Figma · Metrics · Mid · 45 to 60 minutes

How should Figma improve comments?

Figma is a collaborative design and prototyping tool where teams create and review digital work together in the browser. Answer this product management case and get it graded line by line against a weighted rubric.

metrics design
experiment design
product judgment
tradeoff analysis
cross functional planning

The situation

You are a PM on Figma's collaboration area, and your product lead asks you to recommend how the team should improve the commenting experience. Comments are central to how designers, product managers, and engineers review work together, but the team does not yet know which changes would meaningfully improve collaboration versus simply change UI. Assume comment usage is healthy overall, but many threads end after one message and some users report friction in starting, finding, and resolving discussions. Design has several ideas, data science can support experiments, and marketing wants enough confidence to tell a clear story if the work becomes a launch. The hard part is that comments are already part of active workflows, so a large redesign could create risk, while only making tiny tweaks could miss a bigger opportunity. Your task is to propose how Figma should use metrics and experiments to decide what to build, what to test first, and how to know whether the work is helping collaboration rather than just generating more clicks.

What to produce

Write a 1 to 2 page experiment and metrics brief for the comments product area.

Your answer must

State the product problem in terms of a collaboration behavior you want to increase, not just an interface complaint.
List 3 to 5 testable hypotheses about the comments experience, and explain why each could change user behavior.
Propose a sequence of experiments, from smaller and faster tests to larger tests if warranted, and say what decision each stage would unlock.
Define a primary success metric, 2 to 4 supporting metrics, and at least 2 guardrail metrics, including clear event level definitions or proxies.
Explain how you would segment results, document learning across tests, and decide when the evidence is strong enough to move toward a broader launch plan with design, data, and marketing.

Constraints

You only have the summary above, so do not assume any real internal numbers or prior Figma findings beyond what is stated.
Comments are already used in live collaboration workflows, so your plan should minimize disruption to active teams.
The team can ship incremental product changes before committing to a full redesign, but cannot build two completely separate comment systems in parallel.
If you introduce numbers or baselines, label them clearly as illustrative assumptions.

Format

Use sections titled Problem, Hypotheses, Metrics, Experiment Plan, Risks, and Decision Rule.

How Figma actually played it

A Figma data scientist explains how a series of small experiments on commenting grew into a full redesign launch. The post covers the hypotheses, the tests at different scales, and what was learned before rollout. It shows how a PM can use experiments to shape a feature, not only to validate a finished one.

Mix quick insight-driven iterations with larger experiments depending on the risk.
Small tests can add up into a product direction, so keep the learning log.
Tie each hypothesis to a collaboration behaviour you want to increase.
Involve data, design and marketing together when a test turns into a launch.

Built from From experiment to launch: how data shaped a new comments experience by Emily Jia, Figma.