Product Analytics
Intro to Product Analytics
What every product manager needs to know about product analyticsSam provides a good introduction to product analytics, explaining core ideas like setting up event tracking, looking at data for existing products, predicting metric changes from new features, and how to integrate product analytics with customer interviews.
What Metrics to Track
Selecting the Right User MetricSequoia explains the value of a north-star metric (such as DAU, GMV, gross sends), and how to pick it by looking at the vision for a product, its usage, and competitive benchmarks.
The Only Metric That MattersJosh builds on the idea of selecting a north-star metric by explaining that vanity metrics (e.g. DAU) should be replaced by it with a metric that shows the user is getting value and that they are using a core value-based feature of the product.
The North Star PlaybookThis is a 7-chapter playbook that covers what is a north star, how to run workshop to establish one with your team, and case studies on north stars with Netflix and Amplitude.
Critical Metrics Every Product Manager Must TrackEvgeny gives 11 actual examples of metrics to track (which would be outside of your North Star Metric), across user engagement, business metrics, and customer service, with benchmarks.
How to Set Up Product Analytics
The Startup Founder’s Guide to AnalyticsTristan walks you through how your startup should be doing analytics at five different stages: Founding Stage (0 to 10 employees) Very Early Stage (10 to 20 employees) Early Stage (20 to 50 employees) Mid-Stage (50 to 150 employees) Growth Stage (150 to 500 employees)
The first 6 steps to homegrowing basic startup analyticsAndrew takes you through to how to think through the infrastructure for product analytics across 7 steps of a company's growth, from pre-product to the point analytics servers start to slow down from too much data.
Data Science for Startups: Tracking DataBen dives deeper than Andrew's post into how to track data, covering what type of data to collect about product usage, how to send data to a server for analysis, issues when building a tracking API, and some concerns to consider when tracking user behavior.
Closing the gap between data and product developmentFlora explains how product analytics data is collected as events, which are needed to aggregate into patterns of use. Intercom had 350 events with confusing names, making analytics difficult, so they switched to a naming structure with Actions, Objects, Places, and Owners in order to democratize analytics.
How to Set Up a Bottoms-Up SaaS Product Analytics StackScott takes you step-by-step through setting up an analytics stack from scratch that includes Segment, Mixpanel, HubSpot, AWS Lambda, PostgreSQL, Mode Analytics, and Amplitude.
Pitfalls of Working With Data
The Agony and Ecstasy of Building with DataJulie provides 3 pitfalls when working with data, and 4 pitfalls when performing a/b tests.
Product Analytics Case Studies
Product Analytics at SquareFan explains how the product analytics team is structured at Square, and then provides a case study on segmenting user types and digging into signup friction to increase conversion.
The 27 Metrics in Pinterest’s Internal Growth DashboardJohn shows the actual metrics used in Pinterest's primary growth dashboard, and why they selected each one.
