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DCI Flashcards

1
Q

Define market research
+
Basic Process

A

The systematic and objective process of generating information that aids in making marketing decisions involving:

  • Specifying the information required to address market issues
  • Designing the method for collecting information
  • Managing and implementing the data collection process
  • Analysing the results
  • Communicating the findings and their implications
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2
Q

Importance of market research / value

A
  1. Business Component
    (marketing management needs research, manager’s need for knowledge & business decisions, reduce uncertainty of marketing strategies and tactics)
  2. Social / welfare component
    (Social marketing, broader societal issues)
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3
Q

Importance of market research / why

A
  1. Identifying and evaluating opportunities (market envionment - opportunities and threats)
  2. Analysing and selecting target markets
    (segment characteristics, purchase motivations)
  3. Planning and implementing marketing mix
    (product, price, place, promotion)
  4. Analysing marketing research
    (monitor performance)
  • Increasing access to big data
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4
Q

Market research process

A
  1. Research problem & Scope (research questions & objectives)
  2. Method: qual, quant or both - consider secondary data
  3. Collect data from right group
    (e.g sample, size, probability/non-probability)
  4. Analyse data, conduct test (SPSS, NVIVO)
  5. Communicate results
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5
Q

Difference between Qualitative and Quantitative research: Qualitative Research

A

Qualitative
- Initial discovery / explore
- Understand
- Words, visuals, open-ended, probing questions
- Interviews, focus groups, observations
- Semi-structured questions usually, order doesn’t matter as much
- Small samples
- Limited generalisability

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6
Q

Difference between Qualitative and Quantitative research: Quantitative Research

A
  • Certainty
  • Confirmation/assessment
  • Explain: descriptive or causal
  • Numbers, measures, scales
  • Surveys
  • Structured, ordered questions
  • Larger sample
  • Samples representative of population / inferences
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7
Q

Focus of qual VS focus of quant

A

Qual
- Insights into preferences, experiences, motivations
- Understand customer journey
- Test new ideas / products
- Develop questions for surveys

Quant
- Assess attitudes, product usage, recall, likelihood of purchase, market potential
- Consumer segmentation
- Test hypothesis and theories
- predict behaviour

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8
Q

Advantages and disadvantages of qualitative research

A

Advantages
- Rich data
- Preliminary insights into behaviours and attitudes
- Preliminary frameworks / how variables are related

Disadvantages
- Lack of generalisability
- Inability to quantify, measure differences
- Insights depend on researcher’s skills and training

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9
Q

Tools of qualitative research - focus group adv and dis

A

Small group w/ moderator

Adv
- can be quick
- gain multiple perspectives
- Flexibility
- Inexpensive
- contrast opinions
- can demonstrate a concept / conduct exercise

  • Synergy
  • Snowballing (1 comment -> responses)
  • Serendipity (group idea generation)
  • Security
  • Spontaneity
  • Structure
    -Scientific scrutiny through observers and recordings

Dis
- Results do not generalise
- difficult for sensitive topics
- difficult for people with busy schedules or want a specific person

  • Require sensitive and effective moderators
  • Participants may dominate conversation
  • social bias
  • sampling issues
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10
Q

Tools of qualitative research - depth interviews

A

One-on-one interview, probing questions

Adv
- Considerable insight
- Understand unusual behaviours

Dis
- Results do not generalise
- Very expensive per interview

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11
Q

Tools of qualitative research
- semi-structured interviews

A

Open-ended questions

Adv
- Can address more specific issues
- Results can be easily interpreted
- More cost-effective than focus groups and depth

Dis
- Lack flexibility that is likely to produce truly creative or novel explanations

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12
Q

Applications of interviews and focus groups

A
  • Test advertising / integrated marketing communications
  • Generate new ideas about a product or delivery method
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13
Q

Advantages of interviews

A

Adv
- Gain deeper insight from each individual
- Good for understanding private or unusual behaviours
- Can cover sensitive topics
- Respondents are not influenced by others
- More flexibility on time and location

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14
Q

Writing appropriate interview questions

A
  • avoid yes/no questions
  • minimise researcher bias / leading questions
  • based off research objectives
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15
Q

