More Quantitative Market Research

1. Marketing Research & Social Communication
Lesson 13
More Quantitative Research
Ray Poynter
1Ray Poynter, Marketing Research & Social Communication, 2015

2. Agenda
1. Updates and last week’s quiz
2. Question from last week
3. Samples
4. Questionnaires
5. Analysis
6. Big Picture
7. Quiz and assignment for next week
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3. Updates
• Please tell me if I speak too fast 
• http://newmr.org/saitama-2015/
• Previous Quizzes – all previous quizzes, i.e.
Lesson 3 onwards, now on the website
• No dictionaries in the exam
• 70 questions, one hour, 31 July, 1pm
• Extra lesson opportunity, 24 July, 2:45-4:15
• Review of last week’s quiz
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4. Key Words
• Sample: a subset of the target population
• Representative: how similar is the sample
to the population?
• Bias: a systematic error, e.g. leading
questions or agreement bias
• Correlation: the degree to which two
variables tend to move together
• Driver Analysis: using statistics to
estimate the extent to which different
variable determine behaviour
Ray Poynter, Marketing Research & Social Communication, 2015 4

5. Sources of Quantitative Data
Quant only
– Surveys – currently the main method
– Transactional data – e.g. bank records or
purchase data
– People meters – e.g. recording TV viewing
– Usage data, e.g. web analytics
Quant and Qual
– Mobile devices
– Social media research
– Research communities
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6. Quantitative Data Collection Modes
• Online – the most common method in Japan
– Usually via Access Panels or Customer Lists
• Face-to-face
– At home or at a location
• Postal/Mail
• Mobile
– Sometimes as Online, sometimes as mobile only
• Telephone
– Often called CATI – computer assisted telephone
interviewing
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7. Quantitative Characteristics
• Larger sample sizes – typically more than
100 interviews per cell of interest
– 300 to 2000 very typical
• Closed questions
– Are you Male or Female
– Agree Strongly, Agree, Neither Agree nor
Disagree, Disagree, Disagree Strongly
– Intention to purchase where 10=definitely will buy
and 0 means definitely will not buy
• Open numerical questions
– How many rooms are there in your house?
– How old are you
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8. The Survey/Questionnaire Process
• Understand the client’s business problem
• Define the population and a suitable
sample
• Create a questionnaire
• Collect the data
• Analyse the data
• Present/report the findings
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9. Key Rules for Questions
• Participants should be able to answer them
accurately/truthfully
– In kilograms, how much rice will you eat in the
next six months?
• Participants should be willing to answer them
accurately/truthfully
– How often are you rude to other people?
• The researcher should be able to interpret
the answer
– For example, “Was the bus clean and on time?” is
a double-barrelled question. If somebody says
‘No’ it is hard to interpret.
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10. Try to Control Bias
• Reduce it where possible
– Avoid leading questions “Do you like brand A?”
=> “Which do you prefer A, B, or C?”
• Keep it consistent (the same over time)
– Keep the questions consistent, put important
questions near the start of the questionnaire, use
the same sorts of question type.
• Recognise it
– Report that people ‘say they will do’ rather than
‘they will do’,
– Understand that people normally over claim
purchase likelihood in market research
– People are more likely to agree than disagree.
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11. Types of Questions
• Demographics
– Describing the research participant, e.g. Age and
Gender
• Awareness and Usage
– What brands/items/media are participants aware of
and/or use? Includes frequency & quantity.
• Attitudes and Beliefs
– What do people think and believe, about brands or
about wider issues?
• Preference or Purchase Intention
– What do people prefer or what how likely are they to
buy something
• Satisfaction
– How satisfied/happy are people with a product or
service?
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12. Typical Sample Structure
• Screener and quota questions
– Excluding the wrong people
– Checking we have enough of the right people
• Critical tasks, e.g. overall satisfaction
• The main part of the study, e.g. usage and
attitudes
• Demographics, e.g. region and media
habits
• Final questions, e.g. open-ended question
about the survey
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13. Before Launching a Questionnaire
1. Check that the questionnaires covers all
of the research objectives
2. Check the survey is not too long
– Over 20 minutes is generally too long
– Responses tend to get worse in long surveys
3. Check the wording, spelling and logic
4. Pilot the survey – or soft launch it
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All of these steps, every time!

14. Quantitative Samples
• We use a sample to make estimates about a
population
• Every sample relates to a series of
populations
• The people in this class today relate to the
following populations
– All of the students registered for this class
– All students at the University
– All students in Japan
– All people in Tokyo
• But, the sample is not equally good for each
of these populations!
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15. The link between a
sample and population
Factors that impact the accuracy of results
from a sample in estimating the population
– The similarity of the sample and the
population – a representative sample is one
that is similar to the population
– Chance
– The size of the sample
• If 2 samples are similar in terms of quality, then the
larger sample is normally better
– The variability in the thing being measured
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16. Random Probability Sample
• This is the best type of sample
• But it is not often used in market research
– Because of cost
• Every member of the population has a
known and non-zero probability of being
selected
– For example selecting people via random
numbers
• Random probability samples are the least
likely to suffer from sampling bias
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17. Online Access Panels
• The most common method of recruiting
online research participants
• Many large panels, with 50,000 or more
people signed up
– SSI, Research Now, Toluna etc
– Macromill, AIP (Rakuten), Cross Marketing etc
• Panels are NOT random probability
samples
– Which can create bias problems
• Cost efficient and easy to work with
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18. Some of the Reasons Survey
Results can be Wrong
• The sample did not match population
• The sample was too small
• People were unable to answer the
questions accurately/truthfully
• People were unwilling to answer the
questions accurately/truthfully
• The researcher was unable to interpret the
answers appropriately
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19. 1936 USA Presidential Election
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http://bit.ly/NewMR_115

