Frequently Asked Questions (FAQs)
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How should conjoint analysis task parameters be set to obtain reliable results?
Key parameter settings: 1) Number of concepts per task: 2-5 is recommended. Too few provides insufficient comparison, while too many increases cognitive load; 2) Total number of tasks: balance data quality and participant fatigue, and calculate based on the number of attributes and levels; 3) None option: you may include a "none of the above" option to simulate real purchasing scenarios; 4) Concept presentation rules: you can choose to show all concepts, show them in sequence, show only some of them, or display them only in specific tasks, depending on the research design requirements; 5) Prohibited combinations: you can set unreasonable attribute combinations to avoid presenting meaningless options.
In what situations should conjoint analysis be used instead of other question types?
Conjoint analysis is recommended in the following situations: 1) when you need to understand how consumers trade off multiple product attributes; 2) when the research objective is to determine the optimal combination of product configurations; 3) when you need to predict market acceptance of a new product or service; 4) when analyzing price sensitivity and price elasticity; 5) when you need to understand preference differences across different market segments; 6) when it is suitable for market research on complex products such as automobiles, electronics, financial services, and real estate; 7) when data support is needed for product pricing strategy.
How should conjoint analysis results be interpreted? How can they be applied?
Result interpretation and applications: 1) Attribute importance: shows the degree of influence each attribute has on consumer decisions, helping identify key drivers; 2) Concept utility: reflects the preference level for different product configurations, used to optimize product combinations; 3) Price sensitivity: helps understand how consumers react to price changes, guiding pricing strategies; 4) Market segmentation: identifies groups with different preferences, supporting precise marketing; 5) Predictive model: can be used to predict market performance after a new product launch. Application recommendations: the sample size should be greater than 100 to obtain reliable results, the results should be used in combination with qualitative research, and data should be updated regularly to reflect market changes
What is the Kano model? What research scenarios is it suitable for?
The Kano model is a product development framework developed by Dr. Noriaki Kano in the 1980s to help companies understand and prioritize product features. Based on customer needs and preferences, it categorizes product features into five types: basic features, performance features, excitement features, indifferent features, and reverse features. It is suitable for scenarios such as product feature prioritization, service design optimization, user experience improvement, product roadmap planning, and customer satisfaction enhancement, and is especially useful for research that needs to understand the degree to which features affect customer satisfaction.
What do the five function types in the Kano model represent? How should they be understood?
The five function types and their meanings are: 1) Must-be quality: the basic functions customers expect a product to have; if missing, they will be dissatisfied, but having them does not necessarily increase satisfaction; 2) Performance quality: has a linear relationship with customer satisfaction; better performance increases satisfaction, while worse performance decreases satisfaction; 3) Attractive quality: functions customers do not expect, but whose presence significantly increases satisfaction; an important source of product differentiation; 4) Indifferent quality: whether present or not, they have no significant impact on customer satisfaction; 5) Reverse quality: functions that, when present, instead reduce customer satisfaction and may be seen as unnecessary or annoying. Understanding these types helps with product feature prioritization and resource allocation.
How do I create Kano model questions? What should I pay attention to?
Steps and precautions: 1) Select the "Kano" question type to add it to the survey; 2) Enter the names of the functions or services you want to study, one per line. These names will become the questions; 3) The system will automatically generate the default Kano model format options, including a 5-point scale and two question lines: How would you rate it if this feature exists/is missing? 4) You can adjust question settings such as required/optional, display logic, skip logic, grouping settings, etc. Precautions: feature names should be clear and specific, and avoid vague descriptions; make sure the feature description is understandable to participants; consider the relative importance of the features and avoid including too many indifferent features.
In what situations should the Kano model be used instead of other question types?
The Kano model is recommended in the following situations: 1) when you need to understand the different types of impact product features have on customer satisfaction; 2) when the research goal is to identify product differentiation opportunities and competitive advantages; 3) when you need data support for prioritizing product feature development; 4) when analyzing changes in customer expectations and feature maturity; 5) when it is used for user experience research in software products, mobile apps, website design, service processes, and similar areas; 6) when you need to balance the maintenance of basic features with the development of exciting features; 7) when developing a long-term product roadmap and feature planning strategy.
How to interpret Kano model results? How to apply them?
Interpretation and application of the results: 1) KANO attribute: determine the function type based on the highest score to guide feature classification; 2) Better coefficient: measures the impact of attribute performance improvement on satisfaction, calculated as (Performance% + Excitement%)/(Basic% + Performance% + Excitement% + Indifferent%); 3) Worse coefficient: measures the impact of attribute performance decline on satisfaction, calculated as [(Basic% + Performance%)/(Basic% + Performance% + Excitement% + Indifferent%)]×(-1). Application recommendations: basic features must be guaranteed for quality, performance features need continuous improvement, excitement features are the key to differentiation, indifferent features can be considered for simplification or removal, and reverse features should be avoided. The results should be used in combination with qualitative research such as user interviews and competitor analysis.
What is MaxDiff analysis? Which research scenarios is it suitable for?
MaxDiff (Maximum Difference Scaling) is a powerful survey technique widely used to measure consumer preferences and priorities. It presents participants with a series of choices and asks them to identify the most preferred and least preferred options. It is suitable for scenarios such as prioritizing product features, evaluating the importance of service items, analyzing preferences for brand attributes, and market segmentation research. It is especially well suited to studies that need to understand the real hierarchy of user needs.
What are the advantages of MaxDiff analysis compared with traditional rating questions?
Compared with traditional rating questions, MaxDiff analysis has the following advantages: 1) It avoids rating bias, as participants must make a choice instead of giving all options high scores; 2) It provides a ranking of relative importance, which is more in line with real-world decision-making scenarios; 3) It reduces cognitive burden, since only a small number of options need to be compared each time; 4) It produces more reliable preference data, making it suitable for statistical analysis; 5) It can identify the features that are truly important and avoid the "average score trap".
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