Amplitude

Amplitude is the leading product analytics platform, helping digital businesses understand how users engage with their products, identify what drives retention and revenue, and run experiments to improve key metrics. It's used by Shopify, Peloton, Dropbox, and thousands of other product teams to move from gut-feel product decisions to data-driven feature development. Amplitude's AI features include predictive analytics for churn, automated chart generation, and behavioral cohort analysis.

Behavioral Event Analytics

Amplitude captures every user interaction as an event — button clicks, page views, feature uses, and conversions — and enables teams to query these events in real time. Product managers build funnels showing exactly where users drop off before converting, create cohorts of users who performed specific actions, and measure how feature changes affect downstream behavior through retention and revenue metrics.

AI-Powered Predictive Analytics

Amplitude's predictive capabilities use machine learning to identify users likely to churn, convert, or reach high-value milestones before they do. Teams use these predictions to trigger targeted campaigns — reaching churning users with win-back offers, or identifying power users for beta program recruitment. Predictions are generated from behavioral signals without requiring data science expertise to configure.

Experiment and A/B Testing Integration

Amplitude Experiment connects product experiments directly to behavioral analytics — run an A/B test and immediately see the impact on retention, activation, and revenue metrics rather than just simple conversion rates. The integrated experiment-to-analytics workflow eliminates the data silos between experiment tools and analytics platforms that make measuring true experiment impact difficult.

Session Replay and Visual Insights

Amplitude Session Replay records user sessions that can be analyzed directly alongside quantitative funnel data. When a funnel shows 60% drop-off on step 3, teams click into Session Replay to watch exactly what users encounter at that step. This qualitative-quantitative integration closes the "what happened" vs "why it happened" gap without switching between separate tools.