FOR MEASURING SELF-CONCEPTS, PERSON CONCEPTS, AND PRODUCT CONCEPTS 55 Semantic Diff. Rugged [ ] [ ] [ ] [ ] [ ] [ ] [ ] Delicate Excitable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Calm Uncomfortable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Comfortable Dominating [ ] [ ] [ ] [ ] [ ] [ ] [ ] Submissive Thrifty [ ] [ ] [ ] [ ] [ ] [ ] [ ] Indulgent Pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unpleasant Contemporary [ ] [ ] [ ] [ ] [ ] [ ] [ ] Non-contemporary Organized [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unorganized Rational [ ] [ ] [ ] [ ] [ ] [ ] [ ] Emotional Youthful [ ] [ ] [ ] [ ] [ ] [ ] [ ] Mature Formal [ ] [ ] [ ] [ ] [ ] [ ] [ ] Informal Orthodox [ ] [ ] [ ] [ ] [ ] [ ] [ ] Liberal Complex [ ] [ ] [ ] [ ] [ ] [ ] [ ] Simple Colorless [ ] [ ] [ ] [ ] [ ] [ ] [ ] Colorful Modest [ ] [ ] [ ] [ ] [ ] [ ] [ ] Vain Rating profiles of different objects / respondents / segments. Each point corresponds to a mean or median of the respective scale.
  • 56. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SEMANTIC PROFILES 56 Semantic Diff.
  • 57. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” EXAMPLE 57 Semantic Diff.
  • 58. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” LATENT CONSTRUCTS & MULTI-ITEM SCALES 58 A Latent Construct is a variable that cannot be observed or measured directly but can be inferred from other observable measurable variables. ! Thus, the researcher must capture the variable through questions representing the presence/ level of the variable in question. ! ! ! ! ! ! ! A Latent Construct satisfied [ ] [ ] [ ] [ ] [ ] [ ] [ ] dissatisfied pleased [ ] [ ] [ ] [ ] [ ] [ ] [ ] displeased favorable [ ] [ ] [ ] [ ] [ ] [ ] [ ] unfavorable pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] unpleasant I like it very much [ ] [ ] [ ] [ ] [ ] [ ] [ ] I didn't like it at all contented [ ] [ ] [ ] [ ] [ ] [ ] [ ] frustrated delighted [ ] [ ] [ ] [ ] [ ] [ ] [ ] terrible Please indicate how satisfied you were with your purchase of _____ by checking the space that best gives your answer. α=.84
  • 59. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” LATENT CONSTRUCTS & MULTI-ITEM SCALES 59 Construct Dimensions Factors Items Scale customer satisfaction satisfaction with product satisfaction with service friendli-
 ness expertise liability the salesperson 
 was appealing the salesperson 
 smiled to me the salesperson was courteous strongly agree strongly disagree largely 
 agree largely disagree
  • 60. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” LATENT CONSTRUCTS & MULTI-ITEM SCALES 60 Advantages allow to assess abstract concepts make it easier to understand the data and phenomenon reduce dimensionality of data through aggregating a large number of observable variables in a model to represent an underlying concept link observable (“sub-symbolic”) data of the real world to symbolic data in the modeled world Satisfaction Loyalty Trust Service Quality Purchase intention Attitude Toward the Brand Involvement Price Perception Website Ease-of-Use ... Examples
  • 61. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 61 Quality Criteria of Market Research: 
 Reliability and Validity
  • 62. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MULTI-ITEM SCALES: MEASUREMENT ACCURACY 62 Measurement A measurement is not the true value of the characteristic of interest but rather an observation of it. ! XO = XT + XS + XR ! where XO = the observed score of measurement XT = the true score of characteristic XS = systematic error XR = random error The True Score Model
  • 63. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” ! extent to which a scale produces consistent results in repeated measurements RELIABILITY 63 1st Measurement (9:15h) 85kg ! 2nd Measurement (9:16h) 85kg ! 3rd Measurement (9:17h) 85kg Reliability
  • 64. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” ! extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured VALIDITY 64 Validity 1st Measurement (9:15h) 85kg ! 2nd Measurement (9:16h) 85kg ! 3rd Measurement (9:17h) 85kg
  • 65. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 65
  • 66. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” RELIABILITY & VALIDITY 66 XO = XT + XS + XR Reliability extent to which a scale produces consistent results in repeated measurements absence of random error 
 (XR → 0 | XO → XR + XT) Validity extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured no measurement error 
 ( XO → XT, XS → 0, XR → 0)
  • 67. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” RELATIONSHIP BETWEEN RELIABILITY & VALIDITY 67 XO = XT + XS + XR validity implies reliability
 ( XO = XT | XS = 0, XR = 0) unreliability implies invalidity
 ( XR ≠ 0 | XO = XT +XR ≠ XT) reliability does not imply validity
 ( XR = 0, XS ≠ 0 | XO = XT +XS ≠ XT) ! reliability is a necessary, but not sufficient, condition of validity
  • 68. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 68 Sampling
  • 69. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 69 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  • 70. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 70 The world’s most famous newspaper error President Harry Truman against Thomas Dewey Chicago Tribute prepared an incorrect headline without first getting accurate information Reason? → bias → inaccurate opinion polls
  • 71. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 71
  • 72. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 72 Yes, dear Dilbert, it was the wrong Sample
  • 73. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SAMPLING 73 Population the group of people we wish to understand. Populations are often segmented by demographic or psychographic features (age, gender, interests, lifestyles, etc.) Sample a subset of population that represents the whole group Most research cannot test everyone. Instead a sample of the whole population is selected and tested. ! If this is done well, the results can be applied to the whole population. ! This selection and testing of a sample is called sampling. ! If a sample is poorly chosen, all the data may be useless.
