posthog.com

Goal‑oriented AI experts embedded in Amplitude that continuously analyze your analytics, session replay, experimentation, and guides/surveys data to surface insights and recommend actions.

Agents can carry out actions you approve (e.g., running an experiment or sending targeted guidance), and won’t make customer-facing changes without your approval.

Create and schedule multiple agents and push takeaways/updates to email, Slack, and other destinations via recurring notifications.

Amplitude MCP (Model Context Protocol) server brings governed behavioral context into tools like Claude and Cursor so teams can ask questions in plain language and get answers based on live Amplitude data.

Aggregates customer feedback from sources like support tickets, surveys, app reviews, social media, and call scripts into a unified dashboard.

Links “what users say” to “what they do” by connecting insights to cohorts, Session Replay, experiments, and even creating guides/surveys from insights; includes natural-language analysis and drafting (e.g., PRDs).

Measures how AI assistants talk about your brand (visibility score, mentions, rank, citations) and compares competitors/share-of-voice with weekly updates.

Connects AI visibility performance to outcomes like AI-sourced traffic, conversion paths, and revenue metrics (closing the loop from AI mention to purchase).

Automates baseline product reporting (dashboards/charts) with “just one line of code,” surfacing real-time user metrics with minimal customization.

Out-of-the-box Product Analytics supports “Views” so different teams can customize settings like active events, retention intervals, onboarding funnels, and tracked features.

Provides expert-built templates and charts to speed time-to-insight and flatten the learning curve for teams.

Ask questions about your product without needing to know your taxonomy; use AI to generate and customize charts with natural language.

Drive engagement with tailored in-product communication based on behavioral insights (e.g., guiding onboarding/activation and prompting feature discovery).

Set metrics and track goals; save and standardize metrics across charts, cohorts, and tables.

Analyze and compare metrics across multi-dimensional user segments to pinpoint growth drivers (including campaign-related behaviors).

Analyze events, cohorts, and user profiles to visualize trends and compositions; advanced capabilities include formulas, comparing current vs past, and creating custom events/properties for deeper analysis.

Measure conversion through a sequence of events with configurable conversion criteria (e.g., conversion window) and advanced options like “Optimized Re-entry” for capturing subsequent funnel entries.

Identify the most frequent paths users take to or from any event (e.g., via Sankey-style user flows), with options like expanding steps and breaking down journeys by segments.

Measure engagement over time by defining a “Retention Behavior” (do an event, then come back to do another), with multiple measurement types (e.g., retention rate, uniques, property sum).

Visually define cohorts based on user properties and/or behavioral conditions, then use cohorts for filtering/breakdowns, sharing, and downstream targeting workflows.

Export cohorts to external destinations (e.g., Braze, Facebook Ads, Google Ads) with one-time, scheduled, or real-time sync cadences to operationalize analytics audiences.

Collect reports (Insights, Funnels, Flows, Retention) plus text/media into a single view for tracking KPIs at a glance, with sharing and organization features like favorites and pinned boards.

Subscribe to a Board to receive scheduled updates on key metrics via email or Slack, including sending updates to people outside your immediate team.

Use natural language prompts from Boards to generate Insights, Funnels, Retention, and Flows reports; iterate on prompts and save results back to the Board.

Watch playbacks of user experiences to investigate drop-offs discovered in analytics, with entry points from Mixpanel reports and user profiles.

Session Replay data is masked by default, with controls for what to unmask so teams can collect only what they need.

Filter large volumes of replays into a focused set using Mixpanel events, properties, and cohorts to quickly narrow to the sessions that matter.

Generate heatmaps powered by Session Replay data, including click maps (element-level) and traditional coordinate heatmaps for understanding engagement and friction.

Automatically compute “Session Start” and “Session End” from events with configurable session definitions (timeout-, event-, or property-based) that can be changed historically.

Sync data from warehouses (e.g., Snowflake, BigQuery, Databricks, Redshift, Postgres) into Mixpanel via a codeless setup to analyze business/back-end data alongside product usage.

