Conjoint Analysis Software: A Researcher's Guide

What to Look for in Conjoint Software

Conjoint analysis software needs to handle three things well: survey design (building the experimental design and choice tasks), data collection (presenting visual choice cards to respondents on any device), and analysis (estimating utilities, calculating importance, and running market simulations). Some tools do all three. Others specialize in one or two and require you to stitch together a workflow across multiple products.

The right choice depends on your team's technical expertise, study volume, budget, and whether conjoint is your primary research method or one of many. This guide breaks down the field so you can match your needs to the right tool.

Types of Conjoint Software

Standalone Conjoint Platforms

Purpose-built for conjoint and discrete choice modeling. These offer the most advanced design options (CBC, ACBC, menu-based, alternative-specific designs) and the most sophisticated analysis (hierarchical Bayes, latent class, market simulation). They're built for researchers who run conjoint studies regularly.

Best for: Research agencies, insights teams that run 5+ conjoint studies per year, academic researchers. Trade-off: Higher learning curve, higher cost, less integration with broader survey workflows.

Survey Platforms with Conjoint Modules

General-purpose survey tools that include conjoint as one of many question types. The conjoint capabilities are typically more limited (CBC only, fewer design options), but the advantage is running conjoint within a larger survey alongside other question types, screening logic, and custom branding.

Best for: In-house insights teams that need conjoint alongside standard survey questions, companies that don't want separate tools for each method. Trade-off: Fewer advanced conjoint options, may lack ACBC or menu-based support.

Statistical Analysis Software

Tools like SPSS, R, and Python that can run conjoint analysis but don't handle survey design or data collection. You'd build the survey in another tool, export the data, and run the estimation in the stats package.

Best for: Academics, data scientists, organizations with existing statistical infrastructure. Trade-off: No survey design or fielding capabilities. Significant manual effort required.

Platform Comparison

Feature-by-Feature Breakdown

Experimental Design Generation

This is make-or-break for conjoint quality. The software needs to produce balanced, efficient designs that maximize statistical information per respondent.

Standalone tools (Sawtooth, Conjointly) generate D-efficient or balanced overlap designs with support for prohibited pairs, minimum/maximum overlap settings, and alternative-specific designs. You get full control over design parameters.

Survey platforms typically auto-generate designs with less user control. This is fine for standard studies (5-6 attributes, 3-4 levels) but limiting for complex designs that need interaction effects or custom constraints.

What to check: Can the software handle prohibited attribute-level combinations? Does it support alternative-specific designs (where not all attributes appear in every alternative)? Can you export the design for external review?

Visual Choice Task Presentation

Respondents need to see choice tasks as visual product cards, not data tables. Mobile-responsive design is non-negotiable since 40-60% of survey respondents complete on phones.

Most platforms handle this adequately now, with card-based layouts and responsive design. The differences are in customization: can you use product images instead of text? Can you control card layout, font sizes, and color coding?

Watch out for: Platforms that display conjoint tasks as grids of text (resembling a spreadsheet) rather than visual cards. This format increases cognitive load and produces lower-quality data, especially on mobile devices.

Hierarchical Bayesian (HB) Estimation

HB is the current standard for conjoint analysis. It produces individual-level part-worth utilities (not just aggregate averages) and handles moderate sample sizes well.

Standalone tools include HB as a core feature with full control over priors, iterations, and convergence diagnostics.

Survey platforms vary. Some run HB natively; others use aggregate logit or multinomial logit, which only gives you group-level estimates. If individual-level analysis or segment comparisons are important, confirm HB support before committing.

Market Simulator

The simulator turns utility scores into business decisions by predicting share of preference for hypothetical product configurations. It's the output stakeholders care about most.

Full simulators let you define multiple product profiles, test pricing scenarios, and compare share predictions across segments. They support different choice rules (first choice, share of preference, randomized first choice).

Limited simulators only allow 2-3 product comparisons or don't support segmentation. This constrains the analysis to simple A/B scenarios rather than full competitive market modeling.

No simulator means you'd need to export utilities and build simulations in Excel, R, or Python. Feasible, but time-consuming and error-prone.

Integration with Other Research Methods

If your research program includes MaxDiff, Van Westendorp, standard surveys, and qualitative methods alongside conjoint, a platform that handles multiple methods saves time and reduces data silos.

Standalone conjoint tools typically don't support non-conjoint question types. You'd need a separate survey tool for screening questions, demographics, and follow-up items, then merge the data.

Integrated platforms (Quali-Fi, Qualtrics, QuestionPro) let you embed conjoint within a larger survey that includes screening, MaxDiff, Van Westendorp, custom questions, and logic branching, all in one instrument.

Standalone vs. Integrated: A Decision Framework

Hidden Costs to Watch

Per-Respondent Fees

Some platforms charge per completed response on top of annual licensing. At $1-$3 per response, a 500-person study adds $500-$1,500 per study. This adds up quickly if you run multiple studies per year.

Sample Panel Access

The software license rarely includes respondent access. You'll need a separate panel provider or your own recruitment channel. Panel costs for conjoint studies typically run $8-$25 per completed response (general population) or $30-$100+ for specialized audiences (healthcare professionals, C-suite executives).

Training and Support

Standalone conjoint tools have steeper learning curves. Factor in training time (1-3 days for Sawtooth, shorter for simpler platforms) and ongoing support costs. Some vendors include training in the license; others charge separately.

Export and Interoperability

Check whether you can export raw data, utility estimates, and experimental designs. Some platforms restrict exports to encourage using their built-in analysis. If your team wants to run additional analysis in R, Python, or SPSS, export capability is essential.

Frequently Asked Questions

Is there free conjoint analysis software?

A few platforms offer free tiers with limited respondent counts (OpinionX caps at 10 respondents on the free plan). R and Python are free and can run conjoint analysis, but you'll need to handle survey design and data collection separately. For production-quality studies, expect to pay for software.

Can I run conjoint analysis in Excel?

You can do a simplified version with manual design and regression analysis, but it's not recommended for real studies. Excel lacks experimental design generation, HB estimation, and market simulation. It's useful for learning the concepts but not for producing data you'd make business decisions on.

What's the minimum I need to spend on conjoint software?

For self-service CBC with a market simulator, Conjointly starts around $1,895/year for a single researcher. Quali-Fi starts at $89/month and includes conjoint alongside 50+ other question types. If you only need 1-2 small studies, OpinionX's $900/year plan or a Conjointly subscription covers the basics.

Do I need specialized software or can my current survey tool handle it?

If your survey platform already includes a CBC module with HB estimation and a market simulator, you probably don't need a separate tool for standard studies. Check the three critical features: experimental design generation, HB analysis, and market simulation. If any of those are missing or weak, a standalone tool or a platform upgrade will produce better results.

Related Guides

• Conjoint Analysis: Complete Guide -- Full methodology overview
• CBC vs ACBC Conjoint -- Understanding which method your software needs to support
• How to Design a Conjoint Study -- Design requirements that affect software choice
• MaxDiff Analysis -- A related method to consider alongside conjoint
• Conjoint Analysis Survey Template -- Ready-to-use template for CBC studies
• Research Vendor Selection -- How to evaluate research platforms broadly

Run CBC conjoint with built-in HB analysis -- try Quali-Fi free for 14 days.