The Mixpanel Product Manager

“What was the moment this product clicked?” — share yours

A product manager or growth lead at a B2C or B2B SaaS company for whom Mixpanel is the primary lens on user behavior. They are not a developer. They understand events and properties well enough to answer most of their questions self-service. They have a set of saved reports they look at every Monday. They also have questions that require a data analyst to answer — and they're slowly working to reduce that list.

• →Understand where users drop out of key flows and why
• →Know whether a new feature is being adopted and by whom
• →Track retention by cohort without it requiring a data request

• —Funnel reports that show a drop-off but can't tell them what the user did instead
• —Events that were instrumented inconsistently making the data unreliable
• —Sharing reports with stakeholders who can't interact with Mixpanel themselves
• —The moment they need a metric that requires joining two event streams — that's a SQL request

• The question "what are users doing?" should have a self-service answer
• A metric without a denominator is decoration
• Retention is the only growth metric that actually matters in the long run

Uses Mixpanel 4–5 days per week. Has a Monday dashboard review ritual. Builds reports using Insights, Funnels, Retention, and Flows. Shares reports via Mixpanel links that recipients can't always access. Works with a data team that handles requests that require SQL. Has been in conversations about migrating to a different tool twice — hasn't moved. Has a set of saved reports that represent institutional memory about what was measured when and what was found. Knows that if they left, most of that context would leave with them.

• →Report exports that preserve interactivity for stakeholders who aren't Mixpanel users
• →remove the screenshot-and-explain loop
• →User path analysis that shows what users did after a drop-off (not just that they dropped)
• →closes the biggest analytical gap in funnel investigation
• →Proactive instrumentation coverage reports that surface unmeasured user flows
• →reduce the "I need this metric but the event wasn't tracked" discovery
• →Cohort definitions that stay current without manual rebuilding every quarter

Pairs with `posthog-primary-user` for the PM vs. engineer analytics tool philosophy comparison. Contrast with `data-analyst` for the self-service analytics ceiling and where SQL begins. Use with `jira-primary-user` for the product review workflow that connects metrics to feature decisions.