Question order

A
  • Intro -> topic A -> topic B -> conclusion
  • introductions and warm up
  • Grand tour questions (general to specific)
  • Floating prompts (encouragement deliberate, concrete examples)
  • demographic questions
  • information sheet, consent
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16
Q

Role of the researcher

A
  • know what will be said
  • be prepared to ask follow up questions
  • Listen more than talk
  • Active listening
  • probing and clarifying questions
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17
Q

What is qualitative analysis

A
  • Nonnumerical explanation and interpretation of observations
  • seek to understand phenomena, behaviour, thoughts, emotions
  • equally art as science
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18
Q

Themes vs codes

A

Code
- Identifying themes in accounts and attaching labels (codes)
- process to get to theme
- can also be sub-themes

Theme
- Features of participants’ accounts

NVIVO: Node groups, and can be both

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19
Q

Conducting qualitative analysis

A
  1. Organise data
  2. Develop coding framework
  3. Allocate data into framework
  4. Interpret the data (core data and theory)
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20
Q

Organising and collating qualitative data

A
  • Transcribe interviews
  • Clean interviews
  • Records of interviews
  • De-identify data
  • data to form it can be manipulated on NVIVO
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21
Q

Discovering patterns in in qualitative data

A
  • frequencies
  • magnitude
  • structures
  • processes
  • causes
  • consequences
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22
Q

Process of qualitative analysis

A
  • Coding
  • Annotating (draw attention to most important)
  • Labelling (grouping data)
  • Selection (choose important items)
  • Summarising (one or more examples to illustrate findings)
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23
Q

3 coding framework

A
  • Open coding
    (initial classification and labelling)
  • Axial coding
    (reanalysis, relationships between codes, general concepts)
  • Selective coding
    (central concepts)
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24
Q

NVivo process

A
  1. Clean transcript
  2. Import transcript
  3. Create nodes -> open coding
    – highlight and drag text to the nodes and label
    – or right-click, new node
  4. Axial coding
    (go through codes, common concepts, start grouping)
    – drag node onto another
  5. Selective coding
    (refine into core categories)
  6. Begin visualising
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25
Q

NVivo tools for visualising

A

Word frequency (Mind maps, word maps, word trees)

Hierarchy chart
Bar chart
Concept map

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26
Q

Interpreting qualitative research

A
  1. Capture and share information
  2. Find themes
  3. Making sense of findings
    (links between themes, dig deeper?)
  4. Define insights
    (what was surprising, connect with business problem)
    - insight statement -> rephrase theme into statement
  5. Evaluating themes / insights
    - useful, innovative, explain, resonate, address problem
27
Q

Visualising qualitative research

A

– canva, NVivo, worditout

  1. Infographics
  2. Quotes
    (voice of customer, support findings)
  3. Word Clouds
    (importance of certain words)
  4. Word Trees
    (How one word is connected to others)
  5. Conceptual framework (e.g processes - think funnel / customer journey)
28
Q

Qual vs Quant research design

A
  1. Interview guide (qual) vs questionnaire / survey (quant)
  2. Semi-structured, open-ended (qual) vs closed, ratings
  3. small sample size - 30 or saturation (qual) vs large - 100 or more (quant)
  4. Words, sentences, text, images (qual) vs variables, constructs (quant)
29
Q

Advantages and disadvantages of surveys

A

Advantages
- Large samples = generalisability
- Estimates differences
- Easy to administer and record answers to structured questions
- Can assess abstract concepts and relationships

Disadvantages
- Questions to accurately measure attitudes and behaviour can be hard to develop
- Data from open-ended (?) questions may not be enough to record all concepts -> can’t probe
- Response rate
- Over-surveying

30
Q

Building survey questions

A

Option 1: Adapt items from secondary research
- change wording
- Item removal / addition

Option 2: Use qualitative research
- usually to justify variables, adapt scales

31
Q

Scale vs item

A

Scale = set of items that measures something

Item = component of a scale, or singular statement that measures something

32
Q

Variable vs construct

A

Variable = Observable attribute of an object, measurable, one item

Construct = Abstract, set of related questions

33
Q

Survey questions wording (what to avoid and aim for)

A

Avoid:
- Jargon / slang / abbreviations
- Ambiguity / confusion
- Emotional language
- Double-barrelled questions
- Leading questions

