1 tip & 3 resources per week to improve your Strategy, OKRs, and Discovery practices in less than 5 minutes. Explore my new book on realprogressbook.com
Why Your 'Correct' Discovery Method Might Be Wrong
Why Your 'Correct' Discovery Method Might Be WrongDear Reader, "Which experiment should we run next?" This question comes up in almost every Discovery coaching session I facilitate. Teams often focus on finding the methodologically perfect way to test their assumptions. But here's the thing: the most technically correct experiment isn't always the right one to run. When choosing methods for Product Discovery, we often focus on what fits our research question or assumption best. Say you want to understand how users perceive features in your product. A diary study might be the perfect method—capturing real usage patterns over time. But what if it takes three months to get reliable insights? A series of well-structured interviews or shadowing sessions might get you 80% of the way there in just two weeks. Several factors determine your lead time to insight:
Here's another way to think about it: An A/B test might seem quick to start—taking just hours or days to implement. But depending on your traffic and conversion rates, it could take weeks or months to reach statistical significance. In contrast, while recruiting participants for qualitative interviews might take two weeks, you could have reliable insights within days of completing them. Neither method is inherently better. What matters is the total time to reliable insight, not just how quickly you can get started. So, when picking your next Discovery priority, ask yourself:
Here's a practical tip: When evaluating lead time, avoid abstract scoring systems. Instead, estimate the actual duration by adding:
This concrete timeline helps you make practical trade-offs between methods. Remember: The goal isn't to achieve perfect certainty. It's to reduce uncertainty enough to make confident decisions about what to build next. Did you enjoy the newsletter? Please forward it. It only takes two clicks. Creating this one took two hours. Thank you for Practicing Product, Tim Good News!Some last tickets are available for my in-person Product Discovery workshop on March 10 in London (as part of Mind the Product conference).
As a Product Management Coach, I guide Product Teams to measure the real progress of their evidence-informed decisions. I focus on better practices to connect the dots of Product Strategy, Product OKRs, and Product Discovery. |
Product Practice Newsletter
1 tip & 3 resources per week to improve your Strategy, OKRs, and Discovery practices in less than 5 minutes. Explore my new book on realprogressbook.com
Product Practice #405 How to treat Prototypes as Decision-Making Tools PUBLISHED Apr 23, 2026 READ ON HERBIG.CO From Strategy Choice to Planned Experiments in One Workshop In my upcoming live remote workshop From Strategy to Discovery, I take you from sharpening your Product Strategy, to defining leading indicators for measuring progress, all the way to prioritized assumptions you need to derisk end-to-end. Three 4h Live Sessions - Lifetime Material and Recording Access - Real Results Join...
Product Practice #404 Linked Better Practices over Stacked Best Practices PUBLISHED Apr 16, 2026 READ ON HERBIG.CO Dear Reader, During a recent webinar, someone asked a question I had to think about a bit longer: "What do you do when your strategy is still early, and you're not sure if it's right?" The answer that popped into my head was based on an incredible piece of advice (or admission) I received from a former boss 10+ years ago: No one knows if their strategy is right in the beginning,...
Product Practice #403 Linked Better Practices over Stacked Best Practices PUBLISHED Apr 9, 2026 READ ON HERBIG.CO Dear Reader, During my last webinar on Connect Strategy, Goals, and Discovery with Progress Wheel, I asked people which part of their work is most prone to Alibi Progress. Almost everyone who chimed in named OKRs. And that's because many OKR cycles start the same way for teams: Someone opens a spreadsheet, fills in three to five semi-random metrics, and picks a value that isn't...