What is Conjoint Analysis? (with examples)
A product lead deciding whether a conjoint result is solid enough to greenlight a launch needs a straight answer, not a survey lecture. Conjoint analysis is a survey method that shows people product profiles built from varying attributes and prices, then infers what they value from the trade-offs they pick. Three common examples: a phone maker tests camera quality against battery life and price, a health system tests drug formulary trade-offs, a SaaS company tests feature bundles against price tiers. Running one tells you what people say they'd trade off. It does not by itself tell you what they will do.
- Conjoint analysis shows respondents product profiles with varying attributes and prices, then infers relative feature importance and willingness-to-pay from the trade-offs they choose.
- Common examples: pricing and feature trade-offs in consumer tech, drug formulary and treatment-preference research in health economics, and bundle-versus-price decisions in SaaS.
- A conjoint result is a stated-preference estimate. It only predicts real behavior once it's been checked against real-world or held-out outcomes, and that check is not automatic (EJHE 2018, eClinicalMedicine 2024).
- The three standard estimators, McFadden discrete choice, mixed logit, and ICLV, differ in what they assume about consistency and latent attitudes, not in whether the underlying experiment is causal.
- Before greenlighting a launch off a conjoint, ask for the replication number against a held-out human sample, one built to guard against published studies leaking into training data. The public comparison is on the leaderboard.
What is conjoint analysis?
Conjoint analysis is a survey method that shows respondents product profiles built from varying attributes and levels, then infers relative feature importance and willingness-to-pay from the trade-offs they choose, instead of asking direct rating questions. It traces to Paul Green and V. Srinivasan's 1970s marketing applications of Luce and Tukey's mathematical psychology, and it's now a standardized category: Sawtooth Software is the reference implementation for CBC, ACBC, and hierarchical Bayes estimation, Qualtrics folds it into its enterprise XM suite, and ISPOR's Good Research Practices Task Force has codified design and analysis standards for discrete choice experiments (DCEs), the health-economics name for the same conjoint method (ISPOR task force). Most vendor guides stop at "design attributes, run the survey, read the utilities," and treat that as the finish line.
What are some conjoint analysis examples?
Common examples include pricing and feature trade-offs, health-plan or treatment-preference research, and bundle-versus-price decisions:
- A research team benchmarked conjoint results against real Swiss naturalization referendum outcomes and found paired designs tracked real behavior best, but that design choices materially changed accuracy (PNAS, Hainmueller et al., 2015).
- A phone maker tests camera quality, battery life, and price against each other to prioritize the next feature.
- A health system runs a discrete choice experiment on drug formulary or treatment-preference trade-offs, now routine in pricing and access decisions.
- A SaaS company tests feature bundles against price tiers before a pricing change.
How do you know a conjoint result predicts real behavior?
Running a conjoint answers "what did people say they'd trade off." It doesn't answer "does this predict what they'll do," and treating the two as the same question is where launch decisions go wrong.
A 2018 meta-analysis of health-related DCEs found pooled sensitivity of 88% against real-world choices but specificity of only 34% (AUC 0.60). DCEs are good at confirming who will act, weak at flagging who won't, which is exactly the segment that determines ROI on a launch (European Journal of Health Economics, 2018).
A 2024 follow-up meta-analysis of ten studies found "reasonable" external validity for opt-in choices specifically, but accuracy still varies by intervention type, setting, and analysis method, not a uniform guarantee (eClinicalMedicine, 2024).
Stated willingness-to-pay compounds the problem: absent bias-mitigation techniques, it commonly runs two to three times higher than revealed willingness-to-pay (arXiv, Haghani et al., 2021).
Conjoint estimates can also shift with which attributes respondents happen to attend to during the task, a generalization risk separate from sample size or design efficiency (arXiv, 2024).
Subconscious runs randomized experiments analyzed with discrete choice models on a simulation of the market, then scores that against real human results, not survey output alone. Our best configuration reaches 87% of the measured human ceiling on one study, 0.832 rank correlation against the published human result, where two independent human samples reach 0.959; the mean across all 43 studies passing design filters is 0.73 (causal fidelity paper). That's a replication accuracy figure against a measured ceiling, not a guarantee for a market we haven't tested. Because published studies can end up in a model's training data, the protocol scores held-out configurations built to guard against that overlap. Current numbers, by method and study, are public on the leaderboard.
Which estimator fits your decision?
It depends on what assumption you can defend: independence of alternatives, heterogeneous preferences across respondents, or an unobserved attitude driving the choice.
| Estimator | Key assumption | Best for |
|---|---|---|
| McFadden discrete choice | Independence of irrelevant alternatives (IIA); can distort substitution and preference-share estimates when options are similar | Quick feature or price prioritization when alternatives are clearly differentiated |
| Mixed logit | Preferences vary across respondents, relaxing IIA | Markets with heterogeneous segments or close substitutes |
| ICLV (integrated choice and latent variable) | Models an unobserved attitude, like trust or risk aversion, alongside the observed choice | Decisions where an underlying attitude, not just observed attributes, drives the trade-off |
What to do with a conjoint result before you greenlight a launch
Ask whoever ran it for the replication number against a held-out human or real-world outcome, one built to guard against training-data overlap with published studies, not just the utilities table. If nobody can produce one, you have a stated-preference survey, not a validated prediction, and either is fine as long as you know which one you're deciding on. Check the estimator against what you actually need: preference-share and substitution calls need a mixed logit or a defensible IIA assumption, not a flat logit run by default. For how these estimators compare across live studies, see methods and validation. If you want to walk through a specific launch decision, meet with us.