  • 74. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 74 SAMPLING: TWO GENERAL METHODS This relies on personal judgement of theresearcher (often on people available, e.g.,people passing in the street or walkingthrough a mall). ! This may yield good estimates of populationcharacteristics, however, doesn’t allow forobjective evaluation of the precision ofsample results. That is, the results are notprojectable to the population. Non- probability Sampling Here, sampling units are selected by chance, i.e., randomly. ! This randomness allows applying statistical techniques to determine the precision of the sample estimates and their confidence intervals. The results are generalizable and projectable to the population from which the sample is drawn. Probability Sampling
  • 75. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CLASSIFICATION OF SAMPLING TECHNIQUES 75 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate
  • 76. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” QUOTA SAMPLING 76 Control Characteristic Population Composition Sample Composition Percentage Percentage Number Sex Male
 Female 
 48
 52
 ------- 100 
 48
 52
 ------- 100 
 480
 520
 ------- 1000 Age
 18-30 31-45 45-60
 Over 60 
 27
 39
 16
 18 ------- 100 
 27
 39
 16
 18 ------- 100 
 270
 390
 160
 180 ------- 1000 ! develop control categories, or quotas, of population elements (e.g., sex, age, race, income, company size, turnover, etc.) so that the proportion of the elements possessing these characteristics in the sample reflects their distribution in the population. ! The elements themselves are selected based on convenience or judgment. The only requirement, however, is that the elements selected fit the control characteristics (quota). ! Quota Sampling Often used in online surveys
  • 77. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 77 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  • 78. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” DATA ANALYSIS 78 Products Blue Red Yellow Choice Respondent #1 50 40 10 Blue Respondent #2 0 65 75 Yellow Respondent #3 40 30 20 Blue Average 30 45 35 Red Given the following preferences, which product should we offer to this market? Red exhibits the highest overall preference But no one in the market prefers Red
  • 79. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” DATA ANALYSIS 79 Given the following individual preference structures, how does the collective preference structure looks like? > > > > > > > > > respondent #1 respondent #2 respondent #3 let’s count the “votes”: vs vs vs number of votes 2 vs 1 2 vs 1 2 vs 1 ✔ ✔ ✔ Result: apple is the most and the least preferred item aggregate preferences are inconsistent!
  • 80. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” METHODS OF DATA ANALYSIS 80 Methods of data analysis Univariate methods Bi- and multivariate methods Interdependence
 analysis Dependence analysis regression analysis … cluster analysis … average standard error / variance …
  • 81. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” WHEN NOT TO CONDUCT MARKET RESEARCH 81 Occasion Comments Lack of resources If quantitative research is needed, it is not worth doing unless a statistically significant sample can be used. When funds are insufficient to implement any decisions resulting from the research. Closed mindset When decision has already been made. Research is used only as a rubber stamp of a preconceived idea. Information not needed When decision-making information already exists. Vague objectives When managers cannot agree on what they need to know to make a decision. Market research cannot be helpful unless it is probing a particular issue. Results not actionable Where, e.g., psychographic data is used which will not help he company form firm decisions. Late timing When research results come too late to influence the decision. Poor timing If a product is in a “decline” phase there is little point in researching new product varieties Costs outweigh benefits The expected value of information should outweigh the costs of gathering an analyzing the data.