Cookieless measurement natively supports fully cookieless tracking mechanisms, ensuring robust data collection while strictly adhering to complex European privacy laws.
Engineered primarily as a privacy-compliant alternative to mainstream analytics, this platform deeply integrates cookieless tracking as a core feature rather than a workaround. When users decline tracking cookies via a consent banner, the system can dynamically switch to capturing anonymous, non-personal data hits. It utilizes short-lived session hashes to track immediate navigation without storing persistent identifiers on the user's device. This ensures organizations can still measure aggregate traffic volumes, campaign performance, and basic site usage even when strict GDPR or ePrivacy consent is denied. This approach provides a significant competitive advantage in the European market, balancing the need for actionable marketing data with absolute legal compliance.
Native mobile tracking provides dedicated, privacy-focused SDKs for iOS and Android, allowing for secure tracking of mobile application lifecycles and custom events.
The platform extends its privacy-first measurement approach to mobile applications through dedicated, open-source SDKs for both iOS and Android. These SDKs automatically track essential app metrics like screen views, app launches, and crashes, while providing developers the flexibility to instrument custom events and user variables. A key differentiator is that these SDKs are designed specifically to operate without violating mobile OS privacy frameworks (like Apple's App Tracking Transparency), ensuring data collection remains compliant even in strict mobile environments. However, the ecosystem integration is less expansive than tools built by major ad networks; it excels in pure measurement and privacy compliance rather than deep, automated ad-network activation.
The platform uses a flexible event tracking model, allowing users to define custom categories, actions, and names, alongside unlimited custom dimensions.
Custom event tracking is highly adaptable, allowing analysts to monitor any specific user interaction that standard page views miss. It utilizes a familiar, structured event schema (Category, Action, Name) that makes migrating from legacy analytics platforms exceptionally straightforward. More importantly, this structure can be heavily enriched by attaching numerous custom dimensions to a single event, providing deep contextual data (e.g., tracking a 'Form Submit' event while passing the specific 'Form ID' and 'User Type' as dimensions). This tracking is typically deployed and managed via the platform's own tightly integrated Tag Manager. It offers a solid balance of structured reporting and custom flexibility, ideal for tracking complex B2B funnels or specialized SaaS applications.
Bot filtering utilizes an automated exclusion system based on established bot libraries, keeping analytics data clean without requiring complex manual rules.
To maintain reporting accuracy, the platform features a native bot and spider filtering mechanism. By default, it automatically scrubs incoming traffic against continuously updated, global lists of known web crawlers, search engine bots, and automated scrapers. This ensures that baseline metrics, such as conversion rates and time on site, are not artificially skewed by non-human traffic. While this automated list covers the vast majority of standard bot traffic effectively, the platform lacks the highly granular, firewall-like capabilities of dedicated cybersecurity tools to block sophisticated, custom-built scrapers targeting specific infrastructure. For the standard marketing and analytics use case, however, the background filtering is entirely sufficient and requires zero configuration.
The analytics interface features a flexible, widget-based dashboard builder, allowing teams to construct personalized views of critical business KPIs.
The platform provides a highly intuitive, drag-and-drop dashboard environment designed to surface key metrics quickly. Users can create multiple custom dashboards tailored for specific departments, assembling them from a wide variety of pre-built report widgets or custom tables. Analysts can easily add dynamic data filters to these dashboards, allowing stakeholders to toggle between different audience segments or date ranges without altering the underlying report structure. While it excels at consolidating top-level KPIs for daily operational monitoring, the visualization engine is relatively straightforward; it does not support the highly complex, multi-layered data storytelling or bespoke chart coding found in enterprise-grade Business Intelligence (BI) platforms.
Users can build highly customizable, multi-step funnel reports to analyze user flow and identify precise drop-off points in the conversion journey.
