Conjoint Analysis — Understand and Quantify Customer Preferences
Which product features really matter?
What matters more to your customers — price, brand, or feature set? Conjoint analysis answers this question with data. We measure which attributes drive purchase decisions and simulate market scenarios before you invest.
Request conjoint analysisWhat Is Conjoint Analysis — and When Do You Need It?
Conjoint analysis (also known as trade-off analysis) is one of the most powerful methods in quantitative market research. It measures how customers weigh product attributes against each other — and which combinations of price, performance, and features deliver the highest utility.
What makes it special: respondents don't rate individual attributes in isolation (where everything seems important and nothing too expensive). Instead, they choose between realistic product configurations — exactly like in real purchase decisions. From this, we calculate part-worth utilities for each attribute.
With this data, you can calculate willingness-to-pay, identify optimal product configurations, and simulate market shares for different scenarios — before a product hits the market.
As a full-service institute, we handle the entire process: from attribute definition through experimental design and fieldwork to analysis with Hierarchical Bayes estimation and interactive market simulator.
Conjoint Methods at a Glance
Depending on your research question, we deploy the right variant of conjoint analysis.
Choice-Based Conjoint (CBC)
The most widely used method. Respondents repeatedly choose between 3–5 product configurations. Best replicates real purchase decisions and provides data for market simulations and price optimization.
Adaptive Conjoint (ACBC)
Adapts during the interview to the respondent's preferences. Ideal with many attributes (8+), as less important features are automatically filtered out. Higher precision for complex product categories.
MaxDiff / Best-Worst Scaling
Respondents select the most and least important feature from a list. Creates a clear ranking without scale bias. Perfect for feature prioritization and value communication.
Menu-Based Conjoint
Respondents build their ideal product from a menu, deciding for each feature: add it or not? Ideal for configurable products, tariffs, and modular systems.
How Your Conjoint Analysis Works
Four phases — from attribute definition to market simulator.
Attribute Workshop & Design
Together, we define the relevant attributes and their levels. We create the experimental design — optimized for statistical efficiency and realistic choice scenarios.
Programming & Fieldwork
We program the survey using professional conjoint software, conduct a pretest, and manage fieldwork. Recruitment from our Fresh Recruits database or your customer lists.
HB Estimation & Analysis
Analysis using Hierarchical Bayes estimation — the gold standard for individual part-worth utilities. We calculate relative importances, price sensitivities, and segment-specific preferences.
Market Simulation & Reporting
You receive an interactive market simulator: change product configurations, vary prices, see market shares in real time. Plus a management summary with clear recommendations.
Typical Application Areas
Conjoint analyses are used wherever customers weigh product attributes: What price is acceptable? Which features are essential? Which configuration maximizes perceived value?
Across industries, companies rely on conjoint analysis for new product development, pricing, tariff optimization, and communication planning. From automotive to telecommunications to FMCG — the methodology works in any market.
Conjoint analysis is particularly valuable when you want to secure investment-relevant decisions: new product lines, price adjustments, or portfolio strategies.
Your Advantages With Eleono
- Realistic trade-off decisions instead of wish lists
- Individual part-worth utilities through HB estimation
- Interactive market simulator for your scenarios
- Willingness-to-pay quantified
- Segment-specific analysis and target group profiles
- Full-service: from workshop to presentation
Choice-Based Conjoint
The most widely used method — best replicates real purchase decisions.
Hierarchical Bayes
Individual utility values at respondent level — the gold standard in conjoint analysis.
Market Simulation
Interactively explore scenarios: configure products, vary prices, see market shares.
Frequently Asked Questions About Conjoint Analysis
What does a conjoint analysis cost?
Costs depend on complexity: number of attributes, sample size, and depth of analysis. A standard CBC study with 300–500 respondents typically falls in the mid five-figure range. We provide a customized quote.
How many respondents do I need for a conjoint analysis?
For a CBC study, we recommend at least 200–300 respondents. For segment-specific analyses (e.g., by age group or region), plan for 400–500+, so each segment is sufficiently populated. We calculate the exact sample size upfront.
How many attributes and levels can a conjoint analysis include?
A typical CBC study works with 5–8 attributes and 3–5 levels each. With more attributes, we recommend Adaptive Conjoint (ACBC), which efficiently handles 12+ attributes. We advise on the optimal configuration.
What's the difference between conjoint analysis and a regular survey?
In a classic survey, participants rate attributes individually — where everything seems important and nothing too expensive. Conjoint analysis forces trade-offs: respondents must choose between product configurations, just like in real purchase situations. This yields significantly more realistic results.
What is Hierarchical Bayes (HB) and why does it matter?
HB is the statistical method we use to estimate individual part-worth utilities for each respondent. Unlike aggregate methods (e.g., logit), HB identifies individual preference patterns and enables segment-specific analyses. It's the gold standard in conjoint research.
What can I do with a market simulator?
The market simulator is an interactive tool that lets you explore product scenarios: change features or prices and see in real time how predicted market shares shift. This lets you make data-driven decisions — even months after the study.
How long does a conjoint study take from start to finish?
Plan for 6–10 weeks: 1–2 weeks for workshop and design, 1 week for programming and pretesting, 2–3 weeks for fieldwork, 2–3 weeks for analysis and reporting. For urgent projects, we can accelerate the timeline.
Is conjoint analysis suitable for B2B markets?
Yes, especially so. In B2B, decision-makers often face complex trade-offs between price, service level, contract duration, and technical specifications. Conjoint analysis captures exactly this decision logic. Our B2B recruitment ensures the right decision-makers are surveyed.
Ready to Decode Your Customers' Preferences?
Talk to us about your conjoint analysis. We'll show you which method fits your research question — and what the results mean for your business.
Request Now