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Self-serve product analytics with pre-built templates to get key product metrics quickly, plus the ability to connect quantitative insights with Session Replay.

Understand conversion and drop off for key events with Amplitude’s Funnel Analysis chart.

Analyze how often users return to your product after taking a specific action using the Retention Analysis chart.

Analyze sequences of steps taken by users to understand the paths they take and why they convert, using multiple Journeys visualizations.

Automatically group users based on their actions so teams can connect behaviors to business outcomes and target consistently across the platform.

Create dashboards and notebooks to collaborate on insights and reporting, and turn analysis into shareable narratives.

Instrument your website with one line of code to capture rich web insights with default tracked events and analyze metrics like sessions, clicks, traffic, and conversions.

Automatically bucket UTMs and referrers into channels to organize acquisition traffic and understand what’s driving conversion.

Use multi-touch attribution to evaluate which acquisition strategies shine and connect campaign performance to outcomes.

Reconstruct digital experiences and launch replays from analytics workflows (including charts and event streams) to get the “why” behind your metrics.

Enable heatmap capture and manage heatmaps alongside replay capture across projects via Session Replay settings.

Configure masking (including out-of-the-box privacy levels) and retention controls to align Session Replay capture with legal, security, and privacy requirements.

Enable or disable functionality without deploying new code; use flags for both experiments and progressive rollouts (including fast rollbacks).

Run product experiments using feature flags to display/hide functionality or A/B options for specific audiences, using the Experiment SDK or REST API.

Create website experiments with a visual editor to alter web elements and scope variants to specific URLs/pages without engineering resources.

Visualize trends and compositions in your data with flexible charting; advanced capabilities include formulas, comparisons to past periods, and creating custom events/properties for deeper analysis.

Measure conversion through a series of events to find drop-off points, compare segments, and analyze time-to-convert within a defined window.

Identify the most frequent paths users take to or from any event to understand sequential behavior and where journeys diverge or drop off.

Assess engagement over time by measuring whether users come back and do a return behavior; supports retention rate and other measurements like property sum/average.

Visually define and manage user cohorts based on properties and behaviors, then use them to filter/break down analysis and share segments across the team.

Quantify the correlation between a “correlation event” and a “goal event” to discover behaviors associated with conversion/retention outcomes.

Watch playbacks of user experiences and move from quantitative drop-offs to qualitative “why” by jumping into replays from Mixpanel workflows (e.g., reports and profiles).

Visualize click engagement on web pages using Click Maps and Traditional Heatmaps, powered by Session Replay data to spot hotspots and friction quickly.

Automatically capture web events with minimal setup (e.g., page views, button clicks, form interactions) including dead clicks and rage clicks.

Compute “Session Start” and “Session End” from event data (no special tracking required) with configurable session definitions and session duration metrics.

Go beyond page views to understand website journeys (from first click to purchase), including where users drop off and which channels drive ROI.

Measure the impact of A/B tests by comparing variants on success metrics, analyzing lift and statistical significance, and supporting an experiment workflow from setup to decisions.

Control who sees a feature and when (gradual rollouts, cohort/region/platform targeting, and kill-switches) and serve variants to support experimentation workflows.

Build a “live” logical framework that shows relationships between metrics and inputs/outputs, adding context and accountability to strategic decision-making.

Turn natural language questions into Mixpanel reports, iterate with follow-up prompts, and “see how the report was created” for transparency.

A single snippet automatically captures user interactions (e.g., clicks, pageviews, swipes/taps, and form fills) from installation forward—so you don’t have to predefine a tracking plan.

Answer new questions later using historical data captured since install (“lookback”/retroactive dataset), without waiting for new tracking to ship.

Works across web and mobile so teams can analyze end-to-end journeys across platforms with a unified behavioral dataset.

Smart Capture auto-collects key mobile behaviors (e.g., taps, screen views, text inputs) across native and cross-platform frameworks—no instrumentation required.

Add manual tracking and server-side events via APIs to enrich Autocapture when you need more customization or backend signals.

Point-and-click interface to find, name, and manage events without touching your codebase.

Create Events/Segments/Properties “above” the raw dataset so you can change, combine, or delete definitions without losing underlying data.

Event definitions apply across the entire dataset, so newly defined events can be analyzed historically right away.

Add custom context to events via “Snapshot Properties” without codebase changes.

Built-in governance workflow that moves data through Define, Validate, Monitor, and Evolve to keep behavioral data trusted as products change.

Enforce a standard naming taxonomy across your dataset to maintain consistency at scale.

Central “definitions hub” that provides shared context for events, with categories and version history to keep data organized and usable.

Heap monitors event health and notifies admins when events need attention (including surfacing definitions that stop collecting data).

Heap Illuminate automatically surfaces key insights (including friction/opportunity) from behavioral data—even for behaviors you weren’t explicitly tracking.