A single platform that combines Experimentation, Feature Flags/Feature Management, Product Analytics, Session Replay, Web Analytics, Infra Analytics, and AI Evals so teams can “ship, measure, and learn” in one place.

Run Statsig’s experimentation and analytics workflows directly on top of your data warehouse (no ETL), while using Statsig’s stats engine for analysis.

Warehouse Native supports bringing your existing assignment/flagging system (or using Statsig’s), while still running analysis in your warehouse.

“Feature Gates” (feature flags) toggle product behavior in real time without deploying new code, with targeting based on user and environment attributes.

Sophisticated targeting, environment controls, and customizable release workflows for feature rollouts.

Time-based, multi-phase rollout schedules at the rule level that execute automatically.

Configure alerts and automate rollbacks when releases show negative impact.

Monitor exposure events and health checks in real time to assess performance and debug quickly during rollouts.

Override gates for specific users (bypass lists) to support testing, dogfooding, and controlled access.

Chain Feature Gates in dependent relationships so a top-level “kill switch” can control multiple sub-flags.

Run lightweight A/B tests for feature launches by treating gated users as control vs. exposed users as treatment.

Replace hard-coded values with server-defined JSON that can be updated near real time and targeted to different user segments.

Organize remote-configurable “parameters” (instead of gates/experiments/configs), and remap them to any Statsig entity without hardcoding entity names in code.

Infrastructure-aware, multi-stage rollout strategies across environments/regions with time intervals and/or manual approvals, plus instant rollback control.

Support for advanced experimentation techniques including sequential and switchback tests, multi-armed bandits, and contextual bandits.

A single snippet automatically captures the entire digital experience (e.g., clicks, pageviews, formfills) across platforms—no tracking plan required.

Define what to measure later and still analyze historical behavior (“lookback analysis”) because Heap records from the moment you install it.

Point-and-click UI to find, name, and manage events (virtual events) without touching your codebase, with definitions that apply retroactively across the dataset.

A live data feed to inspect incoming data and validate/QA tracking and definitions as you interact with your site/app.

Governance workflow that moves events through a lifecycle—Define, Validate, Monitor, and Evolve—to keep a clean, trusted behavioral dataset at scale.

Data Dictionary plus naming conventions, categories, and version history to keep definitions consistent and understandable across the organization.

Automated governance monitoring and workflows, including admin notifications and surfacing definitions that haven’t collected data in 72 hours for repair/archival.

Native Session Replay is fully integrated with Heap analytics, letting teams move between quantitative analysis and replay context in one platform.

Session Replay includes “intelligent matching & search,” automatically cueing replays to the exact event and letting you search client/server-side events, paths, errors, and clicked text.

Highly customizable privacy settings for replays/heatmaps (e.g., mask all text, mask all form fields & sensitive content, and mask additional elements via CSS selectors).

Heatmaps provide “endless visual insights” with Clickmaps, Scroll Depth, and Attention Maps—no tagging or event creation required.

Automatically detects Rage Clicks to help pinpoint hidden user frustration caused by bugs, slow response times, or UX confusion.

Heap Illuminate is the platform’s data science layer that proactively surfaces key insights about friction and opportunity—including behaviors you weren’t following.

Illuminate generates a “Top Events” table to show what most influences conversion between two moments in a funnel.

Automatically suggests the most important segments (“Group Suggestions”) to examine when you run graphs/funnels to understand conversion and engagement differences.

A conversational, agentic way to use Pendo—product teams can ask it to analyze feedback, build reports, or find users for research to automate time-consuming workflows.

Connect Pendo data to LLMs via the open Model Context Protocol (MCP), including tools like Claude or Agentforce.

AI-powered recommendations to uncover trends across user behavior and reveal growth opportunities ("cut through dashboard clutter").

Product analytics that "instantly tracks" what drives engagement and retention with “no manual event tagging needed.”

“Start yesterday” with retroactive product analytics to learn from past behavior.

“Automated, collaborative dashboards” that bring quantitative and qualitative data together to rally teams around shared metrics.

