I started with clarifying the metric itself, like are we talking activation rate, signups, or something else entirely.
Start by clarifying what 'new user adoption' means at Box—likely activation and early engagement—and how it's measured. Then systematically diagnose the decline by segmenting the funnel and cohorts, forming hypotheses, and prioritizing fixes based on impact and effort. Finally, propose a test-and-learn plan with clear success metrics.
Pro tip: Show that you'd first validate the data and rule out measurement issues before jumping to solutions—many 'declines' are actually tracking or definition changes. Also, tie your analysis to Box's business model (B2B, seat-based) by considering admin and end-user adoption separately.
Clarify what 'new user adoption' means (e.g., sign-up, activation, first key action) and confirm the decline is real by checking data pipelines, definitions, and external factors.
Break down the metric by cohort, channel, plan type, geography, and device to isolate where the decline is concentrated. Analyze funnel steps to pinpoint drop-off points.
Generate hypotheses for the decline (e.g., onboarding friction, product changes, competitive pressure, pricing) and prioritize them by potential impact. Use qualitative and quantitative data to validate.
Based on validated hypotheses, prioritize fixes using an impact/effort matrix. Propose experiments (A/B tests, user research) to address root causes and measure results.
Set up ongoing monitoring with clear success metrics and guardrail metrics. Establish a feedback loop to continuously improve adoption and share learnings with stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.