This document provides an overview of market research from Jun.-Prof. Dr. Paul Marx at Universität Siegen. It discusses the role of market research, the market research process, types of market research by objectives, data sources and methodology. Market research involves planning, collecting and analyzing relevant data to inform marketing decisions and communicating results to management. The key types are exploratory, descriptive and causal research using primary or secondary qualitative and quantitative methods. Market research is important to prevent costly marketing mistakes and ensure efficient decision making.

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.