📊 Free Guide

The Product Analytics Handbook

Twelve chapters covering everything a product manager needs to measure, analyze, and act on product data. From setting up your first metrics framework to building a data-informed culture across your team.

12Chapters
45+Sections
30+Formulas & benchmarks
40+Cross-references

What You'll Learn

Define metrics that matter

Set up AARRR, North Star, and HEART frameworks tailored to your product stage and business model.

Instrument your product correctly

Design an event taxonomy that captures user behavior without drowning in noise.

Run valid experiments

Calculate sample sizes, set up A/B tests, and interpret results without a statistics PhD.

Diagnose retention and churn

Build cohort analyses and retention curves that pinpoint where and why users drop off.

Build dashboards that drive action

Design dashboards stakeholders actually use, organized by decision rather than data source.

Create a data-informed culture

Move your team from gut instinct to evidence-based product decisions without slowing down.

12 Chapters Inside

1

Product Analytics Fundamentals for PMs

Establish the core concepts: what product analytics measures, how it differs from business intelligence, and the mental models that separate data-informed PMs from data-drowning ones.

4 sections
2

Setting Up Your Metrics Framework

Learn three proven frameworks for organizing product metrics, how to pick the right one for your product stage, and how to cascade a single North Star Metric into team-level KPIs.

5 sections
3

Event Tracking: What to Track and How

Learn how to design a clean event taxonomy, choose between auto-track and manual instrumentation, and avoid the tracking debt that cripples most analytics setups.

4 sections
4

Funnel Analysis and Conversion Optimization

Master funnel construction, identify where users drop off, calculate conversion rates correctly, and prioritize fixes that move business metrics.

4 sections

Who This Guide Is For

🎯

Product Managers

PMs who want to move beyond vanity metrics and make product decisions backed by real user data.

📈

Product Leaders

VPs and Directors who need to establish analytics practices, set team-wide KPIs, and review dashboards with confidence.

🔄

Growth & Data-Adjacent PMs

Growth PMs, analysts transitioning to product, and anyone who collaborates with data teams and needs shared vocabulary.

TA
Written by
Tim Adair

Tim Adair has led product teams at early-stage startups and growth-stage companies, shipping analytics infrastructure, experimentation platforms, and data-informed product strategies.

Frequently Asked Questions

Do I need SQL or Python to follow this guide?
No. The guide is written for product managers, not data engineers. We explain formulas in plain language and link to interactive calculators where you can plug in your own numbers. SQL examples appear in a few places for context, but you can skip them and still apply every concept.
Which analytics tool should I use?
Chapter 10 covers the full tool landscape. The short answer: it depends on your team size, budget, and technical resources. Amplitude and Mixpanel are strong for product analytics; Google Analytics 4 works for marketing-heavy products; PostHog is a solid open-source option. The frameworks in this guide are tool-agnostic.
How is this different from a data science textbook?
Data science texts focus on building models. This guide focuses on making product decisions. We cover just enough statistics for you to run valid experiments and interpret results, then spend the rest of the time on frameworks, dashboards, and organizational practices that turn data into shipped improvements.
What if my company has no analytics infrastructure yet?
Start with Chapter 3 (Event Tracking). We walk through designing an event taxonomy from scratch, choosing your first tool, and implementing tracking with minimal engineering effort. You can have useful data within a week.
Is this guide relevant for B2B SaaS products?
Yes. B2B SaaS examples appear throughout, including account-level metrics, low-volume experiment approaches, and retention analysis for products with long sales cycles. Chapter 5 specifically covers cohort analysis techniques for B2B products with smaller user bases.

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