20. Analysing the Data
• Check the data is correct, the QA process
• Organise the data into a suitable format
– Gathering other relevant information
• Find the total picture
• Expand the total picture
• Create a story that answers the research
questions / business objectives
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21. Checking Survey Results
• What was the response rate?
– The % of people invited who completed the
survey
• Does the sample match the specification,
e.g. males and females
• Were any questions not answered?
• Do the open-ended questions suggest
problems?
• Do the totals make sense?
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22. Coding Open-ended Data
• Open-ended questions in a survey can be
turned into quantitative information by coding
– “I liked the red bottle” might be coded as ‘Colour’
• Sentiment analysis is a special type of coding
– Using the codes Positive, Negative or Neutral
• Humans versus machines
– Humans are currently more accurate than
machines at coding
– Machines/software are typically faster and
cheaper than people.
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23. Perceptual Maps
• Tries to express a market in 2 dimensions
• Usually based on quantitative data
• It is always a simplification
– But sometimes a useful simplification
• Key questions
– What market? (e.g. which country)
– What data?
– What has been left out?
• Design
• Statistically
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24. Ray Poynter, Marketing Research & Social Communication, 2015 24
https://strategicthinker.wordpress.com/perceptual-map/
What country?
What data?
What has been left out?

25. Ray Poynter, Marketing Research & Social Communication, 2015 25
What country?
What data?
What has been left out?

26. Correlation
Measures the extent to which two characteristics
move in association
Represented by the letter r
Range
+1  perfectly correlated
0  no correlation
-1  perfectly negatively correlated
Correlation does NOT imply causation

27. Correlations
Positive
correlation
r close to +1
Negative
correlation
r close to -1
No correlation
r close to 0

28. R-squared
If we square the correlation coefficient r
– we get r-squared (r2)
– also known as the variance
If X and Y have an r of 0.7
– then the r2 is 0.49
– or, 49% of their variance is shared
– and 51% of their variance is not shared
– Note r-squared of 49% could be r = -0.7
If relationships are strong and impressive
– they are usually quoted as r-squared
– sometimes in % format

29. Beware the third force!
If X is correlated with Y, then
– X causes Y
– or Y causes X
– or they are both affected by some other factor, Z
– or they influence each other
– or its just chance!
Sales of Oranges in Peru are correlated with sales of cars
in UK!!!!
– both increases are driven by increases in
• wealth
• population
– there is no ‘real’ link between them

30. Ray Poynter, Marketing Research & Social Communication, 2015 30
http://www.tylervigen.com/spurious-correlations

31. Uses of Correlation
• To assess interactions between attributes
• To assess the quality of estimates or
predictions
• To identify associations between
phenomena
– For example between weather and and
choice of transport mode
• Driver analysis*

32. Ray Poynter, Marketing Research & Social Communication, 2015 32
Transport Choices - Netherland
The Impact of Weather Conditions on Mode
Choice: Empirical Evidence for the Netherlands
Muhammad Sabir, Mark J. Koetse and Piet Rietveld
Causal link,
weather on choice
of bike or car

33. Driver Analysis
Do you choose a convenience story because it is
friendly, has a good range, is cheaper, is more
convenient, has better lighting?
– The answer is people don’t know the real values that
underpin their actions
Driver analysis uses mathematics to analyse what
factors seem to be associated with your choices
– Ideally, causally related with your choices
– For example in the travel data from the Netherlands, it
looks as though almost 40% cycle when the weather is
over 25°, nearly 50% of this number is driven by the
weather, and just over 50% is determined by other factors
Driver Analysis seeks to understand why people do
things – what factors ‘drive’ or determine their choices or
behaviour
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34. McDonald’s use Market Data to
Target Products and Services
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35. Key Words
• Sample: a subset of the target population
• Representative: how similar is the sample
to the population?
• Bias: a systematic error, e.g. leading
questions or agreement bias
• Correlation: the degree to which two
variables tend to move together
• Driver Analysis: using statistics to
estimate the extent to which different
variable determine behaviour
Ray Poynter, Marketing Research & Social Communication, 2015 35

36. Big Picture
1. Quantitative is all about measuring
2. Remember Numbers and Tables (QaNTitative)
3. A good sample is representative of its population
4. Questions need to:
a. Help organisations make better decision – i.e. link to
the business objectives
b. Be understood
c. Be capable of being answered truthfully and
accurately
d. Be likely to be answered truthfully and accurately
e. Generates answers that are capable of being
understood
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37. Before Next Lesson
1. Read chapters 4 and 12 from the
textbook
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38. Questions?
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39. Quiz Lesson 13
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