Visualize how users move through your product to spot navigational patterns, identify drop-off, and see cross-app user journeys across your portfolio.

Identify high/low value features, replicate power-user behavior, track usage trends, and spotlight (or sunset) features to drive engagement.

Build precise user segments using behavioral, demographic, or customer attributes, and use “Nested Segments” to combine/exclude segments (like a “do not call” list).

No-code in-app guidance to “deploy quickly” and drive adoption with embedded guides, walkthroughs, and tooltips—plus actionable guide metrics.

“Guide Branching” uses conditional logic to create personalized paths that adapt to users’ choices for tailored onboarding and training.

“AI guide creation” auto-generates guides and steps from simple prompts—no design or coding required.

Guides automatically switch when your app switches to dark mode (“Guides Dark Mode”).

Create “cross-app consistency” with cohesive guide experiences across web and mobile apps.

Orchestrate combines in-app guides, email, and data so teams can reach users in and out of the product with connected communications.

Create simple on/off flags or more complex multivariate flags to control feature variations at runtime.

Use LaunchDarkly’s context-based model to target not just users, but devices, environments, companies, locations, and more with rules as simple or complex as needed.

Group targets into “segments” and create rules for cohorts; import large segments from other product analytics providers and keep them in sync.

Gradually release changes over time using percentage-based rollouts or expanding segments to limit blast radius and gather insights.

Preview targeting-rule changes by testing which variation a context would receive and how rollout percentages would shift before you go live.

Design ordered, multi-environment “release pipelines” so features progress from local development to production with consistent standards.

Automate multi-step releases with capabilities like scheduling releases in advance and automatically requiring flag change approvals (and packaging these into repeatable workflows).

Initiate remote flag changes using a unique webhook URL, enabling third-party tools (like APM systems) to turn flags on/off or adjust targeting.

Track flag usage, set TTLs, and auto-clean stale flags to reduce technical debt and keep your flag inventory healthy.

Declare prerequisite and dependent flags so interdependent features only activate in the right situations and stay consistent across platforms.

Locate where flags are used across repositories/branches, search references by alias, and see when a flag was last referenced to avoid blind spots.

Compare flag configuration settings between environments and copy/clone rules to replicate setups without manual errors.

Monitor real-time flag activity to quickly identify and resolve issues as changes happen.

Use a customizable dashboard to organize and filter flags by tags and other attributes for governance and visibility.

LaunchDarkly automatically creates an audit log of every software change made in the platform to support compliance and reporting.

Tagless autocapture that records every interaction in real time so teams don’t miss behavioral data points.

Fullcapture adjusts to changes in your site or app automatically, reducing the need to retag or update events as experiences evolve.

Uses “one global script” with server-side data processing to avoid performance hiccups while capturing behavioral data.

“Private by Default” minimizes risk by capturing no text unless explicitly allowlisted as safe to capture.

Masks elements into wireframes during Session Replay so you can see interactions without capturing sensitive content on Fullstory’s servers.

Element-level privacy controls with Exclude, Mask, and Unmask rules for managing what gets captured and replayed.

Watch sessions to understand the “why” behind user behavior and diagnose experience issues with full interaction context.

Session Replay includes behaviors “behind” metrics, including tabbed browsing context.

One-click, AI-generated session summaries provide quick context around a user’s behavior so teams can move faster to action.

StoryAI Summaries condense multiple sessions into a readable narrative to spot patterns, blockers, and intent across replays.

StoryAI recommends which sessions are most worth reviewing first to prioritize the moments that matter.

Automatically detects frustration signals (e.g., rage clicks, dead clicks, error loops) and rolls them into AI summaries for context.

Ask StoryAI analyzes a session’s event list to identify key moments, frustration signals, and user sentiment—without watching the full replay.

Provides clickable “Key Moments” timestamps so you can jump directly to the most important parts of a replay.

Interprets the user’s experience with sentiment (Positive/Mixed/Negative) plus counts/lists of success and frustration signals.