Aim for
- understandable
- useful
- explanations of concepts

34
Q

Different levels of measurement

A

Nominal, ordinal, interval, ratio

35
Q

Nominal data

A
  • basic level
  • values or categories with no quantitative value
  • assigned number codes but still no order
36
Q

Ordinal

A
  • meaningful order
  • distance between answers may not be equal
  • include likert scales
37
Q

Interval data

A
  • Distance is meaningful
  • differences between answers can be measured not just classified
  • scemantic scales 1,2,3,4,5 etc.
38
Q

Ratio data

A
  • Distance is meaningful
  • Measurable
  • There is a true zero value
39
Q

Probability vs non-probability sampling

A

Probability
- Every member of population has known and equal chance of being chosen.
- Selection is based on a chance
- no error related to researcher
judgment in selecting respondents
- standard error and confidence intervals can be calculated
- generalisations can be made

Non-probability
- no population list
- every member of pop. does not have known and equal chance of being chosen
- sampling error not known
- limited generalisations
- units selected from personal judgement or convenience
- can be bias

40
Q

Types of probability sampling

A
  1. Simple random sampling
  2. Systematic sampling
  3. Stratified sampling
  4. Cluster sampling
41
Q

Types of non-probability sampling

A
  1. Convenience sampling
  2. Judgement (purposive) sampling
  3. Quota sampling
  4. Snowball sampling
42
Q

Simple random sampling

A

Probability
RNG, all in a hat

43
Q

Systematic sampling

A

Probability
Select members at a regular interval from a sampling frame (e.g every 4)

44
Q

Stratified sampling

A

Probability
Random sampling from sub-groups (e.g gender)

45
Q

Cluster sampling

A

Probability
Segment into geographic areas
Random cluster chosen

46
Q

Convenience sampling

A

Non-probability
Units most conveniently available
try to have people from different households

47
Q

Judgement (purposive) sampling

A

Non-probability
Based on researcher’s judgement on who would be best

48
Q

Quota sampling

A

Non-probability
Ensures representation of certain subgroups
e.g Males 50%, females 50%
- bias -> may not be representative of actual population

49
Q

Snowball sampling

A

Non-probability
Initial respondents selected with random, but additional respondents obtained from these
e.g referrals

50
Q

Independent samples t-test

A

Test significant differences in rating between two groups

Nominal (2 categories) by metric (ordinal, interval, ratio)

51
Q

Independent samples t-test in SPSS

A

Analyse, compare means, independent samples t-test, categorical grouping variable, metric test variable, assign codes to grouping variables (e.g female = 1)

52
Q

Independent samples t-test interpretation of output

A

Independent samples t-test
1. Levenes >0.05, equal variances assumes - top row
< 0.05, not assumed, bottom row

  1. sig (2-tailed): >0.05, not sig
    < 0.05, sig

Group statistics
3. Look at means

53
Q

Paired samples t-test

A

Tests differences between two variables for the same group

54
Q

Paired samples t-test in SPSS

A

Analyse, compare means, paired samples t-test, select variables of interest

55
Q

Paired samples t-test interpretation of output

A

Paired samples test
1. sig (2-tailed)
Paired samples statistics
2. Compare means (which is higher, lower)
- can also look at mean in paired samples test

56
Q

Visualising tests of difference

A

bar graphs
pie charts
infographics

57
Q

Association between variables

A

Presence of association - level of significance
Direction of association
Strength of association - correlation coefficients

58
Q

Strength of associations

A

Non-existent - r=.00
Weak (small) - r = 0.01-0.4
Moderate (medium) - r = 0.41-0.6
Strong (large) = r = 0.61-1

59
Q

Pearson’s correlation

A

Interval / ratio variables

60
Q

Spearman’s correlation

A

Ordinal by ordinal/interval/ratio

61
Q

Correlation SPSS process

A

Analyse, correlate, bivariate, tick either spearman or pearson

62
Q

Correlation SPSS output interpreation

A

Correlations
1. sig (2-tailed)
> 0.05 = no correlation
< 0.05 = correlation

  1. Correlation coefficient
    r = __ (weak, moderate, strong)
63
Q

Tests of association visualisation

A

Balls, arrows, numbers
scattergraph can be difficult for managers to interpret

DCI (2 decks)

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