Mailtrap made a multi-step survey a required part of signup. Counterintuitively, this added friction had no negative impact on conversion rates. The collected data on user intent, role, and marketing attribution proved invaluable for segmenting users and focusing on high-value cohorts, informing both product and marketing strategy.

Don't just collect feedback from all users equally. Identify and listen closely to the few "visionary users" who intuitively grasp what's next. Their detailed feedback can serve as a powerful validation and even a blueprint for your long-term product strategy.

To manage an infinite stream of feature requests for their horizontal product, Missive's founders relied on a simple filter: "Would I use that myself?" This strict dogfooding approach allowed the bootstrapped team to stay focused, avoid feature bloat, and build a product they genuinely loved using.

Intentionally create open-ended, flexible products. Observe how power users "abuse" them for unintended purposes. This "latent demand" reveals valuable, pre-validated opportunities for new features or products, as seen with Facebook's Marketplace and Dating features.

Product teams often fear showing prototypes because strong customer demand creates pressure. This mindset is flawed. Having customers eager to buy an unbuilt feature is a high-quality signal that validates your roadmap and is the best problem a product manager can have.

People are unreliable at predicting their future behavior. Instead of asking if they *would* use a new feature, ask for a specific instance in the last month where it *would have been* useful. If they can't recall one, it's a major red flag for adoption.

Instead of manual survey design, provide an AI with a list of hypotheses and context documents. It can generate a complete questionnaire, the platform-specific code file for deployment (e.g., for Qualtrics), and an analysis plan, compressing the user research setup process from days to minutes.

Instead of only testing minor changes on a finished product, like button color, use A/B testing early in the development process. This allows you to validate broad behavioral science principles, such as social proof, for your specific challenge before committing to a full build.

Before building a complex feature, validate its value by manually creating the desired output for customers. The Buildots team used Excel to generate performance insights from their data. Only after seeing customers act on these manual reports did they productize the feature.