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ETH1: 2.3 i2marketing > L3 Marketing Research > Flashcards

L3 Marketing Research Flashcards

1
Q

Data:

  • implication of being quantifiable
  • conseq of quantity
A
  • does not equal important
  • there’s a lot -> find the meaningful one, w a theory
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2
Q

Data:

  • Primary VS secondary
  • Internal VS external
A
  • collected for the study at hand VS for other purposes
  • collected by company itself VS by others
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3
Q

The 3 main steps of data-related research projects

A
  1. theoretical concept (research Q in a broader picture)
  2. operationalization = define n measure
  3. data (collection)
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4
Q

prevalence of unknown parameters in marketing analysis: why? solution?

A

Coz we lack data from other org units, or due to confidentiality. > use marketing intelligence data = census & industry reports

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5
Q

5=1+3+1 reasons for Marketing Research:

A

best one:

  • Base for taking decisions

3 political PrPrCs:

  • Prestige
  • PR
  • Consensus seeking

worst one:

  • procrastinating decisions
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6
Q

3 Reactions to MR in short n 3 in long term,
across the 3 same dimensions

A

short term: gradual adaptation f comm, easy to chg features, repositioning

Long term: new biz strat, new product, new market…

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7
Q

How to discover consumers’ needs?

1=4+2 methods
+ 8 techniques

A

with qualitative marketing research e.g.:

  • short term:
    • Focus groups –> test new product in dev.
    • In-depth interviews –> get new product ideas
    • problem-centered interviews
    • observation
  • long term:
    • Delphi method –> find (tech) long term changes
    • weak signal research –> predict uncertain future
  • observation

+ 6 techniques:

  • Critical incidents
  • Laddering (why? why? …)
  • Brainstorming
  • Free association
  • Collages
  • “Planet-game”
  • text analysis
  • projective techniques:
    • ​Thematic Apperception Tests
    • word association test
    • sentence completion test
    • third person techniques
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8
Q

Research design 3 design (type)s + their methods

A
  • Exploratory design < qualitative
  • Descriptive < qualitative n quantitative (panels, surveys, secondary data)
  • Experimental design < test hyp. w experiments (the only really scientific type according to some)
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9
Q

Lab vs field experiments: tradeoffs

A

Controlling all variables vs realism

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10
Q

weakness of descriptive research

A

“correlation does not imply causation”

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11
Q

to infer causal relationship X>Y in research, 3 conditions must be satisfied

A
  1. X & Y happen together
  2. X does not happen after Y
  3. other possible causes are excluded
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12
Q

Experiment - Variables, 3 types

A
  • independent Vs > get manipulated
  • dependent Vs > presumably affected n observed
  • extraneous Vs > all other Vs that could presumably affect result
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13
Q

Experimental designs:
2 dims w 2 values each

A

(quasi- aka natural experiment VS experiment)

-> randomization, w control group, is NOT vs YES possible

x

(field VS laboratory)

-> realistic: high reliability = few confounding factors & construct stableness

VS

controlled: high external validity = generalizability & stability across different contexts

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14
Q

4=1+3 main differences bw qual & quant R

A

typical goal:

  • goal: dev initial understanding vs recommend final decision
  • properties:*
  • unstructured vs structured
  • non-statistical vs statistical
  • Nrs: small vs big
    • that’s why C-level mgrs often prefer quant R
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15
Q

Delphi method:

def

limit

+ 2 strengths

  • 2 weaknesses
A

iteratively sending questionnaires to experts

but are experts really better than normal ppl in their knowledge of reality / future?

+ 2 Strengths

  • No need to bring experts together physically
  • No focus groups or discussion effects

- 2 Weaknesses

  • Hard to retain panelists
  • Future developments not always predicted correctly by iterative consensus
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16
Q

focus groups VS in-depth interviews:

2 vs 2

A
  • i-d-Is are more structured
    n discover more n richer info;
  • focus Gs yield more innovative info
    n are less subject to interviewer bias
17
Q

1+1+1=2 criteria to judge quantitative R

A
  • objectivity > diff. investigators would reach same conclusions
  • reliability > accuracy of measurements = w random errors, but free from systematic errors
  • construct validity >
    • internal: we really measured what we wanted? is causation warranted?
    • external: can results be generalised?
18
Q

research 6 steps:

A

The basic research process includes
2+2+2: pro.des.D.S.A.R.

2 conceptual choices

  1. defining the problem
  2. determine research design

​2 data collection

  1. design data collection method and form
  2. design sample and collect data

​2 result elaboration

  1. analyze and interpret data
  2. prepare research report
19
Q

explain 3 main research designs:

A
  • Exploratory research design 1. generates first insight, 2. deepen understanding of a problem, and 3. clarifies issues from quantitative research – no statistical tests
  • Descriptive research design describes the population as it is, includes large set of variables – w statistical tests
  • Experimental research design measures causal effect of independent on dependent variables – w statistical tests
20
Q

3 options for sampling (mostly in descriptive research):

A
  • Cross-sectional design
    • Single vs.
    • Multiple cross-sectional design​
  • vs. longitudinal design
21
Q

Quasi-experiment vs. (true) experiment:

A

Quasi-experiment vs. experiment: both can be field or laboratory, but the former lacks random assignment of treatment or control group

22
Q

A/B split testing:

A

A/B Testing: Method used in a randomized experiment with two variants, A and B, which are the control and variation (aka treatment) in the controlled experiment

23
Q

the 3 criteria to assess the quality of quantitative research:

A
  • Objectivity ~ standardize to avoid observer bias
  • reliability ~ re-test to ensure absence of error
  • construct validity ~ have we really measured what we were trying to measure?
24
Q