Illuminate’s “Top Events” highlights the key events between moments in a funnel to understand what most influences conversion.

Statsig combines experimentation, feature flags (“Feature Gates”), product analytics, session replay, web analytics, and infra analytics in one platform so releases can be measured end-to-end.

Run Statsig’s experiment analysis and product analytics workflows directly in your own data warehouse for privacy/security and data governance.

Warehouse Native can run as a “modern statistical engine” on top of existing experiment/assignment data (including third-party or in-house systems), not only Statsig-assigned experiments.

High-performance, open-source SDKs across client and server environments to evaluate gates/experiments and log exposures/events.

Feature Gates (feature flags) let you toggle product behavior in real time without deploying new code.

Target Feature Gates using user attributes, device/browser context, environments, and reusable targeting building blocks (e.g., segments).

Ramp Feature Gates over time (scheduled rollouts) for safer releases and fast rollback when needed.

Attach metrics to releases and treat rollouts as lightweight A/B tests via Pulse to ensure new features don’t harm key metrics.

Configure alerts and automatic rollbacks for negative-impact detection during progressive rollouts.

Multi-stage rollout strategies across environments/infra segments with phase transitions controlled by time intervals and/or manual approvals.

Replace hard-coded values with server-defined JSON “Dynamic Configs,” including targeted configs for different user experiences.

Organize remote-config as “parameters” and remap them to static values, Feature Gates, Experiments, Dynamic Configs, or Layers—reducing code churn and hardcoded entity names.

Create reusable user sets (“Segments”) for targeting across Feature Gates and Dynamic Configs, including conditional segments and ID-list segments.

Randomize and evaluate gates/experiments on custom identifiers (e.g., companyID, device, page_url) instead of only userID/stableID.

Layers (a.k.a. Universes) group experiments into mutually exclusive “universes” and let teams share parameters at the layer level to avoid code churn across iterations.

Instantly tracks what drives user engagement, retention, and product success with “no manual event tagging needed,” reducing engineering effort to get started.

“Start yesterday” by learning from the past with retroactive product analytics (analyze historical usage after installation).

Provides “instant recommendations, powered by AI and automation” to help teams act on insights.

Uses AI-powered workflow journey insights to spot and remove workflow friction and improve process efficiency.

Creates automated, collaborative dashboards that bring teams together around quantitative and qualitative data.

Product Engagement Score (PES) rolls up adoption, stickiness, and growth into a single engagement metric for tracking product health over time.

Build and deploy in-app guidance “no engineering required,” including embedded guides, walkthroughs, and tooltips, targeted to the right users at the right time.

Create dynamic in-app experiences with conditional logic so guides adapt to each user’s choices (“personalized paths for every user journey”).

An AI guide builder creates in-app guides and steps from simple prompts (no design or coding required).

Guides automatically match your application theme when your app switches to dark mode, keeping experiences seamless and on-brand.

A customizable in-app menu that provides on-demand access to help content like guide lists, onboarding checklists, announcements, and feedback modules.

Watch video playbacks of web and mobile sessions to see experiences “through users’ eyes,” integrated with other user signals (quant/qual/visual/sentiment).

Capture and analyze unlimited session replays so exploration isn’t restricted when investigating critical user interactions.

Filter replays for relevance and use “Suggested Replays” to surface hidden behavior trends faster.

Built-in, configurable privacy controls (including out-of-the-box no-code settings) to help protect customer privacy while capturing replays.

First-party analytics that emphasizes full data ownership and privacy compliance (e.g., GDPR, HIPAA, PDPL, COPPA).

Deploy Countly your way (self-hosted, on-premises, or private cloud) to retain control of your infrastructure and data.

10+ “battle-tested SDKs” plus a robust Data Write API to capture and enrich data from apps, devices, and systems.

Data management tools to help you plan, validate, filter, and transform data before or after capture to ensure accuracy.

An AI “analytics concierge” that answers questions in natural language and can automate setups like cohorts and funnels.

AI-assisted tools to generate reports, dashboards, and recommendations to streamline analysis and decision-making.

Countly 26.01 introduces a re-architected data engine for up to 100× faster performance and scalability beyond 100 billion data points.

Built-in monitoring for self-hosted users to provide visibility into performance and operations.

Manage and modify SDK integrations post-deployment from the dashboard, with no app code changes required.

A bridge to connect Countly data directly with LLMs so AI models can work with structured, privacy-safe product data.

Real-time monitoring of crashes (mobile/desktop) and errors (web), with grouping/categorization and Jira task assignment.

A codeless query builder to drill down into granular Session, Event, Views, Feedback, and Error/Crash data; save and replay frequent queries.

Create unlimited dashboards to visualize and share KPIs using widgets from Countly features (including full-screen mode).

Behavior- and property-based user groupings that auto-regenerate and can be used to segment data and actions across Countly features.

Step-by-step funnel analysis with retroactive tracking to find drop-offs and identify friction points in customer journeys.

