Product Analyst

DO:

• •Include key KPIs improved (activation, retention, conversion).
• •Show data tools (SQL, Python, Mixpanel, GA4).
• •Highlight A/B testing frameworks and results.
• •Mention collaboration with product and growth teams.

DON'T:

• •Skip actual metrics or conversion percentages.
• •Use vague language like 'analyzed data' without context.
• •Forget to mention tools used for insights.
• •Ignore reporting automation achievements.

DO:

• •Highlight number of experiments designed and analyzed.
• •Show business impact in terms of revenue or adoption.
• •Mention visualization tools (Tableau, Power BI).
• •Include collaboration with CXOs and product managers.

DON'T:

• •Forget to include statistical rigor or monitoring methods.
• •Overload with tools not used in experimentation.
• •Skip business outcome linkage.
• •Use unclear hypothesis-driven metrics.

DO:

• •Include automation impact (time saved).
• •List tools (BigQuery, Looker, Redash).
• •Mention cross-functional use cases for dashboards.
• •Quantify turnaround time improvements.

DON'T:

• •List dashboards without users or metrics impacted.
• •Ignore self-serve analytics enablement.
• •Forget to mention adoption of insights decks.
• •Leave out NPS/churn/retention impact.

Performance Metrics:

• •Improved conversion by 12% through A/B testing
• •Reduced cart abandonment by 20%
• •Improved retention by 15% via cohort analysis
• •Automated key product metrics reporting

Scale Metrics:

• •Designed experiments for 4+ squads
• •Analyzed 25+ product experiments
• •Influenced $2M in incremental revenue
• •Delivered insights across 3 product lines

Business Metrics:

• •Boosted activation by 18% within 6 months
• •Increased feature adoption by 35%
• •Supported upsell flows generating $2M
• •Reduced churn by 10% through targeted interventions

Product Analytics:

Funnel Analysis, A/B Testing, Cohort Analysis, Growth Metrics

Tools:

Mixpanel, Amplitude, GA4, BigQuery, Tableau, Power BI

Languages:

SQL, Python, pandas, numpy