Includes automated anomaly detection on metric charts, highlighting unexpected spikes or drops.
To quickly identify technical issues or shifts in user behavior, the platform features native anomaly detection. It automatically establishes expected confidence intervals based on historical trend data. If a metric or event volume deviates significantly from this baseline, such as a sudden drop in checkouts or a spike in error events, the system highlights the anomaly on the Insights chart. While effective for spotting irregularities during daily monitoring, it primarily serves as a visual aid within reports rather than a standalone alerting infrastructure.
Allows building of complex user cohorts for direct export to external marketing platforms.
The cohort builder is designed for precise targeting and analysis, allowing users to define segments based on historical events, timeframes, and user properties. Dynamic cohorts can filter any report within the platform. Through extensive integration, these segments can be automatically synced to marketing automation tools like Braze or HubSpot. This capability enables personalized messaging based on recent product usage, enhancing marketing effectiveness. The platform's integration ecosystem supports direct transitions from analysis to marketing execution, making it a valuable tool for targeted outreach.
The native Lexicon feature acts as a central data dictionary, allowing administrators to manage, govern, and validate the event tracking schema.
To solve the common issue of chaotic and duplicated tracking data, the platform includes a powerful data governance tool called Lexicon. This acts as a centralized data dictionary where administrators can define descriptions for every event and property, establishing a clear single source of truth for the organization. Lexicon allows data teams to easily merge duplicate events, hide obsolete properties from the main UI, and flag unexpected data payloads sent by developers. This automated schema management ensures that analysts and business users can trust the data they are querying, drastically reducing the friction often associated with open-ended custom event tracking.
This testing capability tightly integrates with external A/B testing platforms, providing deep behavioral analysis of experiment results natively within the dashboard.
While the platform historically offered a native A/B testing feature, its current strategic approach relies on deep, bi-directional integrations with dedicated experimentation tools like Optimizely, VWO, and LaunchDarkly. Instead of building a basic internal testing module, the platform automatically ingests experiment assignment data from these specialist tools as event properties. This allows analysts to evaluate the outcome of an A/B test using the platform's immensely powerful behavioral funnels, retention charts, and cohort analysis. This provides a far deeper understanding of how an experiment impacted long-term user behavior, rather than simply measuring a basic click-through conversion rate.
Advanced retention reports allow teams to track user loyalty and churn by grouping users based on highly specific behavioral triggers.
The platform provides deep, highly customizable cohort and retention tracking, which is critical for subscription and SaaS businesses. Analysts can move beyond standard acquisition cohorts to define specific behavioral groups, such as "users who watched a video 3 times in their first week." The Retention report then tracks exactly how these specific cohorts return to perform a target action over subsequent days, weeks, or months. The interface supports both N-day retention (tracking exact daily return rates) and unbounded retention (tracking if a user ever returns after a specific point). This depth of analysis is essential for identifying the precise product behaviors that drive long-term user loyalty.
Users can build interactive, highly shareable "Boards" that consolidate multiple reports, metrics, and text blocks into a single view.
The dashboard functionality, known as "Boards," provides a highly collaborative environment for product teams to monitor core KPIs. Users can easily pin any saved report—whether it is a complex funnel, a retention table, or a simple metric trend—directly to a Board. The interface is highly interactive; viewers can apply global filters (like date ranges or specific user cohorts) to an entire Board instantly without altering the underlying reports. Additionally, analysts can add rich text blocks and markdown to provide context or commentary alongside the data. It functions less like a static executive summary and more like an active, exploratory workspace for data-driven product squads.
The data model fundamentally replaces rigid session-based tracking with an open, event-driven schema tailored to unique product workflows.
The platform is built entirely around a flexible, user-centric data model rather than the rigid, session-centric model used by traditional web analytics. Every interaction is tracked as an independent event tied to a specific user profile, accompanied by rich metadata properties. This custom model allows organizations to define their own specific KPIs and track complex product logic that generic pageviews cannot capture. The platform also supports tracking group-level analytics (B2B account-level tracking), allowing SaaS companies to analyze behavior by "Company" or "Workspace" rather than just individual users. However, this immense structural freedom requires rigorous internal data governance.
Operates on a purely event-driven schema with limitless custom actions and rich metadata.
Utilizing a highly flexible, flat event model, developers can define any specific user action as a custom event, such as Message Sent or Plan Upgraded. An unlimited number of contextual properties can be attached to both the event and the user profile. A major strength is the ability to send complex data types, such as nested JSON objects and arrays, as event properties, allowing for deeply nuanced tracking of in-app behavior. This structural freedom requires organizations to implement strict tracking plans to prevent the data dictionary from becoming chaotic.
Standard enterprise contracts allow for five years of data retention, providing robust historical depth for long-term product analysis.
Because product lifecycles often span years, the platform offers very generous data retention policies. By default, standard enterprise contracts retain granular, user-level event data for up to five years, a significantly longer period than the 14-month limits frequently imposed by marketing-focused web analytics tools. This extended retention allows analysts to perform deep historical queries, run multi-year retention analyses, and evaluate the long-term impact of major product updates. Organizations with strict data minimization requirements can configure the system or request manual purges, but the platform fundamentally supports extensive historical data availability for deep behavioral modeling.
The e-commerce framework handles e-commerce measurement through its flexible custom event tracking rather than providing rigid, pre-built retail templates.
