Product design question
How would you improve WhatsApp Channels?
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Whether the candidate can identify concrete gaps in an existing one-way broadcast feature and propose improvements grounded in creator and follower needs.
How to approach it
- Identify the two users: channel admins (creators, brands) broadcasting updates, and followers consuming them.
- Name pain points: limited discoverability of new channels, no threaded replies for followers to engage, and weak analytics for admins to understand what resonates.
- Prioritize by impact: discoverability likely drives channel growth more than any single engagement feature.
- Propose improvements: a recommendation surface based on interests, lightweight reactions or polls for followers, and basic engagement analytics for admins.
- Define success as new follower growth rate per channel and admin retention (percent still posting after 90 days).
What a strong answer includes
- Separates admin-side pain points (analytics, growth tools) from follower-side pain points (discovery, engagement), addressing both.
- Proposes lightweight, one-way-compatible engagement (reactions, polls) rather than turning Channels into a full two-way chat, preserving its broadcast simplicity.
- Ties the fix to a growth metric, new follower rate, since discoverability is the biggest lever for a broadcast product.
Common mistakes
- Proposing to turn Channels into a group chat, which undermines its simple broadcast value proposition.
- Ignoring the admin side and focusing only on the follower experience.
Likely follow-up questions
- How would you avoid channel spam if you improve discoverability?
- What would you measure to know an admin is at risk of abandoning their channel?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop