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April 2, 2026
Successful Product Discovery: 4 Key Insights from a Sr. Product Manager at Miro

Pat Osorio


In our June masterclass, Julia Rudge, Senior Product Manager at Miro, shared valuable insights and experiences from her career as a Product Manager. With expertise in business, marketing, and product development, Julia has worked with companies ranging from startups to industry giants like Uber.Â
In this article, we'll explore the key takeaways from Julia's masterclass, including:
- the concept of product discovery
- the product development life cycle at Miro
- choosing validation strategies
- leveraging customer and stakeholder perspectives
Let's dive right into it.
Understanding Product Discovery
Product discovery is the process of identifying and understanding customer and business needs to inform the development of successful products. At Miro, the product life cycle consists of three stages: the thinking stage, the building stage, and the shipping stage. The thinking stage focuses on validating the right product ideas, while the building stage ensures the selected ideas are executed effectively. The final stage involves evaluating the success of the product. Throughout this life cycle, continuous validation and discovery processes are crucial, enabling Miro to stay closely connected with its users.
Choosing Validation Strategies
When choosing a validation strategy, it is essential to consider the value and effort associated with each method. Julia emphasizes that validation should not be done just for the sake of it, but rather to derive meaningful insights. Thanks to advancements in AI, such as tools like Birdie, the effort involved in continuous discovery has been significantly reduced. Julia shares a set of criteria they consider when selecting the appropriate validation method:
- Product Maturity: The stage of the product, whether it's in the early phase (zero to one) or the optimization phase (one to 100), influences the choice of validation strategy.
- Risk Assessment: Understanding the types of risks involved, such as value risk or viability risk, helps determine the appropriate validation approach. Different risks require different methods of validation.
- Timing: Timing plays a crucial role in the validation process. Instead of front-loading all the learning at the beginning, a continuous learning approach is preferable. The timing of validation affects the cost and value associated with it.
- Learning Methods: Not all insights can be acquired solely through user conversations. Observation, data analysis, and leveraging the expertise of UX researchers can provide valuable information. Choosing the right research format for each specific learning objective is vital.
- Leveraging Existing Knowledge: It's important not to overlook what is already known. Previous research within the company or the team might hold valuable insights to be built upon. Reanalyzing data and user interviews can yield fresh perspectives without starting from scratch.
The Power of Intuition
While data-driven decision-making is crucial, Julia emphasizes the significance of intuition. Intuition should not be disregarded but instead complemented with quantitative and qualitative data. When intuition is combined with data, it strengthens insights and enables more actionable decision-making.
In the world of product management, there is no one-size-fits-all playbook. Product discovery requires a diverse toolbox of methods and approaches. By following the guidelines presented by Julia, product managers can enhance their understanding of customer needs, make informed decisions, and increase the chances of building successful products.
The continuous learning process, coupled with the right validation strategies and the integration of intuition and data, enables product managers to confidently navigate the complex landscape of product development.
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What is product discovery?
Product discovery is the process of identifying and understanding customer and business needs to inform which products to build. It is the thinking stage that comes before building and shipping, focused on validating that you are pursuing the right ideas. Done continuously, it keeps a team closely connected to its users rather than guessing at what to build.
How do you choose a product discovery validation strategy?
Match the method to value and effort rather than validating for its own sake, and weigh a few factors. Consider the product's maturity (zero to one versus optimizing one to 100), the type of risk you are testing (such as value or viability), and the timing, favoring continuous learning over front-loading everything upfront. Pick the research format that fits the specific learning goal, and reuse existing research before starting from scratch.
What's the difference between product discovery and product delivery?
Product discovery is about deciding what to build, validating ideas against customer and business needs before committing. Product delivery, or the building and shipping stages, is about executing the chosen ideas well and getting them to users. Discovery reduces the risk of building the wrong thing; delivery reduces the risk of building the thing wrong.
Does continuous product discovery actually lead to better decisions?
Yes, because spreading validation across the whole life cycle catches wrong assumptions early, when changing course is cheap. Instead of front-loading all learning upfront, teams test continuously and combine methods, which strengthens insight and makes decisions more actionable. The payoff shows up as fewer expensive mistakes and products that stay aligned with real user needs.
Do I still need product discovery if I trust my intuition?
Yes, intuition and discovery work best together rather than as substitutes. Intuition should not be dismissed, but on its own it is untested; pairing it with qualitative and quantitative data is what turns a hunch into an actionable, defensible decision. Discovery is how you check and sharpen your instincts, not a replacement for them.
Can product discovery rely only on customer interviews?
No, interviews are one method among several, and leaning on them alone leaves gaps. Not every insight comes from talking to users; observation, data analysis, and the expertise of UX researchers often reveal things conversations cannot. Effective discovery uses a diverse toolbox and picks the format that fits each specific question.
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