Product Analytics & Metrics Course in Barcelona

Turning Product Data into Actionable Insights and Measurable Business Results

Understanding how customers interact with products is essential for improving user experiences, strengthening retention, and achieving sustainable business growth. Product analytics enables organisations to move beyond assumptions by using behavioural data, performance metrics, and structured analysis to identify opportunities and make informed product decisions.

The Product Analytics & Metrics course provides participants with a practical understanding of product analytics processes, data collection, performance measurement, and analytical frameworks. The programme explores AARRR and RARRA frameworks, product metrics, customer segmentation, cohort analysis, retention analysis, funnel analysis, and the use of North Star metrics to align product activities with business objectives.

Through practical exercises and product scenarios, participants learn how to translate user data into actionable insights, evaluate customer behaviour, identify performance gaps, and design product experiments. The course also examines A/B testing, data governance, and the organisational practices needed to establish a data-driven product culture.

You'll Learn How To

• Understand the principles and processes of product analytics.
• Translate product goals and user data into actionable insights.
• Apply AARRR and RARRA frameworks to structure product analysis.
• Identify relevant data sources and apply effective data collection practices.
• Define product KPIs, leading indicators, and lagging indicators.
• Analyse customer segments using RFM and customer lifetime value concepts.
• Apply cohort analysis to understand customer behaviour over time.
• Evaluate retention, churn, and repeat purchase performance.
• Use funnel analysis to identify conversion barriers and improvement opportunities.
• Develop North Star metrics and KPI trees to align product performance with business objectives.
• Design A/B tests and interpret experimentation results.
• Apply data governance principles and promote data-driven decision-making.