To develop your "people sense," actively predict the outcomes of A/B tests and new product launches before they happen. Afterward, critically analyze why your prediction was right or wrong. This constant feedback loop on your own judgment is a tangible way to develop a strong intuition for user behavior and product-market fit.
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.
Before finalizing an offer, create and promote two distinct lead magnets. The one that outperforms reveals your audience's true pain point and can pivot your entire business strategy. This approach transforms a list-building tactic into a powerful market research tool for finding product-market fit.
Foster a culture of experimentation by reframing failure. A test where the hypothesis is disproven is just as valuable as a 'win' because it provides crucial user insights. The program's success should be measured by the quantity of quality tests run, not the percentage of successful hypotheses.
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.
In AI PM interviews, 'vibe coding' isn't a technical test. Interviewers evaluate your product thinking through how you structure prompts, the user insights you bring to iterations, and your ability to define feedback loops, not your ability to write code.
When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.
Gamma compresses the product development cycle into a single day. They generate an idea in the morning, build a functional prototype, and use platforms like Voice Panel to run user research studies in the afternoon, yielding actionable feedback by evening. This operationalizes rapid, pre-build validation.
Great PMs excel by understanding and influencing human behavior. This "people sense" applies to both discerning customer needs to build the right product and to aligning internal teams to bring that vision to life. Every aspect, from product-market fit to go-to-market strategy, ultimately hinges on understanding people.
The rapid evolution of AI makes traditional product development cycles too slow. GitHub's CPO advises that every AI feature is a search for product-market fit. The best strategy is to find five customers with a shared problem and build openly with them, iterating daily rather than building in isolation for weeks.