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.
Utilizes a flexible, event-driven schema, replacing rigid session-based models.
Rejecting traditional session-centric data models, this approach uses an event-driven schema. Each user action is treated as an independent event linked to their User ID, regardless of session timing. This model allows product teams to define metrics and KPIs based on specific event combinations. However, the freedom it offers requires a centrally managed data dictionary to prevent chaos. Without strict governance, the custom taxonomy can become disorganized.
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 data model operates on a profoundly flexible, variable-driven schema, allowing data architects to design a bespoke analytics structure from the ground up.
Unlike standard analytics tools that enforce predefined event categories, this platform provides an open, highly malleable data schema. Data architects build custom models using hundreds of available variables (eVars for persistent dimensions, props for traffic, and custom events for metrics). Administrators have complete control over how these variables behave, including complex allocation rules (e.g., linear, participation, or first-touch) and precise expiration conditions (e.g., variable expires after a visit, after a purchase, or after 30 days). This allows enterprise organizations to map their precise business logic directly into the analytics infrastructure. The trade-off is that this complete structural freedom necessitates exhaustive initial planning; poor architecture design will result in chaotic, fragmented reporting.
Supports basic custom data models, lacking flexibility.
Support for custom data models is limited, offering basic functionality for defining data structures. This feature caters to organizations with specific data organization needs, allowing tailored data collection. However, the implementation lacks flexibility and may require additional development or third-party integrations for complex data modeling. It primarily serves those needing customization without extensive re-engineering. Users with deep needs might find it insufficient.
