funnel-analysis

Create dbt funnel models by defining sequential onboarding or checkout steps with your event data, then generate summary metrics and a reusable macro to measure conversion and identify drop-offs across cohorts or devices.

Yes, segment funnel drop-offs by cohorts, channels, and device types in dbt. The generated models and summary tables apply built-in dbt tests to validate conversion data and isolate drop-offs across these specific user segments.

Step-by-step conversion funnel modeling supports onboarding activation, checkout flows, and feature adoption analysis. It helps analytics engineers identify drop-offs and support data-driven optimizations to improve conversion rates across product experiences.

Yes, you need a data-stack-context file to create dbt funnel models. It tailors funnel definitions to your specific warehouse, event data, and segmentation needs, alongside the dbt project and funnel macro required to produce the models and tests.

The best way to structure dbt models for onboarding and purchase funnels is to build end-to-end fct_ and mtr_ models with a reusable Jinja macro. This approach quantifies conversion between steps and generates drop-off analyses across user segments.

Your dbt funnel model may fail to identify user drop-offs if the data-stack-context is missing or steps are misaligned with event data. Properly defining steps and leveraging built-in dbt tests ensures accurate drop-off analysis across cohorts.