Marketing Research & Social Communication Lesson 13 More Quantitative Research Ray Poynter 1 Ray Poynter, Marketing Research & Social Communication, 2015
Transcript
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 weeks quiz 2. Question from last
week 3. Samples 4. Questionnaires 5. Analysis 6. Big Picture 7.
Quiz and assignment for next week Ray Poynter, Marketing Research
& Social Communication, 2015 2
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 weeks quiz Ray
Poynter, Marketing Research & Social Communication, 2015 3
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 Ray Poynter, Marketing Research & Social
Communication, 2015 5
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 Ray Poynter, Marketing
Research & Social Communication, 2015 6
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 Ray Poynter, Marketing
Research & Social Communication, 2015 7
8. The Survey/Questionnaire Process Understand the clients
business problem Define the population and a suitable sample Create
a questionnaire Collect the data Analyse the data Present/report
the findings Ray Poynter, Marketing Research & Social
Communication, 2015 8
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. Ray Poynter,
Marketing Research & Social Communication, 2015 9
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. Ray
Poynter, Marketing Research & Social Communication, 2015
10
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? Ray Poynter, Marketing Research &
Social Communication, 2015 11
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 Ray Poynter, Marketing Research & Social Communication,
2015 12
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 Ray
Poynter, Marketing Research & Social Communication, 2015 13 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! Ray
Poynter, Marketing Research & Social Communication, 2015
14
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 Ray Poynter, Marketing Research &
Social Communication, 2015 15
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 Ray Poynter, Marketing Research & Social
Communication, 2015 16
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 Ray Poynter, Marketing Research & Social
Communication, 2015 17
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 Ray
Poynter, Marketing Research & Social Communication, 2015
18
19. 1936 USA Presidential Election Ray Poynter, Marketing
Research & Social Communication, 2015 19
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 Ray Poynter, Marketing Research & Social
Communication, 2015 20
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? Ray Poynter, Marketing Research & Social
Communication, 2015 21
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. Ray Poynter, Marketing Research & Social Communication,
2015 22
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 Ray Poynter, Marketing Research & Social
Communication, 2015 23
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 dont 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 Ray
Poynter, Marketing Research & Social Communication, 2015
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34. McDonalds use Market Data to Target Products and Services
Ray Poynter, Marketing Research & Social Communication, 2015
34
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 Ray Poynter, Marketing Research & Social
Communication, 2015 36
37. Before Next Lesson 1. Read chapters 4 and 12 from the
textbook Ray Poynter, Marketing Research & Social
Communication, 2015 37
38. Questions? Ray Poynter, Marketing Research & Social
Communication, 2015 38
39. Quiz Lesson 13 Ray Poynter, Marketing Research & Social
Communication, 2015 39 Please complete the quiz sheet Put your name
on the sheet