Diagnosing Weak Early Retention at ClickUp and Raising New-User Engagement

You are a product manager at ClickUp, the work and project-management app. Too few new users come back after they first sign up. Analyze what could be behind the low return rate, then propose solutions that would improve the new-user return rate, the average new-user sign-up, and average daily usage during a user's first 15 days. ### Clarifying Questions - Does "average new-user sign-up" mean the number of new accounts per day, or the share of people who start registration and finish it? - What counts as a return: any visit on a later day, or a meaningful action such as creating or updating a task? - Has the return rate dropped recently, or has it always been low compared with what the team expects? - Do most new users sign up alone to try the product, or arrive by invitation into an existing team workspace? ### Part 1 — Diagnose the low return rate Explain how you would find out why new users are not coming back, and which causes you think are most likely. ```hint Split before you explain One overall return rate can hide very different groups of new users. Decide which cuts of the data would show where the problem is concentrated before you commit to a cause. ``` #### What This Part Should Cover - Checks that rule out a measurement or definition problem before a product cause - Segments and funnel steps that would locate where new users drop off - Concrete hypotheses specific to a highly configurable work tool, each paired with the evidence that would confirm or reject it ### Part 2 — Propose solutions for the three metrics Propose the changes you would make to raise the return rate, the average new-user sign-up and the first-15-day daily usage, and say how you would know each change worked. ```hint Tie each fix to a moment Each of the three metrics breaks at a different point in a new user's first two weeks. Anchor every idea to the moment it changes and to the measurement that would show the effect. ``` #### What This Part Should Cover - Solutions tied to the causes from Part 1 and to the specific metric each one moves - A prioritization that weighs expected impact against effort and confidence - An experiment design with a primary metric and guardrails for each top idea ### What a Strong Answer Covers - Precise definitions of the three metrics and how a change in one, such as more sign-ups, can move the others - Segmentation that separates kinds of new users instead of treating them as one group - Solutions grounded in diagnosed causes rather than a generic list of onboarding ideas - Guardrails such as paid conversion and notification opt-outs, so gains are real rather than vanity ### Follow-up Questions - If a shorter onboarding lifts sign-up completion but day-7 return falls, what do you conclude and what do you do next? - How would you set a target for first-15-day usage without rewarding busywork or notification-driven visits? - How should the plan differ for someone who signed up alone versus someone invited by a teammate?