Galileo watches user sessions (and can deliver insights in natural language) to proactively surface what’s impacting conversion and engagement.

Galileo Highlights “watches” sessions and summarizes them, linking you directly to the moments that matter so you don’t need to watch the full replay.

Automatically detects user struggle signals (e.g., rage clicks, dead clicks, frustrating network requests, and Error States) and helps you focus on high-impact UX frustration.

AI-driven error tracking that helps you identify, prioritize, and resolve high-impact issues with severity scoring, issue quantification, and stack traces.

Reproduce every user session with “pixel-perfect” replay across web and native mobile apps.

Session replay is paired with technical context like console logs, network requests (headers/bodies), JavaScript errors/exceptions, stack traces/source maps, Redux actions/state, and performance data.

Fast-forward into an actively recording session with “Go Live” to view near-real-time playback (typically under 10 seconds latency).

Search for sessions by user information, errors, URLs, and 50+ other options; combine filters to create cohorts usable throughout LogRocket.

Capture every event, page, and action “with a single line of code,” so teams can answer analytics questions without code changes or new tagging.

Use “Definitions” to label pages/selectors/elements in common business language and retroactively apply those definitions to captured data.

Build conversion funnels to see where users drop off, then jump into session replay for full context; includes AI-powered funnel insights.

Analyze the routes users take to/from key parts of your app, where they drop off, and what issues they experience along the way.

Create cohorts based on behavior, traits, metadata, or any combination of filters; use cohorts to segment charts and compare user groups.

Track product “stickiness” with retention charts and understand the actions that correlate with conversion and repeat usage.

Use out-of-the-box dashboards/templates for common use cases or create custom dashboards tailored to your metrics and KPIs.

Generate Heatmaps (Click & Tap, Move, Scroll, Engagement, Rage-clicks) to visualize where users click/tap, move, and how far they scroll on a page.

Use Engagement heatmaps and Rage-clicks heatmaps to spot high-engagement zones and frustration patterns (repeated rapid clicks).

Build heatmaps from one URL or groups of URLs (including regex) and view aggregated data over a single screenshot.

Heatmaps include Attention Maps, and let you compare behaviors side-by-side across journeys.

Watch real user journeys to spot bugs, confusion, and drop-offs, and connect replays with heatmaps and survey responses.

Session Replay is enhanced with AI-powered summaries to help you find key insights faster.

Session Replay includes error detection (and is paired with console error tooling in Hotjar) to speed up debugging.

Session Replay includes frustration detection, and Recordings surface frustration signals (like u-turns, rage-clicks, and negative feedback).

Recordings include Frustration score, Engagement score, and Relevance score to help prioritize which sessions to watch.

Use Highlights to attach reactions/comments to recordings and group findings for sharing and follow-up.

Create session-based Funnels to visualize conversion steps and drop-offs, and jump into the connected recordings at each step.

Funnels support up to 10 steps based on sessions that viewed a page, clicked an element, or triggered an event.

Compare funnels across segments/devices/users (up to 5 comparisons) and use date filters plus conversion-over-time views.

Funnels include Sense Chat to answer questions like where users drop off and provide practical recommendations.

Create and compare custom metrics in Trends, visualize them in charts over time, and add them to Dashboards; click through to related recordings/heatmaps for context.

Matomo positions analytics data ownership as “100% yours”, with the relationship remaining between you and your visitors (no external sources looking in).

Supports privacy-first setups, including configurations that can be consent-exempt (where applicable) and integrations with consent management platforms.

Reports are based on 100% of your data (no sampling) so teams can make decisions from complete datasets.

Marketed as having no data limitations (including unlimited websites/users/segments and no limitation on how much data you store, depending on hosting).

Fully managed Matomo with quick setup, access to all features, automatic updates, and support handled by Matomo.

Matomo Cloud includes infrastructure monitoring, scalable/redundant setup, and daily backups (per Cloud product description).

Self-host Matomo on your own servers to keep data in your hands; positioned as free to download for maximum flexibility.

A WordPress plugin option aimed at making self-hosting “a whole lot easier,” while keeping ownership/control of your data.

Offers “hassle-free migration” between Cloud-hosted and On-Premise (and vice versa).

Cloud-hosted Matomo highlights the ability to request a full raw data dump at any time.

Exports raw, unaggregated Matomo Cloud data and supports scheduled exports to common data warehouses (e.g., BigQuery, Snowflake, Redshift).

Import your historical Google Analytics reports/properties into Matomo to keep historical analytics data available inside Matomo.

Built-in tag management to “manage and unify all your tracking and marketing tags,” using Tags, Triggers, and Variables.

Matomo provides consent tooling guidance, including tracking without cookies and consent-related configurations (depending on your legal basis/jurisdiction).

Real-time reporting to watch visits as they happen and see what visitors are doing on the site in the moment.