1. Compare journeys and cohorts
Open Funnel to follow the decisions, attempts, retries and method switches inside a flow. Select the flow and filter by application, touchpoint, environment or configured custom tags. Cohort comparisons help locate where journeys differ, including platform authenticators versus security keys. Keep the metric’s population in view:- Flow outcome: whether the whole journey completed, was skipped or remained incomplete.
- Attempt outcome: what happened in one method attempt, even if the flow later completed through another method.
- Engagement: observed interaction. A field receiving focus or a prefilled value alone does not establish engagement; a completed Conditional UI ceremony does. Input completion can distinguish manual, pasted, autofilled and prefilled values without treating those labels as proof of a particular provider.
Follow a change over time
Use the funnel’s history and trend views to inspect node, edge and method metrics across daily or hourly buckets. Compare a period with a weekday-aligned earlier period, or compare cohorts within a period. Keep your interval, filters and denominator consistent across both sides. Hourly history selection is limited to 21 days and depends on the hourly data retained by your project. Change suggestions distinguish a cohort’s own movement from its gap against a comparison group. A persistent gap deserves investigation even when it has not recently grown. Rankings identify where outcomes are affected; they do not establish causation. Expanded and hidden funnel nodes are retained in shared URL state. Hiding a node changes the view, not the underlying counts. Share the console URL to preserve the selected view and filters for colleagues with project access.2. Investigate errors
Open Error Overview to inspect grouped errors and their environment correlations. Correlations can include browsers and versions, native app attributes and configured tags. A truncated result is not the complete population; retain the console’s truncation warning when interpreting the largest returned cells. Compare errors across experiment variants in Experiment Analysis. Its table aligns each error across variants and supports severity and presence filters, rate-difference ranking and frequency ranking. A dash means no flows recorded that error in the variant. Error detail depends on the flavours embedded in that project’s variant series; older data may lack that detail. Experiment conclusions depend on the recorded exposure and available sample. Allocation diagnostics help find mismatched exposure, but do not prove that visitors were randomly assigned. Treat observed differences as evidence to investigate alongside your experiment design. For a concrete attempt, use User Search. Follow the journey, then expand its technical events to inspect what the integration supplied. Verify your integration explains this workflow.3. Read a finding
Open Findings for Corbado-authored explanations of authentication problems. Customers see published findings. Corbado manages their content, status and evidence; internal drafts and notes are restricted to staff. Open a finding’s drawer to review:- Explanation and action: what happens, how to reproduce it and how to fix it.
- Who is affected: the measured segment, its failure rate and the comparison group. Check the group labels before interpreting the difference.
- What we checked: supported, ruled-out and open hypotheses with their evidence.
- Impact: affected users and estimated lost completions, with the measured window and any sampling qualification. Population estimates assume the sampled flows are representative.
- Timeline: the recorded changes, evidence and status updates.