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Summary of all lectures CMA
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Summary of all the lectures of CMA, including steps on how to interpret results.
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Lecture 1: IntroductionMarketing research
Marketers use the 'right' principle to do marketing. To be right in
marketing: you need decision making information that reduces uncertainty
to aid in smarter managerial decision making.
The marketing system:
The task of marketing management: product, promotion, price and
placement towards the target market.
The environment affecting marketing: economic, political, social, natural,
technological, competitive.
Marketing research: planning, collection and analysis of data relevant to
marketing decision making and the communication of the results of this
analysis to management.
Micro-level: individual
Macro-level: market
Value of marketing research: decreased uncertainty, increased likelihood
of right decision, improved marketing performance and higher profits.
Distinction:
Marketing decision problem: action oriented, focuses on the
symptoms, asks what the decision-maker should do.
o Ex: what should we do to increase store traffic? How can we
reduce consumer complaints? What logo designs should we
use for olympics?
Marketing research problem: information oriented, focuses on
underlying causes, asks what information is needed and how it can
be obtained.
o Ex: how much do people like the different proposed logos
Classifying marketing research
Type of data:
o Quantitative: research that focuses on numbers, amendable
to statistical analysis.
, Ex: survey measuring satisfaction levels on a scale.
Good for detailed usage, variations between groups,
measuring preferences.
o Qualitative: research not concerned with numbers.
Ex: in-depth interviews to understand thoughts about a
brand.
Good for motivations behind behaviour, stimulating new
ideas.
Research design:
o Exploratory: research in which the major emphasis is on
gaining ideas and insights.
Ex: explore reasons for a decline in product sales,
without specific hypothesis.
o Descriptive: research often guided by an intitial hypotheses.
Ex: analyzing demographic information of costumers,
detailing who they are and how often they purchase.
o Causal: research in which major emphasis is on determining a
cause-and-effect relationship.
Ex: testing the impact of a new advertising campaign on
sales.
Use laboratory of field experiments.
By data source
o Secondary: data previously collected for purposes other than
the research at hand. With internal and external sources.
Ex: using reports and databases.
Syndicated research: large-scale marketing
research that is undertaken by a research firm to
be sold, often on a subscription basis, to a number
of client.
Ex: GfK consumer panel.
o Primary: data collected specifically to answer the question
posed by the current research objectives. By communication
or observation.
Ex: conducting a survey.
, Lecture 3: Measurement and scaling
Some variables are simple, and some are more complex. Many theories
involve unobserved or latent constructs. To test a theory, we need to
measure these latent constructs.
Measurement: assigning numbers to characteristics of objects or people
according to a pre-specified rule.
Scaling: generation of a continuum upon which measured objects are
located.
Types of scales:
Nominal: nonmetric measurement scale
o Logistic regression (DV)
o Chi-square
Ordinal: "
Interval: metric; no absolute zero, so zero doesn't mean absence.
o Pearson correlation coefficient
o Lineair regression
Ratio: "; presence of an absolute zero point.
o Pearson correlation coefficient
o Lineair regression
Scales are used to determine which data analysis technique are the most
applicable.
Scaling techniques:
Comparative scaling techniques
o Paired comparison scaling: respondents need to select one
object (out of two) according to some cirterion.
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