Questionnaire design process

in 9 = 2 + ( (2 + 2) + 2) + 1 steps

A
  1. Specify the information needed
  2. Specify the interview method
  3. Determine the content of individual questions
  4. Overcome the respondents’ inability or unwillingness to answer questions
  5. Choose question structure
  6. Choose question wording
  7. Arrange questions in proper order
  8. Identify the form and layout
  9. Eliminate problems by pilot-testing
25
Q

quantitative research:

5=2+2+1 data analysis methods

A
  • T-Test: statistical examination of two means
  • ANOVA: comparing two, three, or more means
  • Regression analysis: modeling the relationship between a dependent variable Y and one or more explanatory variables X (e.g. multiple regression, logistic regression)
  • Factor analysis: aggregate observed, correlated variables to a lower number of unobserved variables called factors
  • Cluster analysis: sorts raw data into groups of relatively homogeneous cases or observations called clusters
26
Q

in-depth interview:

description

+ 4 strengths

  • 4 weaknesses
A

Description

An extended discussion between a respondent and an interviewer based on a brief interview guide that usually covers 10-30 topics

  • Open-ended question explored in depth
  • Key informants, opinion leaders, lead users …
  • Typically 10-15 interviews
  • If target group is diverse, 5-10 interviews per subgroup
  • often using laddering (why? why? …) based on Means-End Chain Theory

+ 4 Strengths

  • Unexpected insights or new ideas
  • Helps create trust between interviewer and respondent
  • Less intrusive than questionnaire
  • Useful with illiterate respondents

- 4 Weaknesses

  • Time-consuming compared to structured questionnaire
  • Strong interviewer bias
  • Well-trained interviewers required
  • Skills required for data analysis
27
Q

methods in research designs:

4+4+1

A
  • *Exploratory design: ​**–> discover new insights
  • Mostly qualitative, sometimes quantitative*
  • Expert surveys
  • Pilot surveys
  • Secondary data
  • Qualitative research
  • *Descriptive Design:** –> describe characteristics
  • Both qualitative and quantitative*
  • Secondary data
  • Surveys
  • Panels
  • Observation
  • *Experimental Design:** –> determine causal relationships
  • Mostly quantitative, sometimes qualitative*
  • Experiments
28
Q

sources for descriptive research in 2 dims:

A

Internal

  • primary
    • Employee surveys
    • Salesmen surveys
  • secondary
    • Internal Statistics
    • Financial Accounting
    • Cost Accounting
    • Sales Data
    • Customer Database

External

  • primary
    • Surveys to Customers
    • Shareholders
    • Partners
    • the general public
  • secondary
    • Census Data
    • Publicly Available Statistics
    • Published Studies
    • Journals and Newspapers
    • Industry Association Reports
    • Panel Data
29
Q

Experiment: def w 3 variables types

A

An experiment is formed when the researcher

  • manipulates one or more independent variables
  • and*
  • measures their effect on one or more dependent variables,
  • while*
  • controlling for the effect of extraneous variables.
30
Q

qual VS quant reserch:

5 diffs

A

Objective:

  • To gain a qualitative understanding of the underlying reasons and motivations
  • *VS**
  • To quantify the data and generalize the results from the sample to the population of interest

Sample:

  • Small number of non-representative cases (theoretical sampling)
  • *VS**
  • Large number of representative cases (statistical sampling)

Data Collection:

  • Unstructured
  • *VS**
  • Structured

Data Analysis:

  • Non-statistical
  • *VS**
  • Statistical

Outcome

  • Develop an initial understanding
  • *VS**
  • Recommend a final course of action
31
Q

focus groups:

description

+3 strengths - 4 weaknesses

A

Description

  • 8-12 homogeneous participants
  • 1-3h in relaxed atmosphere
  • moderator with good interpersonal skills, carefully asking carefully prepared open questions, first uncued then cued

+ 3 Strengths

  • Provides concentrated amounts of rich data, in participants’ own words, on precisely the topic of interest
  • Interaction of participants adds richness to the data that may be missed in individual interviews
  • Provides critical information in development of hypotheses or interpretation of quantitative data

-4 Weaknesses

  • Small number of participants
  • Limited generalizability
  • Group dynamics can be a challenge, particularly if moderator is inexperienced, moderator bias
  • Interpretation is time-consuming and requires experienced analysts
32
Q

weak signal research:

description in 2 points

A
  • Identification of weak signals =
    First tendencies in certain areas of the environment which could lead to a trend (but with high uncertainty)
  • Who is typically involved? A small group of open-minded and strategically thinking marketing researchers, jointly with respective experts
33
Q

quantitative research:

3 purposes

5 approaches

A

3 Purposes

  • Producing hard facts and statistics
  • Generalize results of samples to population of interest
  • Create basis for decisions (price, product mix, market attractiveness etc.)

5 Approaches

  • Mostly in Descriptive and Experimental Research
  • Formulating hypotheses
  • Data collection:
    Asking people for their opinions in a structured way (surveys by questionnaires etc.)
    OR
    Gaining data from secondary sources (internal statistics, panels, published surveys etc.)
  • Apply mathematical models to hypotheses pertaining to phenomena
  • Interpreting the results of hypotheses testing
34
Q

the 3 research designs compared in short

A
  • Exploratory research design generates first insight, deepen understanding of a problem, and clarifies issues from quantitative research – no statistical tests.
  • Descriptive research design describes the population as it is, includes large set of variables – statistical tests
  • Experimental research design measures causal effect of independent on dependent variables – statistical tests

ETH1: 2.3 i2marketing (8 decks)

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