Basic Info
Foundations before methods
Use these pages to ground the project lifecycle, data types, supplier landscape, and the qualitative versus quantitative split.
Basic Info topics
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Basic Info
What is market research
A practical definition of market research and the role it plays in decision-making.
- Foundations
- Strategy
Why it exists
Market research reduces uncertainty. It turns business questions into evidence, then turns evidence into decisions that can be explained.
What it typically covers
- Understanding customer needs, attitudes, and behaviors
- Measuring awareness, usage, satisfaction, and preference
- Testing concepts, messages, products, and prices
- Prioritizing where to act and what trade-offs matter most
A practical definition
In practice, market research is less about collecting opinions and more about designing a disciplined way to learn. Good studies connect a business decision, a sample, a questionnaire or discussion guide, an analysis plan, and a clear recommendation.
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The standard project process
A step-by-step view of how a typical research project moves from brief to reporting.
- Workflow
- Delivery
Typical project phases
- Clarify the decision and write the research objectives.
- Choose the approach, sample, and timeline.
- Design the instrument or discussion guide.
- Program, field, and monitor quality.
- Clean, structure, and label the data.
- Analyze the results against the original objectives.
- Build a clear report with recommendations.
Where projects usually go wrong
- Objectives are too broad
- Stakeholders want multiple incompatible outputs from one study
- Data cleaning is left too late
- Analysis answers interesting questions rather than decision questions
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The types of data we deal with
A practical overview of structured, unstructured, attitudinal, and behavioral data in research.
- Data
- Foundations
Common data categories
- Quantitative survey data: structured, codable, easy to table and model
- Qualitative data: interviews, groups, diaries, and open ends
- Behavioral data: transactions, visits, clicks, and usage records
- Derived variables: segments, scores, weights, and modeled outputs
Why the distinction matters
Different data types support different claims. Structured survey data is strong for measurement and comparison. Qualitative material is strong for depth and language. Behavioral data is strong for observed action but not always motivation.
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Qual vs Quant research
How to distinguish qualitative and quantitative approaches and when each one is appropriate.
- Method Design
- Foundations
Qualitative research
Qualitative work helps you explore language, meaning, motivations, and context. It is strongest when the goal is discovery, reframing, or understanding how people make sense of a category.
Quantitative research
Quantitative work helps you size, compare, prioritize, and estimate. It is strongest when the goal is measurement, segmentation, modeling, or tracking.
A useful rule
Use qual to shape the hypothesis. Use quant to test how widespread or commercially meaningful it is.
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Panel providers
A starter guide to what panel providers do and how to think about quality, fit, and risk.
- Sampling
- Suppliers
What panel providers do
Panel providers supply respondents that match target definitions, incidence expectations, geography, quotas, and timing requirements.
What to evaluate
- Feasibility and incidence realism
- Sample source transparency
- Fraud and speed control processes
- Ability to manage complex quota structures
- Experience with the category or target audience
Practical caution
Low cost is rarely the only relevant criterion. A cheaper sample that creates noisy or biased data usually costs more once analysis and confidence are affected.
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