Frequently Asked Questions (FAQs)

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.

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.

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.

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.

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