1. 1. Marketing Research & Social Communication Lesson 13 More Quantitative Research Ray Poynter 1Ray Poynter, Marketing Research & Social Communication, 2015
2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. 1936 USA Presidential Election Ray Poynter, Marketing Research & Social Communication, 2015 19 http://bit.ly/NewMR_115
20. 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. 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. 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. 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. 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. 25. Ray Poynter, Marketing Research & Social Communication, 2015 25 What country? What data? What has been left out?
26. 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. 27. Correlations Positive correlation r close to +1 Negative correlation r close to -1 No correlation r close to 0
28. 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. 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. 30. Ray Poynter, Marketing Research & Social Communication, 2015 30 http://www.tylervigen.com/spurious-correlations
31. 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. 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. 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 33
34. 34. McDonalds use Market Data to Target Products and Services Ray Poynter, Marketing Research & Social Communication, 2015 34
35. 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. 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. 37. Before Next Lesson 1. Read chapters 4 and 12 from the textbook Ray Poynter, Marketing Research & Social Communication, 2015 37
38. 38. Questions? Ray Poynter, Marketing Research & Social Communication, 2015 38
39. 39. Quiz Lesson 13 Ray Poynter, Marketing Research & Social Communication, 2015 39 Please complete the quiz sheet Put your name on the sheet