Product Analytics: tools and metrics for your product

Course overview

Description generated based on course syllabus and open data.

Product analytics covers data collection, processing and analysis for evidence‑based decisions. The program combines behavioral segmentation, cohort approach, A/B testing, the TARS framework for feature assessment, and financial aspects such as unit economics and P&L.

Who benefits from product analytics and when product analysis is not suitable

Best suited for

• Analysts and product managers working with acquisition, retention and monetization metrics.
• Marketing specialists who need end‑to‑end product analytics across web and app.
• BI/data engineers integrating GA4, GTM, Amplitude and Looker Studio.
• Teams planning or running A/B tests that require statistically correct conclusions.

May be unsuitable

• If the product does not collect event data or lacks basic tracking.
• When decisions rely solely on intuition without measurement readiness.
• Without team‑level access to tools (GA4/GTM/Amplitude/BI).

Problem → outcome: product analytics in practice

• Scattered metrics → a coherent metric tree with derived indicators from Revenue down to user actions.
• Unclear feature impact → TARS framework to assess feature quality and effects on target metrics.
• Conflicting A/B conclusions → planning, power calculations, statistical significance and effect interpretation.
• Opaque retention → cohort analysis, N‑day and Unbounded Retention, Lifecycle and Usage Interval.
• Financial confusion → reading P&L and applying unit economics for planning and efficiency checks.
• Fragmented reporting → Looker Studio dashboards fed by Adwords, GA4 and event data.

Comparison with alternatives: other ways to handle product analytics

• Self‑study via docs: flexible, yet often misses the systemic link between metrics and business context.
• Marketing‑only analytics: covers channels, but not in‑product behavior and retention drivers.
• BI without product focus: solid dashboards, but risk losing the link between experiments, features and hypotheses.
• Intuition‑led decisions: fast early on, yet lacks hypothesis validation and repeatability.

Competencies and learning outcomes within product analytics

Metrics and methods for product analysis

• Metric tree design, selecting core and derived KPIs for a specific product.
• Segmentation, cohort analysis, Retention/Lifecycle, Pulse metrics.
• A/B test planning and evaluation, statistical fundamentals and effect interpretation.

Product analytics tools

• Amplitude: event schema, behavioral charts, retention and cohort reporting.
• GA4 and GTM: tracking setup, events and attribution.
• Looker Studio (Google Data Studio): dashboards and visualization across data sources.

Financial analytics for products

• P&L reading and essentials of financial analysis for product contexts.
• Unit economics for planning and evaluating metrics at segment level.

Instructor context

• Experience with large user bases, end‑to‑end web/app analytics, and structured testing and prioritization.
• Background in building product‑financial analytics, growth approach and roadmap ownership.