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  • 2 december 2024
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Samenvatting Consumer Research

Readings week 1:
Chrysochou, P. (2017). Consumer behavior research methods. Consumer
Perception of product risks and benefits, 409-428.
- make sure you know the pros and cons of all these methods, but especially
understand why these are pros and cons based on things we learn in course.


Kohavi, R., & Thomke, S. (2017). The surprising power of online experiments.
Harvard business review, 95(5), 74-82.
- Explain well why companies could benefit from experiments, give a good
example, and get used to the terms being used in the field of business
experiments (we won’t ask for definitions).

Slides week 1: Introduction to consumer research
Historical background: The field developed alongside marketing, initially focusing on
sampling, data collection, and analysis.
- Early research measured perceptions, attitudes, preferences. Focus shifted to
understanding the meaning behind data and informing marketing strategies.

Consumer research methodologies: Types of data:
- Primary data: collected specifically for current research question. Could be
internally or externally collected
➔ Examples: Experiments, Test Markets, Focus Groups, Surveys,
Observations, Interviews, Physiological, etc.
- Secondary data: Collected for some other purpose
• External (census, scanner data, Nielsen data, Facebook Topic Data)
• Internal (company records, sales data)
Examples:
➔ Scanner panels (AiMark): Often a database of purchases (e.g., grocery
purchases). Combined with demographics + more. Can see influences of
marketing mix (e.g., promotions) and other factors on sales of individual
products, brands, etc.
➔ Google trends
➔ Social Listening: look at mentions and lokes on social media. Most (small)
companies outsource their “social listening” or “social media monitoring
programs (Salesforce, Talkwalker, Hootsuit)
➔ Targeted ads: Best practices: Don’t be obnoxious, Allow people to opt out,
Careful jumping devices & sensitive products
Problems:
• Often incomplete. “Garbage in, garbage out”
• Backward looking, and may be out of date or not specific
• Definitions or categories might not be what you’re looking for
• Doesn’t get at causality
Fine to look into secondary data first, but then turn to primary data

The six steps of consumer research
1. Research Objectives: Define objectives, research questions, and hypotheses.
2. Research Design: Choose the appropriate method and design.

, 3. Sampling: Identify the population and create a sampling plan.
4. Data Collection: Develop data collection instruments and gather data.
5. Data Analysis: Analyze data to address research questions.
6. Reporting: Document and share results with stakeholders.
Qualitative Quantitative
Exploratory - In-depth interviews - Observational
→ Generate ideas - Focus groups

Descriptive - Case studies - Content Analysis
→ Generate numbers & - Surveys
relationships - Data mining
- Physiological
Causal - Experiments, test markets
→ Assesses Cause- (quasi-experiment)
Effect relationships

In-Depth Interviews: Conducted face-to-face to explore topics in detail. Vary in
structure and direction. Techniques:
- Laddering: Uncovers deeper motivations (Why is that important to you?)
- ZMET: Uses images to express thoughts and feelings.

Focus Groups: Participants discuss a marketing problem, guided by a moderator.
Typically involves 6–10 participants.
Advantages: Encourages interaction and rich data generation.
Risks: Misleading generalizations from small sample sizes.

Observational Research: Observes consumer behavior naturally
- Natural (e.g., home, store) vs. artificial (e.g., lab) settings
Most useful when investigating complex social settings; less useful for studying well-
defined hypotheses under specific conditions
- Combines observation with interviews or ethnographic techniques.

Survey Research: Structured questionnaires for broad population insights.
- Advantages: Measures attitudes, knowledge, brand awareness, satisfaction,
- Disadvantages: Subject to biases and errors & can measure correlations but
not causation & many sources of error
Problems with surveys→ Net Promoter Score (NPS)
- A widely used question in surveys to measure customer satisfaction
and loyalty Critical Question: What Does NPS Actually Measure?
NPS provides a simple, directional indicator of customer sentiment and intent to
recommend but is not a definitive measure of customer loyalty, satisfaction, or future
behavior. To gain a deeper understanding, businesses should complement NPS with
qualitative insights, customer feedback, and behavioral data.

Nescafe example: instant coffee is seen as an shortcut and therefore claimed to be
for lazy housewife’s (tested with the shopping list example)

,Reason for doing experiments:
- Causal Knowledge: Randomized experiments provide reliable causality
compared to econometric models.
• Essential for understanding the direct impact of advertising, product
features, or other variables.
- Forward-Looking Insights: Experiments test hypotheses, helping refine
strategies with actionable insights.
- Incremental Improvements: Business success often stems from small, iterative
changes rather than massive overhauls.

A/B Testing: tests compare two variations (control and treatment) to identify the
impact of a change. Measures dependent variables like engagement, clicks, or
revenue. Advantages:
- Cost-effective and scalable.
- Provides clear, actionable insights into which strategies work best.

Takeaways on Experiments
• Experiments are integral to business success.
• Leading companies like Microsoft, Amazon, and Google conduct thousands of
experiments annually.
• Experiments allow for rapid iteration and validation of ideas.

Readings week 2:
Spiller, S. A., Fitzsimons, G. J., Lynch Jr, J. G., & McClelland, G. H. (2013).
Spotlights, floodlights, and the magic number zero: Simple effects tests in
moderated regression. Journal of Marketing Research, 50(2), 277-288
- Understand and be able to explain why this complex follow-up with a spotlight
or floodlight is needed, and how they work. We do not need you to be able to
reproduce the formulas; but if you study it we do think you should make sure
you understand them as that will help get the actual understanding needed to
be able to see what they do. We want you to be able to indicate which method
you would use in which situation, and vice versa (if we give a situation, that
you can deduce which analyses you should use and explain why that method
is best in that instance).

Read the article, but, as a quick and practical summary it usually is:
- Used to understand the nature of an interaction with a continuous variable
- Tells you at what level of the moderator the IV is significant
(the goal is similar to what simple effect follow-up tests do in ANOVA after
finding a significant interaction)
- Is tested using regression; or in SPSS can also use PROCESS model 1,
Options, Johnson-Neyman output

This article emphasizes the importance of properly analyzing interactions in
moderated regression. By adopting spotlight and floodlight analyses, researchers can
move beyond arbitrary testing practices to uncover deeper insights into how variables
interact in consumer behavior and marketing studies.

, Steffel, M., & Williams, E. F. (2018). Delegating decisions: Recruiting others to
make choices we might regret. Journal of Consumer Research, 44(5), 1015-
1032
- This article provides a nice example of manipulations, mediation, moderation
→ understand and you are likely to be asked to come up with a study yourself
Lecture 3: the experiment

Experiment: procedure under controlled circumstances to discover / illustrate a law or
test a hypothesis. It works well since it uses randomization:
- Arbitrarily assigning each participant to one condition of the experiment
- If I assign person A to the control group, and assign person B to a treatment
group, person A and B are not the same → However, if I do this for every
subject, the average person in the control group is the same as the average
person in the treatment group
- With large samples, true randomization creates balance in for example age,
gender, preference, etc. of participants. groups are the similar on average.

Experiments are a great way to determine causality, which is usually what we’re
interested in. Does a change in X cause a change in Y?

Manipulation is a chance in a construct. How to create a good manipulation:
- Try hard to make the manipulation “clean”
• only manipulate your construct, hold everything else constant→ that
avoids “confounds”
- Try to make the manipulation strong (especially if just doing 1-2 studies)
- Test if it works
• Do a pretest
• Measure a “manipulation check” (in pretest, and in main study)
→ Steffel and Williams: Choice difficulty vs. How easy is it to read?
(using medical terms instead of normal language and ask if people let
doctors make the decision)

How to create a good pretest:
- Make it just like the study, same manipulation, intro, etc.
- Measure the manipulation check
- Can try multiple manipulations and see which works best
- Can also measure potential confounds

Benefits of running a separate pretest, rather than measuring it in the main study:
- Can try multiple manipulations and see which works best
- No carry-over effects
- Can add multiple measures that would be too much for the main study
- Your measure of the manipulation/confound check cannot affect your DV when
(only) tested in the pretest
➔ e.g., I have a manipulation and want to test how believable people think
the manipulation is. However, by asking people whether they believe my
story, they suddenly focus on this aspect.

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