The platform features a robust, native funnel analysis tool that allows analysts to map out complex conversion paths step-by-step. Users can construct funnels using a mix of page views, specific custom events, and destination URLs. The resulting visualization clearly identifies the volume of users entering the funnel, the percentage of users progressing through each stage, and the exact points where drop-offs occur. A strong feature is the ability to easily segment the funnel output, for example, comparing the checkout completion rate of mobile users versus desktop users. It is a powerful, highly visual tool for conversion rate optimization, though it requires tracking events to be logically structured and consistently named during implementation.
The platform offers a visual User Flow report to analyze sequential navigation patterns and identify how visitors move between specific pages and events.
To understand organic user navigation, the platform provides a dedicated User Flow visualization. This interactive, tree-graph report maps the sequence of page views and custom events triggered during a session. Analysts can clearly see where users enter the site, the dominant paths they take, and exactly where they exit. A key strength is the ability to apply strict audience segments to these flows, allowing teams to compare, for example, the navigation paths of organic traffic versus paid campaign traffic. While effective for standard website journey analysis, the visual interface can become difficult to interpret on massive enterprise sites with thousands of diverging, complex URL structures.
The cohort reporting feature groups users by their initial acquisition date to measure long-term retention and returning user behavior.
Built directly into the custom reporting suite, the cohort analysis tool enables businesses to track visitor retention over specific timeframes (days, weeks, or months). By default, it groups visitors based on their first visit date and measures how many of those specific users return in subsequent periods. This is a foundational metric for understanding user loyalty and the long-term impact of specific marketing acquisition campaigns. However, the functionality is somewhat rigid; it excels at basic acquisition-based retention but lacks the ability to easily build advanced cohorts based on complex, sequential behavioral triggers (e.g., users who used feature A but not feature B), which is typically required by deep product analytics teams.
Real-time dashboards provide immediate, live visibility into active visitor counts, their geographic locations, and the specific events they are triggering.
The platform features a robust real-time tracking engine that provides immediate visibility into current website or app activity. Analysts can view an active visitor log that updates continuously, displaying live session data including geolocation, referring sources, active pages, and triggered custom events. This is highly beneficial for marketing teams needing to monitor the immediate impact of a newly launched campaign or for technical teams verifying that a new tag implementation is firing correctly in a live environment. Unlike some platforms that heavily sample or delay live data, this reporting is immediate and unsampled, making it a reliable diagnostic tool for day-to-day operations.
E-commerce tracking includes a dedicated e-commerce module to track product views, cart actions, and completed revenue, ensuring sensitive financial data remains secure.
The platform provides a specialized e-commerce tracking framework that monitors the entire online shopping lifecycle. By implementing standard e-commerce variables, businesses unlock dedicated reports that automatically calculate total revenue, average order value, cart abandonment rates, and individual product performance. A massive advantage for enterprise retailers is the platform's robust privacy and on-premise hosting capabilities, which guarantee that sensitive transactional data and revenue figures are never shared with external advertising networks. However, to fully leverage these reports, developers must strictly adhere to the required data layer schema, which demands a more complex implementation than simple pageview tracking.
Multi-channel attribution reports allow marketers to apply various standard models (like First Click or Linear) to understand campaign performance.
To help marketers evaluate campaign ROI, the platform includes a native Multi-Channel Attribution tool. Rather than defaulting strictly to last-click measurement, analysts can apply various standard models—such as First Click, Last Non-Direct Click, Linear, Position-Based, and Time Decay—to any defined conversion goal. This is crucial for understanding how top-of-funnel awareness campaigns assist in driving final conversions. The interface allows for easy model comparison to see how credit shifts between channels. However, it does not currently feature proprietary, machine-learning-driven algorithmic attribution (Data-Driven Attribution) that automatically weights touchpoints based on historical conversion probabilities, relying instead on these fixed, rule-based models.
The platform is built natively for absolute privacy compliance, featuring an integrated Consent Manager to strictly govern data collection based on user choices.