Hotjar is packaged into three products: **Observe** (Recordings, Heatmaps), **Ask** (Surveys), and **Engage** (User Interviews).

Visualize where users **move, click, and scroll** to identify what’s working—and what’s not—on each page.

Heatmaps include **Attention Maps** to better understand what grabs users’ attention.

Build heatmaps for a single URL or groups of URLs using match types (including **URL matches regex**), and view aggregated data over a single screenshot when multiple pages match.

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

Session Replay includes a **one-click summary** that highlights key moments so you can spot issues faster.

Session Replay is enhanced with **AI summaries**, plus **error detection** and **frustration detection**.

Includes a **privacy-first script built for site speed**, and privacy controls like automatic suppression/anonymization to help protect personal data.

Visualize conversion steps and drop-offs so you can see where users leave and investigate why.

Use **Sense Chat** to ask questions like “Where are my users dropping off?” and get practical recommendations.

Create funnels with up to **10 steps** based on sessions that viewed a page, clicked an element, or triggered an event, with date filters and session filters.

Add up to **5 comparisons** (e.g., devices, new vs returning, traffic channels, A/B variants, User Attributes, Events).

Switch views including **Funnel steps**, **Conversion over time**, and **Metric only** (with conversion rate and average time to convert).

Jump from funnels into the **specific recordings** tied to conversions and drop-offs at each step (including for comparisons).

Create and compare **custom metrics** in charts, track them over time, and add them to your Dashboard.

Galileo “watches every session,” reads customer feedback, and tracks product changes to proactively surface what’s impacting users before it impacts your business.

Ask product questions on demand and get answers instantly—without extra work or chart building.

AI-powered session summaries (“Highlights”) that watch sessions and surface the moments that matter so teams don’t have to manually review replays end-to-end.

Get Galileo insights delivered in existing workflows, including Slack, Microsoft Teams, and via webhooks.

100% accurate reproduction and playback of user sessions across web and native mobile apps, exactly as the user saw it.

Jump from a replay to a live view for currently-active users to support real-time debugging and support interactions.

Session replay includes DOM playback plus console and network logs, errors, and performance data for in-depth debugging.

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

Capture every event, page, and action with “no tagging needed,” so teams can answer analytics questions without code changes.

Define events in common language so teams can reuse consistent event naming and meaning across charts and dashboards.

Build conversion funnels to find drop-off, then use AI-powered funnel insights and session replay to understand what happened before users abandon.

Analyze the routes users take through 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, and apply cohorts across charts.

Track retention and identify the actions that make users more likely to convert and become repeat users.

Use out-of-the-box dashboards for common use cases or build custom dashboards tailored to your team’s KPIs.

Collect first-party user events from any platform using Segment’s Analytics API (web, mobile, server, and more).

Send customer data to 700+ downstream tools with “just a few clicks,” without building each integration yourself.

Activate new tools “in minutes, not weeks,” without needing to push code or wait for App Store approvals (even for tools without their own mobile SDKs).

Sync data from your warehouse to your applications and destination tools to activate warehouse data across your stack.

Build custom integrations, transformations, and data-masking logic with lightweight JavaScript on Segment’s extensible platform.

Generate complete Functions from natural language prompts and get AI recommendations to optimize existing Functions.

View a live stream of event data flowing from your app to Segment to speed up implementation and troubleshooting.

Replay existing data to try new tools quickly and reduce vendor lock-in.

Create a shared, living Tracking Plan to align teams around a single data dictionary instead of spreadsheets.

Automatically audit your instrumentation with real-time validation to catch incorrect properties or data types at scale.

Get in-app reporting and daily email digests so teams can quickly take action on invalid events.

Reduce implementation errors with Typewriter and an extensible API for Protocols.

Enforce data standards by blocking non-conforming events and reviewing discrepancies in a separate quarantine source.

Stitch together customer journeys across sessions and devices with deterministic identity resolution to build unified profiles.

Automatically match and merge data from streaming sources (including the warehouse) into unified, always up-to-date customer profiles.