Is Amplitude free for small teams?

Amplitude's Starter plan is free and includes up to 50,000 monthly tracked users. This is enough for most early-stage products. The Growth plan starts at custom pricing based on your event volume.

What's the difference between Amplitude and Google Analytics?

Google Analytics focuses on website traffic and acquisition metrics. Amplitude focuses on in-product user behavior: what features people use, where they drop off, how they retain over time. PMs typically need both.

How long does it take to set up Amplitude?

Basic setup (SDK installation and first events) takes a few hours. A complete tracking plan with 20 to 30 events, user properties, and initial dashboards takes one to two weeks, including engineering coordination.

Product Analytics with Amplitude: A PM's Guide

TL;DR: Step-by-step guide to using Amplitude for product analytics. Event tracking, funnel analysis, retention charts, and best practices for product managers.

Why Amplitude for Product Analytics

Amplitude has become the default product analytics tool for growth-stage and enterprise product teams. Its strength lies in behavioral analytics: understanding what users do inside your product, not just how they arrive. Funnels, retention curves, user paths, and cohort analysis are first-class features that require minimal setup compared to building equivalent reports in a general analytics tool. If you are evaluating other platforms, see the product analytics hub for tool comparisons, or read our companion Mixpanel setup guide for a side-by-side perspective.

For product managers specifically, Amplitude answers the questions that matter most. Which features drive retention? Where do users drop off in onboarding? What distinguishes power users from churned users? These insights feed directly into roadmap prioritization and help you build a data-informed product practice.

Setting Up Amplitude for Your Product

Step 1: Define Your Tracking Plan

Before writing any code, document the events and properties you want to track. A solid tracking plan for most SaaS products includes:

Core Events:

Sign Up Completed
Onboarding Step Completed (with step_name property)
Feature Used (with feature_name property)
Subscription Started
Subscription Cancelled
Invite Sent

User Properties:

Plan type (free, pro, enterprise)
Company size
Role
Sign-up date
Activation status

Event Properties:

Feature name
Source (where the user came from)
Duration (time spent)
Success/failure

Keep your tracking plan to 20 to 30 events initially. You can always add more. Too many events create noise and make analysis harder.

Step 2: Implement the SDK

Work with your engineering team to install the Amplitude SDK. Choose the right SDK for your stack (JavaScript, React, iOS, Android, Node.js). For web apps, the JavaScript SDK is the most common:

amplitude.track('Feature Used', {
  feature_name: 'dashboard_export',
  format: 'csv',
  row_count: 150
});

Set up user identification early. Call amplitude.setUserId() at login and amplitude.setUserProperties() to attach properties like plan type and company size. This enables segmentation in every report.

Step 3: Build Your First Dashboard

Create a dashboard called "Product Health" with these charts:

Daily/Weekly/Monthly Active Users (line chart, segmented by plan)
Activation Funnel (funnel chart: Sign Up > Onboarding Complete > First Value Action)
Feature Adoption (bar chart: event counts for top features)
Retention Curve (retention chart: Day 1, Day 7, Day 30 retention)
Power User Metric (percentage of users performing your core action 3+ times per week)

Key Amplitude Features for PMs

Funnel Analysis: Build conversion funnels to see where users drop off. Start with your activation funnel and optimize the biggest drop-off points. Amplitude shows conversion rates between each step, time to convert, and segment breakdowns. Use these insights to feed your product metrics tracking.

Retention Analysis: Amplitude's retention charts show what percentage of users return after Day 1, Day 7, Day 30, and beyond. Segment by cohort (sign-up week), plan type, or feature usage. The goal is to find which actions correlate with higher retention, then optimize your product to drive those actions.

User Paths: The Pathfinder feature shows the most common sequences of events. Use it to discover how users naturally navigate your product versus how you designed them to navigate. Unexpected paths often reveal UX issues or unmet needs.

Behavioral Cohorts: Create cohorts based on behavior (e.g., "users who exported data in the last 7 days") and track how they differ from the general population. These cohorts can be synced to your marketing tools for targeted messaging.

Building a Prioritization Workflow with Amplitude Data

Amplitude data should directly inform your RICE scoring. Here is how to connect analytics to prioritization:

Reach: Use Amplitude's event counts to estimate how many users a feature improvement would affect. If 5,000 users per month use the dashboard, improvements to the dashboard have a Reach of 5,000.

Impact: Analyze the correlation between feature usage and retention. If users who export data retain 40% better than those who do not, export improvements have high Impact. Amplitude's "Impact Analysis" feature calculates these correlations automatically.

Confidence: Base your Confidence score on data quality. If you have 6 months of clean data supporting your hypothesis, Confidence is high. If you are extrapolating from two weeks of noisy data, Confidence is low.

Effort: This comes from engineering, not analytics. But Amplitude can help scope effort by showing the volume and complexity of the feature being modified.

Run this analysis before each planning session and bring the data to the prioritization meeting. Data-backed RICE scores are harder to argue with than gut-feel estimates.

Common Mistakes

Tracking everything from day one. Fifty events with inconsistent naming creates a mess that nobody trusts. Start with 15 to 20 well-defined events. Add more as questions arise that the current tracking cannot answer.

Not naming events consistently. "Button Click" and "button_click" and "btn_clicked" are three different events in Amplitude. Define a naming convention (noun_verb, snake_case) and enforce it in your tracking plan.

Ignoring user properties. Events without user context are hard to segment. Always set plan type, company size, and role as user properties so you can filter every chart by these dimensions.

Looking at vanity metrics. Daily Active Users is meaningless without context. Pair DAU with retention rates, feature adoption, and activation metrics to get the full picture. Focus on metrics that matter for your stage and business model.

Not sharing dashboards with the team. Analytics are only useful if the team looks at them. Share your Product Health dashboard in Slack weekly. Make it part of your sprint review ritual.

Complementary Tools and Resources

Strengthen your analytics practice with these resources:

Use the RICE Calculator to translate analytics insights into prioritization scores
Map your acquisition-to-revenue funnel with the AARRR Funnel Calculator
Read the complete guide to product metrics for a framework on which metrics to track
Learn about A/B testing to validate hypotheses that emerge from your analytics
Follow the product discovery guide to combine quantitative analytics with qualitative research
Browse the product analytics hub for more calculators, glossary terms, and tool comparisons
Compare Amplitude to alternatives in the best product analytics tools for 2026 review

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