Definition
Product analytics is the process of collecting, measuring, and analysing data about how users interact with a product. It helps businesses understand usage patterns, identify friction points, and make informed decisions to improve customer experience and drive growth. Product analytics goes beyond basic web analytics by focusing on user behaviour within apps, platforms, or software.
For example, a SaaS company may use product analytics to track how often customers use a new feature and whether it increases retention.
Advanced
Product analytics platforms capture event-based data such as clicks, page views, feature adoption, and user flows. They support cohort analysis, funnel tracking, segmentation, and retention studies. Tools like Amplitude, Mixpanel, and Pendo allow teams to visualise data, run A/B tests, and measure outcomes of product changes.
Advanced product analytics involves integrating data from CRM, ERP, and customer feedback systems to build a complete view of the customer journey. Machine learning models can predict churn risk, recommend personalised features, or identify high-value user cohorts. Teams often connect product analytics to experimentation frameworks and roadmaps to guide prioritisation.
Why it matters
- Provides evidence-based insights for product decisions.
- Improves user experience by identifying pain points.
- Increases retention and engagement by tracking behaviour over time.
- Aligns product strategy with customer needs and business outcomes.
Use cases
- Measuring adoption rates of new product features.
- Tracking conversion through user onboarding funnels.
- Identifying cohorts with high churn risk.
- Running experiments to validate product improvements.
Metrics
- Daily active users (DAU) and monthly active users (MAU).
- Feature adoption and usage frequency.
- Customer retention and churn rates.
- Net Promoter Score (NPS) linked to product usage.
Issues
- Data overload without clear tracking goals.
- Privacy and compliance risks when collecting user data.
- Misinterpretation of metrics leading to poor product decisions.
- Difficulty aligning analytics with cross-team priorities.
Example
A mobile app company introduces a new social sharing feature. Product analytics shows that adoption is low because users struggle to find the option. After repositioning the feature in the interface, usage doubles and overall engagement improves.