Start by outlining a systematic root cause analysis: define the problem, gather data, form hypotheses, and test them. Then, when you narrow it down to new non-prime visitors, focus on segment-specific factors like onboarding, pricing, or UX, and propose targeted experiments to validate and fix.
Pro tip: Show that you balance data-driven investigation with customer empathy, and always tie findings back to business impact and actionable next steps.
Clarify what 'site performance dropped' means: which metrics (e.g., page load time, conversion rate, bounce rate), when it started, and how it was detected. Establish a baseline and impact.
Pull data from monitoring tools, analytics, and logs. Segment by user type (prime vs non-prime, new vs returning), device, geography, and traffic source to identify patterns.
Generate hypotheses for the drop (e.g., recent code deploy, infrastructure issue, third-party script, UX change). Use A/B tests, canary releases, or correlation analysis to validate.
If this segment is disproportionately affected, investigate their unique journey: onboarding, pricing visibility, shipping options, or personalized content. Compare their experience to prime users.
Based on findings, prioritize fixes by impact and effort. Propose experiments (e.g., UX tweaks, targeted messaging) to improve performance for this segment, and define success metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.