Adaptive conjoint analysis (ACA) is a conjoint method that tailors each survey to the person taking it. It learns which product features matter most to a respondent, then spends the remaining questions on those features. That makes it practical for studies with many attributes.<\/p>\n\n\n\n
ACA is also less common than it once was. Choice-based methods now dominate pricing work, and ACA has known limits. Knowing where it still fits saves time and budget.<\/p>\n\n\n\n
In this guide, we will explain how ACA works, how it differs from CBC and ACBC, and when to use it.<\/p>\n\n\n\n\n\n
Adaptive conjoint analysis (ACA) is a survey method that customizes each respondent’s interview, so the questions focus on the product features that person values most. The word “adaptive” means the software re-estimates preferences after each answer and picks the next question to learn as much as possible.<\/p>\n\n\n\n
ACA belongs to the wider family of conjoint analysis<\/a>, which measures how people trade off product features. Three terms come up throughout this guide:<\/p>\n\n\n\n ACA produces a part-worth utility for every level, for every respondent.<\/p>\n\n\n\n Sawtooth Software released ACA in 1985. Its own site now reports that ACA makes up only about 2% of conjoint projects<\/a> among its users. ACA can run on the web or on offline devices in CAPI mode, short for computer-assisted personal interviewing.<\/p>\n\n\n\n ACA runs in two main parts. A short self-explicated section comes first, followed by adaptive paired comparisons. Self-explicated means respondents state their preferences directly instead of choosing between products.<\/p>\n\n\n\n Consider a software company testing ten features for a new project management tool. The figures below are illustrative, not from a real study. One respondent’s stated importance ratings (1 to 4) might look like this:<\/p>\n\n\n\n This respondent cares about automation, integrations, and price, so most pairs pit those against each other. A pair might show Plan A with advanced automation at $30 against Plan B with basic automation at $20. The respondent rates which plan they prefer and by how much.<\/p>\n\n\n\n A standard choice-based study would repeat all ten attributes in every task. Here, most of the interview goes to the four attributes that matter to this person. Across hundreds of respondents, the software averages the utilities and might report that automation drives 28% of preference while price drives 18%.<\/p>\n\n\n\n Choice-based conjoint (CBC) asks respondents to pick their favorite from sets of complete product profiles. Adaptive choice-based conjoint (ACBC) adds adaptive stages to a choice design. MaxDiff, short for maximum difference scaling, asks people to pick the best and worst item from small sets of items.<\/p>\n\n\n\n\n
How does adaptive conjoint analysis work?<\/strong><\/h2>\n\n\n\n
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For each attribute, the respondent orders the levels from most to least preferred.
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The respondent says how much the gap between the best and worst level matters. These answers form the starting estimate, called priors.
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The software shows two concepts, each described on only some attributes. That is a partial profile, and it can use up to five attributes per concept. The respondent rates which concept they prefer on a graded scale.
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The system refines the estimate, so later pairs probe the trade-offs that matter most to that person.
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The software builds a few concepts from the most important attributes and asks the respondent to rate each one.
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The software combines priors and pairs into one set of utilities per respondent. Many studies use hierarchical Bayes (HB) estimation, a statistical method that uses the whole sample to steady each person’s estimates.<\/li>\n<\/ol>\n\n\n\nAdaptive conjoint analysis example<\/strong><\/h2>\n\n\n\n
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Adaptive conjoint analysis vs choice-based conjoint<\/strong><\/h2>\n\n\n\n