Anomaly detection automatically highlights statistical outliers in event trends and correlates them directly with qualitative session recordings.
The platform features native anomaly detection that automatically analyzes historical event volumes to establish expected trend baselines. When a metric deviates significantly—such as a sudden collapse in successful checkouts or a massive spike in API errors—the system visually highlights the anomaly on the chart. What sets this apart from competitors is the immediate next step: when an anomaly is flagged, analysts can click the outlier point to instantly watch the session recordings of the users affected during that specific timeframe. This drastically reduces the time it takes to diagnose whether a data spike is a tracking error, a backend bug, or genuine viral traffic.
Forms dynamic behavioral cohorts for instant targeting in A/B tests or feature flags.
Analysts can create granular 'Cohorts' based on user properties, event frequencies, and time constraints. The platform's native Feature Flags and Experimentation allow these cohorts to target specific app experiences instantly. For example, a cohort of users experiencing errors can trigger a feature flag offering a discount. This integration provides a direct workflow from analysis to product activation, enhancing user experience personalization. The focus on immediate application makes it ideal for organizations seeking to quickly implement insights into product strategies.
The Data Management feature provides a centralized dictionary to verify, annotate, and govern custom events and properties across the organization.
To help teams maintain a clean and reliable event schema, the platform includes a native Data Management module. This acts as a centralized data dictionary where administrators can add descriptions to specific events, verify that they are firing correctly, and tag them to specific product owners. If the tracking taxonomy becomes bloated, analysts can use this interface to hide obsolete events or merge duplicate tracking codes without having to deploy new code. While it provides solid foundational governance, it lacks the highly aggressive, automated event-quarantine features found in top-tier enterprise platforms that automatically block malformed data payloads before they hit the database.
The experimentation framework uniquely integrates A/B testing and multivariate experimentation directly into the analytics platform alongside feature flags and session replay.
A massive differentiator for this platform is its native inclusion of a full-fledged A/B testing and experimentation engine. Teams can launch A/B tests or multivariate experiments directly from the UI, utilizing the platform's native Feature Flags to split traffic. Because the testing engine shares the exact same database as the analytics engine, analysts can evaluate experiment results using complex, long-term behavioral metrics (like 30-day retention or downstream feature usage) rather than just basic click-through rates. This eliminates the severe data discrepancies that typically occur when trying to sync a standalone third-party testing tool with a separate analytics platform.
Enables cohort and retention analysis by grouping users through complex behavioral triggers over custom timeframes.
Retention tracking is deeply integrated, allowing analysis of long-term user loyalty. Analysts can define behavioral cohorts and track their return over days, weeks, or months. The interface supports both standard and bracketed retention, important for products with non-daily usage. Defined cohorts can be saved dynamically and used for reporting or as targeting segments for native Feature Flags and A/B testing modules. This integration enhances strategic product management.
Dashboards are highly customizable, collaborative workspaces that support SQL queries, Markdown text, and embedded session recordings.
The dashboard environment is designed to be a highly interactive workspace rather than a static reporting view. Analysts can pin various insights—including complex funnels, retention charts, or direct SQL queries—onto customized boards tailored for specific product squads. A major differentiator is the ability to interleave Markdown text blocks to explain the data context and directly embed relevant session recordings next to the charts. If a chart shows a sudden spike in errors, team members can watch the associated screen recordings immediately on the dashboard without switching contexts. This deep integration between quantitative metrics and qualitative visual evidence makes the dashboards exceptionally powerful for product diagnostics.
Custom data modeling operates entirely on an event-driven schema, allowing for unlimited custom event definitions rather than relying on rigid session tracking.
The platform discards the rigid, session-centric model of legacy web analytics in favor of a profoundly flexible event-driven architecture. Every action a user takes is logged as an independent event associated with their profile. This model allows data architects to define virtually any custom interaction and attach extensive JSON metadata properties to it. Furthermore, the platform supports "Group Analytics," which allows B2B companies to track behavior at an account or company level rather than just at an individual user level. This flexibility is incredibly powerful for complex SaaS products but requires organizations to maintain strict data governance to prevent the taxonomy from becoming chaotic.
Combines automatic frontend capture with precise custom instrumentation in a flexible schema.
Utilizing a hybrid tracking model, the JavaScript snippet automatically captures all frontend interactions via 'autocapture', instantly populating the dashboard with data. Simultaneously, developers can define highly specific custom events and properties via code for critical backend actions or complex state changes. This provides immediate visibility of an autocapture tool combined with the rigorous data quality of a deliberately instrumented custom schema. However, relying too heavily on autocapture without naming conventions can lead to cluttered, difficult-to-read reports.
Offers flexible data retention with cloud plans and limitless storage for self-hosted instances.
Data retention limits depend heavily on the chosen deployment method. For the managed Cloud version, standard retention for granular event data ranges from 1 year to 7 years depending on the specific premium plan, ensuring deep historical analysis for long-term product trends. However, organizations that choose to self-host the open-source version on their own infrastructure have absolute control over their data retention; they can retain unsampled, user-level data indefinitely, limited only by their own server capacity. The platform also includes administrative tools to automatically purge old data if an organization needs to enforce strict data minimization rules for legal compliance.
