Product Sense Observation Loop
Build product judgment by observing, comparing perspectives, and testing hypotheses.
- Difficulty
- Moderate
- Time to result
- ~ongoing to results
- Steps
- 6
- Confidence
- 96%
Zhuo describes product sense as a loop of observation, comparison, and validation. Begin with your own experience: notice what led you to try a product, when its purpose became clear, where you became confused, and which path you took. Then widen the sample by discussing the same product with other people and critiquing the decisions its builders made. Study deep product analyses to expose yourself to more patterns. Finally, connect qualitative reactions to quantitative evidence, especially experiments that can show a causal result. Neither aesthetics nor numbers are sufficient alone. Repeatedly linking decisions, user reactions, and measured outcomes builds an evidence-informed instinct for what works.
Origin
Zhuo developed product judgment while building at Facebook, where teams could combine close product critique with extensive experimentation. She argues that disciplined observation and studying experiment results both contribute to product sense, rather than placing design judgment and data on opposing sides.
Core principles
- 01Product sense begins with disciplined observation.
- 02Your experience is useful but does not represent the world.
- 03Comparing reactions reveals the impact of product decisions.
- 04Qualitative observations and quantitative evidence strengthen each other.
- 05Experiments turn plausible stories into causal learning.
How to run it
- 1
Observe your own experience
Slow down during a new product experience and notice your assumptions, emotions, confusion, and actions. Mark the point where the product's purpose or value becomes clear.
Pro tip Capture the experience while it is fresh because familiarity quickly hides first-use friction.
Watch out Going through the motions produces memory of the outcome but little learning about the journey.
- 2
Trace the path into the product
Reconstruct what led you to try it, such as a friend, online mention, or trusted recommendation. Acquisition decisions are part of the product experience, not separate from it.
Pro tip Include the expectation you carried into the first session.
- 3
Compare perspectives
Ask other people why they tried the product, what they found compelling, and where their experience differed. Discuss which builder decisions may have created those reactions.
Pro tip Make product dissection a recurring conversation rather than an occasional exercise.
Watch out Your own experience is a starting point, not a representative sample.
- 4
Study expert dissections
Read detailed analyses of successful and unsuccessful products. Look for recurring patterns and how analysts connect micro-decisions to user behavior.
Pro tip Compare analyses across several products instead of memorizing one company's tactics.
- 5
Form a causal hypothesis
Translate observations into a prediction about why a decision produced a reaction. State what behavior should change if the explanation is correct.
Pro tip Distinguish correlation from the causal relationship you are proposing.
- 6
Validate with data
Use A/B tests, behavioral data, or lessons from other teams' experiments to test the hypothesis. Feed the result back into the next round of observation and critique.
Pro tip Ask neighboring teams what their experiments taught them so your pattern library grows faster.
Watch out Numbers can test assumptions, but they do not automatically supply the creative leap for a new product.
In the wild
A product manager downloads a new app and records what caused the download, the expectation created before launch, the first moment of clarity, each wrong turn, and each point of confusion. They compare those notes with another user's experience before deciding which product choice mattered.
→ A vague opinion becomes a set of testable hypotheses about acquisition, onboarding, and navigation.
Zhuo recommends asking product managers on other teams what they are learning from A/B tests. By studying the decision that changed and the measured behavior that followed, a practitioner can add causal patterns beyond the experiments on their own roadmap.
→ Repeated exposure to experiment results improves instinct about which product changes are likely to work.
Common mistakes
Assuming you represent everyone
Personal observation is valuable, but it cannot stand in for users with different contexts, needs, and expectations.
Separating design from data
Treating qualitative judgment and quantitative evidence as opponents discards the way each can test and strengthen the other.
Collecting reactions without hypotheses
Opinions become more reusable when they are translated into an explanation that data or an experiment can challenge.
Is it for you?
Best for
Product managers, designers, founders, and engineers who want stronger judgment across unfamiliar products.
Not ideal for
Teams looking for a single universal design rule without observing their own users or validating assumptions.
From the episode
Julie Zhuo on accelerating your career, impostor syndrome, writing, building product sense, using intuition vs. data, hiring designers, and moving into management