Frequently Asked Questions (FAQs)
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As a survey designer, how do you choose the right question type? What factors should you consider?
When choosing question types, consider the following factors:
1) Research objectives: define what problem you want to solve and what data you need;
2) Participant characteristics: age, education level, professional background, etc.;
3) Budget and time: the implementation cost and timeline of different question types;
4) Data quality requirements: whether you need qualitative or quantitative data, and the depth of statistical analysis;
5) Industry characteristics: different industries are suited to different research methods;
6) Competitive analysis: understand the commonly used research methods and standards in the industry. It is recommended to first clarify the research objectives and then choose the most suitable combination of question types based on actual circumstances.
What specific scenarios are MaxDiff analysis, conjoint analysis, the Kano model, and the Van Westendorp PSM suitable for?
Applicable scenarios for each method:
1) MaxDiff analysis: product feature prioritization, service item importance evaluation, brand attribute preference analysis, and market segmentation research;
2) Conjoint analysis: product pricing strategy development, product design optimization, market positioning analysis, competitive analysis, and new product development decisions;
3) Kano model: product feature prioritization, service design optimization, user experience improvement, product roadmap planning, and customer satisfaction enhancement;
4) PSM: new product pricing strategy development, existing product price adjustment, price elasticity analysis, market positioning optimization, and competitive pricing analysis. You can also combine these methods according to your specific research needs.
What advantages do advanced question types have compared with traditional question types? When should they be used?
Advantages and when to use advanced question types:
1) Higher data quality: avoid bias issues common in traditional question types and obtain more reliable preference data;
2) Greater analytical depth: support complex statistical analysis and provide insights from more dimensions;
3) Better decision support: results can be directly used for product decisions, pricing strategies, and other business decisions;
4) Higher cost-effectiveness: although the implementation cost per survey is higher, the data is more valuable. When to use: when traditional question types cannot meet research needs; when data suitable for statistical analysis is required; when the research results will directly affect important business decisions; when a complex consumer preference structure needs to be understood.
How should the survey process for these advanced question types be designed? What are the best practices?
Process design and best practices: 1) Preparation: clarify the research objectives, identify the target audience, and define the research framework; 2) Question design: ensure questions are clear and specific, avoid ambiguity, and consider participants’ cognitive load; 3) Sampling design: ensure sample representativeness, consider sample size requirements, and design an appropriate recruitment strategy; 4) Implementation control: maintain survey quality, monitor participants’ response behavior, and make timely adjustments; 5) Data analysis: choose suitable analysis methods and combine them with qualitative research findings to generate comprehensive insights. Best practices: start with simple questions and gradually introduce more complex types; provide clear instructions and examples; keep the survey duration under control to avoid participant fatigue; combine multiple research methods to obtain more comprehensive data.
What are the differences in how these advanced question types are applied across different industries? How should they be adjusted based on industry characteristics?
Industry application differences and adjustment recommendations: 1) Consumer goods industry: focus on user experience and price sensitivity, making it suitable for the Kano model and Van Westendorp PSM; 2) B2B industry: emphasize functional value and solution effectiveness, where MaxDiff analysis and conjoint analysis are more appropriate; 3) Service industry: focus on service quality and customer satisfaction, where the Kano model and MaxDiff analysis work well; 4) Technology products: prioritize feature innovation and performance, with conjoint analysis and the Kano model used together; 5) Financial industry: focus on risk appetite and return expectations, requiring question design adjustments based on industry characteristics. Adjustment recommendations: adapt question wording to industry terminology; consider industry-specific decision-making processes and influencing factors; align with industry standards and best practices; and take regulatory requirements and compliance into account.
How do I quickly choose a question type based on research objectives? Is there a decision tree?
Quick question type selection decision tree: 1) Need to understand preference ranking? → MaxDiff analysis; 2) Need to understand trade-offs among attributes? → Conjoint analysis; 3) Need to understand functional impact? → Kano model; 4) Need to understand price sensitivity? → Van Westendorp PSM. Combination use cases: End-to-end product development → combine all four question types; Market positioning research → MaxDiff + Conjoint analysis; User experience optimization → Kano model + MaxDiff; Pricing strategy development → Conjoint analysis + PSM. Remember: use a single question type for a single objective, and combine question types for complex objectives.
What are the different sample size requirements for these question types? How do I determine an appropriate sample size?
Sample size requirements: 1) MaxDiff analysis: 100–500 valid responses are recommended; the more attributes you have, the larger the sample needed. 2) Conjoint analysis: 100–1000 valid responses are recommended; more complex designs require larger samples. 3) Kano model: 100–300 valid responses are recommended; the number of features affects sample size requirements. 4) Van Westendorp PSM: 100–500 valid responses are recommended; segmented analysis requires larger samples. When determining sample size, consider: statistical significance requirements, segmentation analysis needs, budget constraints, time requirements, and data quality goals. In general, at least 100 valid responses are recommended, and more complex analyses require larger samples.
How should these research question types be combined, and what are the best practices?
Question type combination strategy: 1) Product development process: Kano model (feature prioritization) → MaxDiff analysis (feature importance) → conjoint analysis (product configuration) → Van Westendorp PSM (pricing strategy); 2) Market research projects: MaxDiff analysis (market segmentation) → conjoint analysis (product positioning) → Van Westendorp PSM (price sensitivity); 3) User experience optimization: Kano model (feature categorization) → MaxDiff analysis (improvement prioritization) → Van Westendorp PSM (willingness to pay). Best practices: avoid using too many complex question types in the same survey; arrange question types in a reasonable order, from simple to complex; consider participants' cognitive load; ensure there is a logical connection between the question types; combine with qualitative research to gain more comprehensive insights.
What is conjoint analysis? What research scenarios is it suitable for?
Conjoint analysis is a statistical technique used to measure consumer preferences for different product or service attributes. It breaks products down into their component attributes (such as price, brand, quality, etc.) and tests consumer preferences by combining different attribute levels. It is suitable for scenarios such as product pricing strategy development, product design optimization, market positioning analysis, competitive analysis, and new product development decisions. It is especially well suited for research that needs to understand how consumers trade off different product attributes.
What are the three concept types in conjoint analysis? How should you choose?
Three concept types and selection suggestions: 1) Custom Concept: Multi-attribute, multi-level products - suitable for complex products, requires uploading a data template, and is ideal for studies with detailed product specification data; 2) Custom Concept: Simple products - similar to MaxDiff but only requires selecting the "best," suitable for concept testing, packaging design, brand selection, etc., with a minimum of 4 concepts required; 3) System-generated combination concepts: Multi-attribute, multi-level products - suitable for studies that need to test multiple attribute combinations, with the system automatically generating concept combinations. When choosing, consider research complexity, data availability, and analysis requirements.
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