Conjoint Analysis Software helps organizations understand which product features, bundles, and price points customers value most. By presenting respondents with realistic product choices, teams can measure tradeoffs, estimate demand, and identify the configurations most likely to succeed in the market.

SurveyKing’s conjoint module is built for practical product and pricing research. The platform supports choice-based (CBC) designs, respondent-level utilities, and market-share simulations based on feature preferences and price sensitivity. These tools help teams evaluate which product attributes matter, how pricing affects demand, and how different product versions would perform in a competitive scenario.

Whether you are designing new products, optimizing pricing, testing feature bundles, or comparing multiple product concepts, SurveyKing provides the methods and analytics needed to make confident product decisions. This page outlines how conjoint works, when to use it, and how our platform supports product and pricing teams throughout the research process.

SurveyKing’s Conjoint Analysis Software starts at $19 per month, billed monthly and cancelable at any time. This plan includes full access to the conjoint builder, choice-based surveys (CBC), respondent-level utilities, and up to 2,000 responses per month. All core research features are included with no per-study fees, so you can add as many conjoint questions and surveys as needed.

For larger organizations that need higher response volumes, white-label branding, the market-share simulator, custom reporting, or enterprise-level security, we offer flexible enterprise plans. Pricing is based on your estimated annual response volume and any additional features needed. Enterprise customers receive prioritized support and fully branded reporting.

SurveyKing also provides professional research support. Consulting is available at $50 per hour to help design studies, configure attributes and levels, or interpret results. If you need respondents for product or pricing tests, we offer participant panels starting at approximately $2.75 per completed survey, with targeting options including demographics, geography, and behavioral filters.

Conjoint studies start by defining the core product dimensions you want to test, the levels (variations within each attribute), and the attributes. Choosing the right attributes is the foundation of a strong conjoint design. SurveyKing makes this easy by letting teams structure product features into clean attribute–level sets, add images or descriptions, and configure realistic rules such as prohibited combinations. Choice sets typically show 3–5 concepts and 8–12 tasks per respondent, balancing statistical coverage with respondent attention.

SurveyKing supports choice-based conjoint (CBC) with flexible design controls. Researchers can randomize concepts, balance exposure levels, and customize the number of profiles per task. Unlike tools that lock conjoint into a standalone module, SurveyKing lets you insert a conjoint question anywhere in a survey, ideal for panel screeners, demographic filters, or behavior questions that need to come first. This placement flexibility makes segmentation easier and simplifies panel fielding, since all qualifying logic can run before the conjoint appears.

Many teams use preliminary research to refine the final conjoint design. Tools like MaxDiff help identify which features matter most, while Van Westendorp establishes acceptable price ranges. SurveyKing supports these methods within the same survey flow, making it easy to identify which attributes to test, define realistic price levels, and deploy the conjoint study without switching platforms.

After data is collected, SurveyKing summarizes conjoint results into clear, interpretable outputs that show how each product attribute and level influences customer choice. Conjoint analysis uses logistic regression to estimate the numeric values that represent the relative preference or “happiness” respondents assign to each level. Higher utilities indicate stronger preference, while lower values indicate weaker appeal. These level utilities are displayed in intuitive tables and bar charts, making it easy to see which features drive decisions across the study.

In addition to level utilities, SurveyKing calculates attribute importance, which reflects how much each attribute contributes to the overall decision. Importance is derived from the range of utilities within each attribute. For example, a wide spread in flavor preferences would signal that Flavor plays a larger role in decision-making than attributes with narrower ranges, such as Size or Price. This helps teams understand not only which levels perform best, but which attributes truly matter when customers make tradeoffs.

SurveyKing also provides respondent-level utilities, allowing for deeper analytical work, segmentation, and custom modeling. These utilities can be downloaded to Excel for further analysis or integrated into external tools. Researchers can examine individual preference patterns, segment respondents based on earlier-collected demographics or behavior, or prepare datasets for advanced modeling. These findings form the foundation for the next step testing real-world scenarios through the market simulator.

SurveyKing’s market simulator lets teams test real-world scenarios by combining respondent-level utilities with configurable product and pricing options. Inside the simulator dashboard, researchers can build product concepts using any combination of attribute levels, colors, features, bundles, price points, and compare them side-by-side. The simulator estimates share of preference across all configured concepts, helping teams identify which product variations would win in a competitive environment. This allows teams to test new ideas, refine pricing strategy, or evaluate how changes to a single feature shift demand before making costly product decisions.

The simulator also supports segmentation and profile filtering, allowing teams to isolate specific audiences using any screener or demographic question included in the survey. Researchers can filter results by age, region, purchase behavior, product familiarity, or panel-specific screeners to understand how different segments respond to the same product set.

Because respondent-level utilities power the simulator, these filters immediately adjust the predicted shares, enabling teams to compare optimal packages across segments and identify opportunities for targeted product launches. Whether exploring broad market demand or analyzing niche customer groups, SurveyKing provides a clear, flexible tool for evaluating product strategy and making confident decisions.