As a primary competitor to US-based analytics, this European platform is engineered specifically for uncompromising GDPR, CCPA, and HIPAA compliance. Its most significant advantage is the native, deeply integrated Consent Manager. Unlike platforms that require third-party CMP integrations, this system allows businesses to create custom consent banners directly within the UI. Crucially, the analytics tracking mechanism is hardwired to this consent state; it will automatically block or modify tracking tags based on the user's specific privacy selections, ensuring zero unauthorized data collection. It also includes comprehensive tools for processing data deletion requests and anonymizing IP addresses, making it a highly secure choice for the public sector and healthcare.
Users can easily export raw, unsampled session and event data via an API or direct integration with cloud data warehouses like BigQuery.
Recognizing the needs of enterprise data teams, the platform provides seamless access to raw, unsampled data. For cloud deployments, it offers a direct, native integration with Google BigQuery, Microsoft Azure, and Amazon S3, automatically exporting hit-level data on a daily basis. For on-premise installations, data teams have direct SQL access to the underlying ClickHouse database. This unrestricted access is critical for organizations that want to build proprietary attribution models, merge web behavior with offline CRM data, or feed machine learning algorithms. Unlike some competitors that charge exorbitant premium fees for raw data pipelines, this capability is a standard offering for enterprise accounts.
Enterprise clients have extensive control over data retention policies, with options to retain raw data for 25 months or longer, depending on the contract.
The platform offers highly flexible data retention policies tailored to enterprise compliance requirements. For premium cloud accounts, the standard retention period for raw, unaggregated data is typically 14 to 25 months, allowing for robust year-over-year reporting. However, clients can negotiate custom contracts to retain this hit-level data indefinitely if required for long-term historical modeling. Importantly, the platform allows administrators to configure automated data purging rules to comply with strict data minimization policies, ensuring that user-level identifiers are deleted after a set period while aggregated reporting totals are preserved indefinitely.
The platform supports native mobile app tracking via iOS and Android SDKs, fully integrated with its rigorous privacy and consent management framework.
Mobile application measurement is supported through dedicated SDKs for iOS and Android environments. These SDKs allow developers to track standard app lifecycle metrics (installs, updates, crashes) alongside highly specific custom in-app events. The defining feature of this mobile tracking is its seamless integration with the platform's overarching privacy architecture. Developers can easily map in-app consent dialogs directly to the analytics engine, ensuring that mobile data collection adheres to strict privacy laws just as rigorously as the web tracking does. While excellent for privacy-compliant measurement, it does not offer the deep, automated ad-network integrations or in-app messaging capabilities found in mobile-first marketing platforms like Firebase or Clevertap.
The platform provides robust enterprise security with native support for SAML 2.0 Single Sign-On, integrating smoothly with major identity providers.
To meet the strict security requirements of enterprise IT departments, the platform natively supports SAML 2.0 Single Sign-On (SSO). This allows organizations to centralize analytics access through corporate identity providers like Microsoft Entra ID (formerly Azure AD), Okta, or Google Workspace. By implementing SSO, administrators can enforce multi-factor authentication, manage user provisioning automatically through corporate directories, and instantly revoke access when an employee leaves the company. This centralized security architecture is a critical requirement for enterprise deployments, ensuring that sensitive behavioral data is never exposed through weak or shared standalone passwords.
Analysts can construct highly granular audience segments using complex behavioral and demographic rules, applicable across all historical reports.
The platform features a highly capable segmentation engine that allows analysts to isolate specific subsets of traffic across any standard or custom report. Users can build complex, multi-layered segments using a combination of page views, custom events, geolocation, device type, and specific campaign sources. A key strength is that these segments process quickly and can be applied retroactively to historical data, allowing for deep exploratory analysis. However, it functions purely as an analytical segmentation tool; unlike marketing-heavy platforms with native Customer Data Platforms (CDPs), these segments cannot be instantly and automatically pushed to external advertising networks for real-time retargeting.