While it lacks predefined retail templates, e-commerce workflows can be deeply analyzed using its flexible custom event schema and conversion funnels.
Because the platform is engineered as a general-purpose product analytics suite, it does not provide native, plug-and-play e-commerce dashboards (like built-in "Revenue" or "Cart Abandonment" templates). Instead, online retailers must instrument the purchase funnel manually using custom events (e.g., Add to Cart, Purchase) and pass transaction amounts as event properties. Once configured, the platform excels at analyzing the deeply complex behavioral paths that lead to a sale, combining revenue data with session recordings to see exactly why a user abandoned a cart. However, teams looking for instant, out-of-the-box retail metrics will find the initial setup more demanding than dedicated e-commerce analytics tools.
Offers complex funnel creation with features like strict step-ordering and session links.
Complex funnel creation is supported with features like strict step-ordering, time-to-convert metrics, and direct session recording links. Analysts can define funnels using pageviews, custom events, or frontend clicks. The tool accommodates exact-order and unordered funnels, with specified conversion time windows. A competitive advantage is its integration with Session Replay, allowing instant access to recordings of users who dropped off. This provides qualitative explanations for quantitative drop-offs, a rare capability in analytics suites.
Offers EU data residency and open-source deployment for strict privacy compliance.
The platform ensures GDPR and CCPA compliance with EU data residency for cloud customers, keeping European data on European servers. Its open-source nature allows organizations with strict privacy needs to self-host, ensuring no third-party vendor accesses their data. Native tools for IP anonymization and user data deletion requests are included. Legal compliance depends on businesses correctly implementing a Consent Management Platform (CMP) on their frontend.
Allows developers to map anonymous session IDs to authenticated user IDs.
Identity resolution is achieved through a developer-controlled process using identify and alias API calls. Anonymous users receive a distinct ID, which merges with a backend ID upon authentication. This process stitches pre-login behavior with the authenticated profile, ensuring a continuous historical journey across devices. While accurate for SaaS and subscription businesses, it relies on user logins without third-party data graphs.
Offers strong mobile app analytics support via dedicated SDKs.
Excelling in mobile app analytics, this approach unifies capabilities within mobile SDKs for iOS, Android, React Native, and Flutter. Beyond event tracking, SDKs enable feature flag deployment and session replay recording. This offers a detailed view of the mobile experience. The flexible event-driven schema accommodates complex mobile applications, presenting a strong alternative to legacy trackers. This detailed support ensures that mobile app analytics are both detailed and actionable, providing valuable insights into user behavior and app performance.
Open-source SDKs for web, mobile, and backend, integrated with feature flags and session recording.
An engineering-focused approach provides a detailed library of open-source SDKs for JavaScript, iOS, Android, React Native, Python, Node.js, and more. These libraries are multi-functional, handling event tracking, session recording, and feature flag evaluation in a single implementation. This unified approach reduces payload size and simplifies development workflows. Additionally, the open-source nature allows developers to inspect the SDK code, ensuring transparency in data collection and transmission processes. The integration of feature flags and session recording within the SDKs enhances their utility for developers seeking detailed tracking solutions.
The Paths tool visualizes organic user navigation, allowing teams to map journeys based on pageviews, custom events, or screen names.
To understand how users naturally navigate an application, the platform provides a dynamic User Paths visualization. This tree-graph maps out the exact sequence of events or pageviews users trigger before or after a specific milestone. A key strength is the ability to heavily customize the nodes; analysts can choose to only map specific custom events, ignore noisy pageviews, or group URLs by regex rules to clean up the visual flow. This makes it highly effective for analyzing complex Single Page Applications (SPAs). While incredibly useful for spotting navigation loops, highly complex enterprise apps with thousands of events can quickly render the visualization overwhelming without strict filtering.
The Data Pipelines feature allows seamless, automated export of raw, unsampled event data to external warehouses like BigQuery or S3.
For organizations needing to centralize their data architecture, the platform features native Data Pipelines (formerly known as apps or plugins). These pipelines allow teams to configure automated, continuous streams of raw, hit-level JSON data directly into major data warehouses such as Google BigQuery, Snowflake, Amazon S3, or Redshift. This enables data science teams to easily combine product behavioral data with external financial or CRM datasets. Because the platform does not artificially restrict raw data access or mandate aggressive data sampling, this feature provides total data portability and ownership, a critical requirement for enterprise data engineering teams.
Supports secure access via native SAML 2.0 Single Sign-On integration.
For enterprise deployments, SAML 2.0 Single Sign-On (SSO) integration is natively featured. IT administrators can connect the environment to identity providers like Okta, Azure AD, or Google Workspace. SSO enables multi-factor authentication, user provisioning, and quick access revocation. This centralized control maintains data security and compliance, eliminating vulnerabilities from standalone passwords.
