Defining Success Metrics and a North Star for the Medium Publishing Platform

Quick Overview

A product metrics interview question that asks how you would measure whether Medium, the online publishing platform, is succeeding. It tests defining success across writers, readers and paid membership, choosing and defending a north-star metric, and pairing it with supporting metrics and guardrails.

Defining Success Metrics and a North Star for the Medium Publishing Platform

Company: X (Twitter)

Role: Product Manager

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Other

Medium is an online publishing platform: anyone can write and publish stories, and readers discover them, read them, react to them and follow the writers they like. How would you decide whether Medium is succeeding? Define what success means for the product and the metrics you would track to measure it. ```hint Start from the exchange between writers and readers Medium only works when writers keep publishing and readers find stories worth their time. Decide what a successful exchange between those two groups looks like, and how the business benefits from it, before you pick a number. ``` ### Clarifying Questions - Do you want success for Medium as a whole, or for one part of it, such as the reading experience, the writer side or paid membership? - Should success include revenue from paid memberships, or only user value and engagement? - Is this measurement meant to steer a specific decision, such as a quarterly goal or a review of how stories are recommended? - Which readers count most: visitors who arrive from search and social links, or signed-in readers who come back on their own? ### What a Strong Answer Covers - Medium's goal and how value flows among writers, readers and the business that funds the platform. - A north-star metric that captures value delivered, justified against easy alternatives such as page views or monthly active users. - Supporting metrics for each side: reader engagement and retention, writer activity and retention, quality of discovery, and membership. - Guardrails against clickbait, spam and attention concentrating on a few writers, plus how the metrics are segmented and acted on. ### Follow-up Questions - Reading time keeps rising while new paid memberships fall. What would you investigate, and in what order? - After a change to recommendations, total reading goes up but most newer writers get fewer reads. Do you keep the change? - How would you tell whether your north-star metric is being inflated by clickbait or by very long, low-quality stories?

Overview: A product metrics interview question that asks how you would measure whether Medium, the online publishing platform, is succeeding. It tests defining success across writers, readers and paid membership, choosing and defending a north-star metric, and pairing it with supporting metrics and guardrails.

X (Twitter) logo
X (Twitter)
Jun 9, 2024
mediumProduct ManagerOtherAnalytics & Experimentation
0
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Medium is an online publishing platform: anyone can write and publish stories, and readers discover them, read them, react to them and follow the writers they like. How would you decide whether Medium is succeeding? Define what success means for the product and the metrics you would track to measure it.

Clarifying Questions Guidance

  • Do you want success for Medium as a whole, or for one part of it, such as the reading experience, the writer side or paid membership?
  • Should success include revenue from paid memberships, or only user value and engagement?
  • Is this measurement meant to steer a specific decision, such as a quarterly goal or a review of how stories are recommended?
  • Which readers count most: visitors who arrive from search and social links, or signed-in readers who come back on their own?

What a Strong Answer Covers Guidance

  • Medium's goal and how value flows among writers, readers and the business that funds the platform.
  • A north-star metric that captures value delivered, justified against easy alternatives such as page views or monthly active users.
  • Supporting metrics for each side: reader engagement and retention, writer activity and retention, quality of discovery, and membership.
  • Guardrails against clickbait, spam and attention concentrating on a few writers, plus how the metrics are segmented and acted on.

Follow-up Questions Guidance

  • Reading time keeps rising while new paid memberships fall. What would you investigate, and in what order?
  • After a change to recommendations, total reading goes up but most newer writers get fewer reads. Do you keep the change?
  • How would you tell whether your north-star metric is being inflated by clickbait or by very long, low-quality stories?