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Market research isn't just about collecting data—it's about understanding why consumers behave the way they do and how businesses can respond to shifting market dynamics. You're being tested on your ability to distinguish between research methods, understand when each is most appropriate, and connect data collection to strategic decision-making. The techniques in this guide demonstrate core principles like quantitative vs. qualitative analysis, primary vs. secondary research, and experimental vs. observational design.
Don't just memorize the names of these methods. Know what type of insight each technique generates, what trade-offs it involves (cost, time, depth, scale), and how businesses use the results to drive technical change and competitive advantage. When an FRQ asks you to recommend a research approach, you need to justify why that method fits the situation—that's where understanding the underlying logic pays off.
These techniques prioritize numerical data and statistical analysis, allowing businesses to identify patterns across large populations and make generalizable claims about market behavior.
Compare: Surveys vs. A/B Testing—both generate quantitative data, but surveys measure stated preferences while A/B tests measure revealed behavior. If an FRQ asks about validating a marketing strategy, A/B testing provides stronger causal evidence.
These approaches sacrifice scale for richness, using open-ended exploration to uncover the motivations, emotions, and cultural factors behind consumer decisions.
Compare: Focus Groups vs. Ethnographic Studies—both are qualitative, but focus groups create artificial discussion settings while ethnography observes behavior in context. Ethnography takes longer but captures what people actually do, not just what they say.
These techniques gather data by watching rather than asking, minimizing the bias that occurs when consumers know they're being studied.
Compare: Observational Research vs. Social Media Listening—both capture unsolicited behavior, but observation works in physical spaces while social listening operates in digital environments. Social listening scales better but misses non-verbal cues and offline behavior.
These methods leverage existing information and computational power to extract insights without conducting new primary research.
Compare: Secondary Data Analysis vs. Big Data Analytics—both use existing data, but secondary analysis typically works with structured datasets while big data handles unstructured, high-volume information. Big data requires significant technical infrastructure but enables real-time decision-making.
| Concept | Best Examples |
|---|---|
| Quantitative/Statistical | Surveys, Conjoint Analysis, A/B Testing |
| Qualitative/Exploratory | Focus Groups, In-Depth Interviews, Ethnographic Studies |
| Observational/Passive | Observational Research, Social Media Listening |
| Existing Data Analysis | Secondary Data Analysis, Big Data Analytics |
| Controlled Experimentation | A/B Testing |
| Cultural/Contextual Insight | Ethnographic Studies, Observational Research |
| Real-Time Monitoring | Social Media Listening, Big Data Analytics |
| Cost-Effective Approaches | Secondary Data Analysis, Surveys |
Which two methods are best suited for understanding why consumers make decisions rather than what they choose? What trade-offs does each involve?
A company wants to determine the optimal price point for a new product feature. Which technique would provide the most actionable data, and why is it superior to simply asking consumers what they'd pay?
Compare and contrast focus groups and ethnographic studies. In what situation would ethnography be worth the additional time and cost investment?
If an FRQ describes a startup with limited budget needing to validate market demand before product launch, which combination of methods would you recommend and in what sequence?
What distinguishes A/B testing from survey research in terms of the type of consumer preference data each generates? Why might stated and revealed preferences differ?