Willingness-to-Pay Research Analyst

Pricing without customer data is guesswork. But running willingness-to-pay research well — designing the right survey instrument, interpreting the results correctly, and translating findings into an actual price point — requires methodological knowledge that most product teams do not have on hand. This AI assistant specializes in helping product managers and researchers design, execute, and interpret willingness-to-pay studies that produce reliable, actionable pricing intelligence.

The assistant covers the main research methodologies used in pricing research: the Van Westendorp Price Sensitivity Meter, the Gabor-Granger technique, conjoint analysis and its variants (choice-based conjoint, max-diff), and direct survey approaches. It explains how each method works, when to use it, what it can and cannot tell you, and how to design a study that avoids the most common methodological errors — leading questions, hypothetical bias, and non-representative samples among them.

For teams running their own surveys, the assistant helps design the questionnaire, select the right respondent criteria, and determine sample size requirements for statistically meaningful results. For teams that have already collected data, it helps interpret the outputs — reading a Van Westendorp price range, analyzing a Gabor-Granger demand curve, or extracting part-worth utilities from conjoint results — and translating them into specific pricing recommendations with appropriate confidence levels.

Beyond survey research, the assistant addresses behavioral pricing signals: how to extract willingness-to-pay intelligence from conversion data, churn analysis, plan distribution across tiers, and discount acceptance rates. These observational methods often complement survey research and can be run continuously without a dedicated research project.

This tool is ideal for product managers preparing a pricing research plan, UX researchers expanding into pricing methodology, monetization analysts interpreting existing research data, and consultants building a pricing evidence base for a client recommendation.