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Quantitative Research

Choice-Based Conjoint

Choice-Based Conjoint

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Definition:

Choice-based conjoint analysis is a quantitative research technique used to measure how consumers value different product attributes by presenting them with a series of realistic choice scenarios. Rather than asking participants to rate features in isolation, choice-based conjoint forces trade-offs that mirror actual buying decisions, producing preference share estimates and willingness-to-pay data that simpler survey methods cannot generate. The method is widely used in pricing research, product configuration, packaging design, and portfolio strategy. Because it models real decision-making under constraint, choice-based conjoint produces findings that are more predictive of market behavior than direct preference questions or rating scales alone.

How Conveo Does It

Conveo pairs choice-based conjoint tasks with AI-moderated video interviews, so teams capture both the preference data and the reasoning behind each choice from real participants in the same session. Studies launch in under 30 minutes and return decision-ready findings in days rather than weeks. Because every response traces back to a real person who completed the task on camera, teams get the statistical output alongside verbatim explanation, with no synthetic respondents and no avatars inflating the data.

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Frequently asked questions.

Choice-based conjoint analysis is a quantitative method that presents participants with sets of product or service options and asks them to choose their preferred one. By varying the attributes across choice sets, researchers calculate how much each feature influences the decision. The output includes part-worth utilities, relative attribute importance scores, and simulated preference shares that help teams model how different configurations would perform in the market.
Choice-based conjoint is the right method when the research question involves trade-offs rather than isolated preferences. Standard surveys can tell you that participants want faster delivery and lower prices, but they cannot tell you which one they would sacrifice if forced to choose. Choice-based conjoint replicates that constraint, making it the preferred approach for pricing research, feature prioritisation, and portfolio decisions where the interaction between attributes matters as much as the attributes themselves.
Both methods force trade-offs, but they answer different questions. MaxDiff identifies which items in a list are most and least preferred, making it well suited to prioritising features, messages, or concepts from a longer set. Choice-based conjoint goes further by modelling how combinations of attributes drive choice, producing preference shares and willingness-to-pay estimates. MaxDiff tells you what people value most. Choice-based conjoint tells you how they balance competing values when selecting between fully configured options.
AI is closing the gap between the statistical output and the human reasoning behind it. Traditional choice-based conjoint studies delivered preference shares but left teams guessing about the why. AI-moderated interviews now run alongside the conjoint task in the same session, probing participants on the choices they made in real time. Analysis completes automatically as sessions close, so teams receive attribute importance data and thematic explanation together, without waiting weeks for a separate qualitative phase to follow up.
Enterprise teams use choice-based conjoint most often in pricing strategy, new product development, and portfolio rationalisation. A typical application involves testing three to five product configurations across a representative sample, then using the preference share simulator to model how a price change or feature addition would shift demand. Teams in CPG, technology, and financial services also use it to stress-test positioning before launch, comparing how their preferred configuration performs against likely competitive alternatives in the same choice environment.

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