The platform provides robust, open-source SDKs for a wide array of mobile, web, and server-side environments, ensuring reliable event streaming.
To capture precise user behavior natively, the platform offers an extensive library of SDKs covering iOS, Android, Flutter, React Native, and numerous backend languages (Python, Node.js, Ruby). These SDKs are specifically engineered for the demands of product analytics; they handle automatic offline batching, retry logic, and seamless session management natively on the device. They automatically track core app lifecycle events while exposing straightforward methods for developers to trigger custom events with extensive properties. This robust infrastructure is a critical requirement for mobile-first products, ensuring that behavioral data is captured accurately without relying on fragile, third-party tag managers.
The platform operates on a purely event-driven schema, allowing teams to define limitless custom actions alongside rich, nested metadata properties.
Unlike traditional analytics that shoehorn behavior into rigid pageviews or Category/Action/Label structures, this platform utilizes a highly flexible, flat event model. Developers can define any specific user action as a custom event (e.g., Message Sent, Plan Upgraded) and attach an unlimited number of contextual properties 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 is immensely powerful, though it requires organizations to implement strict tracking plans to prevent the data dictionary from becoming chaotic.
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 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.
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.
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.
The platform includes automated anomaly detection on core metric charts, visually highlighting unexpected spikes or drops in event volume.
To help teams quickly identify technical issues or sudden shifts in user behavior, the platform features native anomaly detection. This system automatically establishes expected confidence intervals based on historical trend data. If a specific metric or event volume deviates significantly from this expected baseline—such as a sudden drop in successful checkouts or a massive spike in error events—the system visually highlights the anomaly directly on the Insights chart. While it is highly effective for spotting immediate irregularities during daily monitoring, it operates primarily as a visual aid within the reports rather than a fully standalone, multi-channel alerting infrastructure.
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 employs a robust identity management system that seamlessly merges anonymous device history with authenticated user profiles.
Accurate cross-device tracking is handled natively through a sophisticated ID merging system. When a user interacts with a digital property anonymously, they are assigned a unique distinct_id. Once the user authenticates (logs in or registers), the developer triggers an identify call with the user's permanent backend ID. The platform automatically and retroactively stitches the anonymous pre-login history together with the authenticated profile. This ensures that user journeys remain intact even as individuals switch from an anonymous mobile browser to a logged-in desktop application, guaranteeing highly accurate unique user counts and seamless long-term behavioral tracking.
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.
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.
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.
The platform provides comprehensive compliance tools, including a dedicated API to automatically process user data deletion requests.
As an enterprise-grade solution, the platform provides robust infrastructural support for global privacy laws like GDPR and CCPA. It operates strictly as a data processor, ensuring the business retains complete ownership of its data. It includes a dedicated Data Deletion API, enabling organizations to programmatically automate "Right to be Forgotten" requests, securely wiping specific user profiles and their associated history. Additionally, the platform supports EU data residency, allowing European clients to mandate that their data is stored and processed exclusively on European servers. However, legal compliance ultimately relies on the business implementing a valid Consent Management Platform (CMP) before firing the tracking SDK.
Enterprise users can automatically route raw, unsampled event streams to modern data warehouses like Snowflake, BigQuery, or Amazon S3.
The platform features a robust Data Pipelines add-on designed for enterprise data teams that need to centralize their behavioral data. It enables automated, continuous, or daily exports of raw, hit-level JSON data directly into cloud data warehouses (Google BigQuery, Snowflake) or cloud storage buckets (Amazon S3, Google Cloud Storage). This allows data engineers to seamlessly merge in-app behavioral data with external financial records or use it to train internal machine learning models. Unlike platforms that artificially restrict raw data access or charge prohibitive per-query extraction fees, this pipeline is highly reliable and designed specifically for massive enterprise scale.
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 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.
Secure enterprise access is centrally managed via SAML 2.0 Single Sign-On, integrating with major identity providers like Okta and Azure AD.
To comply with enterprise IT security standards, the platform fully supports SAML 2.0 Single Sign-On (SSO). This feature allows administrators to integrate the analytics workspace directly with centralized corporate identity providers, such as Okta, Microsoft Entra ID (Azure AD), Google Workspace, or OneLogin. Implementing SSO ensures that access is strictly governed by corporate policies, enabling multi-factor authentication, automated user provisioning, and instant access revocation. This centralized management is a mandatory requirement for large organizations, mitigating the severe security risks associated with shared credentials and unmanaged standalone user accounts.
Analysts can build complex user cohorts based on detailed behavioral histories and seamlessly export them to external marketing platforms.
The platform features a highly sophisticated cohort builder designed for precise targeting and analysis. Users can define segments based on complex combinations of historical events, timeframes, and specific user properties (e.g., "Users in New York who used feature X three times but never purchased"). These dynamic cohorts can be used to filter any report within the platform. Crucially, through the platform's extensive integration ecosystem, these highly specific behavioral segments can be automatically synced to external marketing automation tools (like Braze, HubSpot, or Iterable) to trigger personalized emails or in-app messages based directly on recent product usage.