Because the platform is a versatile product analytics engine, it does not offer a strict, out-of-the-box e-commerce module like traditional web analytics tools. Instead, retail and e-commerce companies must explicitly define custom events like Item Added, Checkout Started, and Purchase Completed, attaching revenue values as specific event properties. Once this custom schema is instrumented, the platform provides unparalleled power to analyze the complex behavioral pathways that lead to a purchase. It is brilliant for answering complex merchandising questions based on deep user engagement, but it requires a significantly higher initial configuration effort compared to plug-and-play retail analytics solutions.
The platform offers an exceptionally powerful funnel engine, allowing analysts to track complex, multi-step conversions and measure time-to-convert natively.
Funnel analysis is a foundational strength of this platform. Analysts can construct intricate user journeys using any combination of custom events, with precise control over the conversion window (from minutes to months). It excels in flexibility, allowing teams to analyze exact-order funnels, any-order funnels, and even funnels that measure the conversion rate between multiple sessions. A standout feature is the "Time to Convert" distribution chart, which clearly visualizes the velocity of the user journey. Furthermore, analysts can seamlessly segment the funnel by any event property or use the "Find Insight" feature, which automatically highlights the specific user properties or behaviors that correlate highest with successful conversion.
Includes compliance tools with a dedicated API for automated data deletion requests.
As an enterprise-grade solution, it supports global privacy laws like GDPR and CCPA, operating as a data processor to ensure business data ownership. A dedicated Data Deletion API automates 'Right to be Forgotten' requests, securely wiping user profiles. EU data residency is supported, allowing European clients to store data exclusively on European servers. However, legal compliance relies on businesses implementing a valid Consent Management Platform (CMP) before using the tracking SDK.
Merges anonymous device history with authenticated user profiles through ID management.
Cross-device tracking is managed through an ID merging system. Users receive a distinct_id when interacting anonymously, which merges with a permanent ID upon authentication. This process stitches pre-login history with the authenticated profile, maintaining user journey continuity across devices. It ensures accurate unique user counts and long-term behavioral tracking, even when switching between anonymous and logged-in states.
The app measurement setup is a premier tool for mobile measurement, perfectly tailored to track complex, event-driven behaviors across iOS and Android applications.
The platform is widely recognized as an industry leader in mobile product analytics. Because mobile apps are fundamentally event-driven rather than page-driven, the platform's flexible tracking schema is a perfect fit for mobile development. It natively tracks essential app metrics—such as installs, app opens, crashes, and push notification interactions—while allowing deep instrumentation of custom in-app workflows. It seamlessly handles the complexities of mobile environments, including offline usage tracking and cross-device identity merging. For dedicated mobile product teams, it is often preferred over basic web analytics platforms due to its superior focus on individual user engagement and retention modeling.
Provides open-source SDKs for mobile, web, and server-side environments, ensuring reliable event streaming.
To capture precise user behavior, the platform offers a detailed library of SDKs for iOS, Android, Flutter, React Native, and various backend languages like Python and Node.js. These SDKs are specifically designed for product analytics, managing offline batching, retry logic, and session management directly on the device. They automatically track core app lifecycle events and provide methods for developers to trigger custom events with extensive properties. This infrastructure is necessary for mobile-first products, ensuring accurate behavioral data capture without relying on fragile third-party tag managers.
The Flows report visually maps out complex user navigation, revealing exactly what users do immediately before or after a target event.
To analyze organic user journeys, the platform features a dynamic visualization tool called "Flows." This tree-graph report tracks the sequential paths users take, mapping out the specific custom events triggered leading up to a goal or immediately following an entry point. A significant advantage is the ability to easily exclude noisy, irrelevant events from the visualization, allowing analysts to focus cleanly on core product workflows rather than cluttered background pings. It effectively highlights common drop-off points, unexpected behavioral loops, and alternative paths users invent. It is an indispensable tool for UX researchers and product managers aiming to streamline application navigation.
The Signal report identifies which specific user behaviors and actions correlate most strongly with long-term retention or conversion.
Rather than offering a "black box" machine learning prediction of individual user churn, the platform provides a highly actionable predictive tool called Signal. This feature scans historical data to automatically identify the specific events and properties that have the highest statistical correlation with a defined success metric (like long-term retention or completing a purchase). For example, it might reveal that users who "add 3 friends within 2 days" are 80% more likely to retain. This provides product teams with clear, actionable insights into exactly which features they should optimize to drive growth, though it is not a replacement for dedicated data science models predicting exact lifetime value.
Automatically routes raw, unsampled event streams to data warehouses like Snowflake or BigQuery.
Features a Data Pipelines add-on for centralizing behavioral data. Enables automated exports of JSON data to cloud data warehouses or storage buckets. This allows merging in-app data with financial records or training machine learning models. Unlike platforms with restricted access, this pipeline is reliable for enterprise scale.
Centrally manages secure access through SAML 2.0 Single Sign-On.
Complying with enterprise IT security standards, SAML 2.0 Single Sign-On (SSO) is fully supported. This allows integration with identity providers like Okta, Azure AD, Google Workspace, or OneLogin. SSO ensures access is governed by corporate policies, enabling multi-factor authentication and automated user management. Centralized management mitigates security risks from shared credentials and unmanaged accounts.
