Understanding Markets

1. Marketing Research & Social Communication
Lesson 8
Understanding Markets
Ray Poynter
1Ray Poynter, Marketing Research & Social Communication, 2015

2. Agenda
1. Reviewing material so far
2. Understanding Markets
3. Quant approaches
4. Qual approaches
5. Social media and big data
6. Quiz and assignment for next week
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3. Review of Previous Material
• Quantitative – Quant – numbers and tables
– Surveys, transactional data, web analytics, audits, meters, etc.
– Measures things
• Qualitative – Qual – language
– Focus groups, depth interviews, ethnography, accompanied
shopping, etc.
– Explains things
• Social Media Research
– Quant and Qual
– Answers unasked questions
• Previous Quizzes – all previous quizzes, i.e. Lesson 3
onwards, now on the website
• Website address http://newmr.org/saitama-2015/
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4. Key Words
• Segmentation: Dividing a market into
smaller groups, for example, people
motivated by prices vs people motivated by
style.
• Cluster Analysis: A method of using
mathematics to put people into groups.
• Motivations: Why people do things and what
drives the choices they make.
• Correlation: The extent to which two things
are related to each other.
• Driver Analysis: Estimating the causes of
choices.
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5. Word and Sign Check
• Mean
• Median
• Standard deviation
• Normal Distribution
• Correlation
• <
• >
• <=
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6. Understanding Markets
• Awareness and usage of brands or
services
– Including where people buy, when and how
they use products
• Motivations for product/service selection
• Unmet needs and aspirations
• The differences between customers
– Differences between customers and non-
customers
– Differences between some customers and
other customers
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7. Sources of Information
• Published reports including government
data
• Proprietary data, e.g. sales information
• Quantitative research, e.g. surveys
• Social Media
• Qualitative research, to find deeper
messages
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8. Official Data
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9. Proprietary Data
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10. Brandz Database
from Millward Brown
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11. Typical Survey Projects
• Awareness: Who has heard of what?
• Usage: Who uses what?
• Purchase: Who buys what, where do they
buy it, how do they pay for it, what do they
pay?
• Segmentation: Are there groups of people
who are different?
• Analytics: What are the associations and
relationships in the data?
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12. A day in the life
1578 beverages
400 consumers
1 day
Mobile
Diary
UK, Vision Critical, 2013

13. Diary framework
BEVERAGES
Who?
What?
Why?
When?
Where?
What
else?

14. Mobile interface

15. What and when?
0%
20%
40%
Before 7am 7am-9am 9am-11am 11am-1pm 1pm-3pm 3pm-5pm 5pm-7pm 7pm-9pm After 9pm
Coffee
Tea
Fruit Juice
Fizzy drink
Energy Drink
Water
Alcoholic drink

16. Where at home?
Kitchen
Living room
Dining room
Bedroom
Bathroom
Somewhere else
at home
Kitchen
Living room
Dining room
Bedroom
Bathroom
Garden / yard
Somewhere else
at home
Men Women

17. Segmentation
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18. Ray Poynter, Marketing Research & Social Communication, 2015 18
http://troubadourconsulting.com/2014-holiday-shopping-survey-infographic/

19. Ray Poynter, Marketing Research & Social Communication, 2015 19
http://troubadourconsulting.com/2014-holiday-shopping-survey-infographic/

20. Segmentation
Usually conducted using ‘Cluster Analysis’
– Although there are other alernatives
Cluster analysis
– Mathematical technique
– Takes respondent survey data
– Produces groups of people who are:
• Similar to people in the same group
• Different to people in other groups
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21. McDonald’s use Market Data to
Target Products and Services
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22. 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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23. Ray Poynter, Marketing Research & Social Communication, 2015 23
https://strategicthinker.wordpress.com/perceptual-map/
What country?
What data?
What has been left out?

24. Ray Poynter, Marketing Research & Social Communication, 2015 24
What country?
What data?
What has been left out?

25. 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

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

27. 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

28. 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. If you get causation, this is funny 
I used to think correlation implied
causation, then I went on a course
and now I don’t
So, the course helped
then?
Not necessarily.

32. 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*

33. Ray Poynter, Marketing Research & Social Communication, 2015 33
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

34. 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
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35. Qualitative Analysis
• Accompanied shopping: e.g. how do
people navigate through complicated
market
• Ethnography: e.g. staying with a family to
understand motivation of how cooking
ingredients purchased and used
• Focus group: e.g. discussion of what
washing products used for what purposes
• Depth interview: e.g. exploring how
financial products perceived and the
motivation for selection
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36. Social Media Research
• Used for qual and quant research
• Quant:
– Tracking discussions about brands and
services
– Interactions with brands and services
• Qual:
– In depth reviews, for example Trip Advisor
and hotels
– Searching unmet needs, like the Nivea
deodorant
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37. Big Data
• Loyalty card data (e.g. points cards),
answering who buys what, when, where, and
with what other products
• Transactional and passive data, also
answering who buys what, where, when and
how, but also showing what advertising they
saw
• Metered data with financial data with
geolocational data, creating a 360° of the
customer
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38. Analysis of Homework Data
During the class
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39. Key Words
• Segmentation: Dividing a market into
smaller groups, for example, people
motivated by prices vs people motivated by
style.
• Cluster Analysis: A method of using
mathmatics to put people into groups.
• Motivations: Why people do things and what
drives the choices they make.
• Correlation: The extent to which two things
are related to each other.
• Driver Analysis: Estimating the causes of
choices.
Ray Poynter, Marketing Research & Social Communication, 2015 39

40. Big Picture
1. Market research is used by companies to
explain markets in terms of
– What is happening
– What is causing it to happen
– What needs are met
– What needs are unmet
2. Most of the MR used to understand markets
is quant (putting numbers to things)
– But qual is also important
3. Key tools include segmentation and
correlation
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41. Before Next Lesson
1. Read Chapter 12 of the textbook
2. In groups of 3 or 4, find out some
information about the Japanese kombini
market from – you will have up to five
minutes to report back
– Published sources
– Social Media
– Anywhere else
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You can present using the board, PowerPoint, Word, or anything else that is likely to work.
If you want to present on the screen, bring your file on a data stick.

42. Questions?
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43. Quiz Lesson 8
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Please complete the quiz sheet
Put your name on the sheet