Name the product and describe the onboarding funnel. Juma returns a user activation diagnostic with per-step drop-off rates and ranked fixes.
Describe the product and walk through the intended onboarding sequence from first signup to first meaningful action. Juma traces each step of the user activation journey, calculates drop-off rates, and cross-references event counts to catch broken instrumentation before it sends the team in the wrong direction.
After the diagnostic flags a tracking gap, this step examines the activation event itself to determine whether the problem is the product or the measurement. It checks how the event is defined, where it fires, and what it misses.
The audit covers:
The output is a specific recommendation on what the activation metric should track and how to correct the definition.
The funnel shows where users drop off. This step examines the onboarding flow to understand why. It surfaces timing and behavior patterns that aggregate drop-off rates alone cannot reveal.
The analysis covers:
The output is a step-by-step breakdown with flagged friction points the team can act on directly.
Some users complete onboarding and then go quiet. This step identifies that cohort, measures it, and profiles when and where users go inactive after the onboarding flow ends.
The analysis covers:
The output is a segmented re-engagement approach matched to each drop-off timing pattern.
The diagnostic surfaced the problems. This step turns them into a structured action plan ranked by expected impact on the activation rate, not by ease of implementation.
The plan includes:
The output is ready to bring directly into a sprint planning session or roadmap review.
Teams build one Juma project per client and add context over time. Every flow the team runs for that client pulls from the same project. If a project already exists, adding activation context means each diagnostic starts from the client's own definitions and benchmarks.
Add a short description to each knowledge item in the project's info field so Juma knows what each file contains and when to use it. For example:
This Flow returns a complete user activation diagnostic, including the tracking validation check most teams skip manually, in minutes. The equivalent manual process takes a product or growth analyst several hours to a full day, requiring data from multiple sources, per-step drop-off calculations, and event count reconciliation across platforms.
Most teams skip the instrumentation validation step entirely because it requires comparing event counts across different tools and judging whether the ratios are statistically plausible. This is the step where manual analysis is most likely to produce a costly misdiagnosis.
The time saving is largest on the tracking validation piece. This is the check that most often changes what the team decides to fix first, and the one that separates a product redesign decision from an engineering fix.
The diagnostic maps the full user activation journey from first signup through each onboarding step to the first meaningful action. It shows how many users arrived at each stage and how many completed it, and color-codes the severity of each drop-off so critical losses are immediately visible.
Beyond drop-off rates, the analysis looks for behavioral patterns the team may not spot in a standard funnel dashboard. Looping behavior, where users cycle through the same onboarding steps more than once, often signals a confusing or circular flow rather than low user motivation. Exit points that correlate with specific steps reveal friction the team can remove or delay.
Each finding connects to a recommendation, so the diagnostic does not just describe the problem but gives the team a clear starting point for fixing it.
The check compares related event counts that should correlate, such as activation events against messaging or engagement events from the same period. When the ratio between two related events is statistically implausible, the diagnostic flags it as a likely instrumentation problem rather than real user behavior.
The most common version of this problem is a steep drop-off between an onboarding completion event and an activation event. If 26,000 users triggered the onboarding completion event but only 17 triggered the activation event in the same period, the activation event is almost certainly misfiring, not reflecting a real user behavior pattern.
This distinction changes the fix entirely. A real user drop-off requires a product change. A broken event requires an engineering fix. Without the cross-reference check, teams routinely spend months redesigning an onboarding flow that was never the actual problem.
A description of the product and the intended onboarding funnel is enough to get started. The more detail the team provides about the intended user journey, the more precisely the analysis can identify where real behavior diverges from the designed path.
Live connections to PostHog or Google Analytics pull real event-level data automatically and enable the full tracking validation check. PostHog provides the most detailed view, capturing in-product events and user paths that GA4 does not capture at the same level of granularity.
Teams without a connected analytics tool can share event data exports, screenshots of their funnel dashboard, or a written description of the intended flow. The diagnostic runs from any of these inputs and returns a complete analysis, though live connections produce the most reliable tracking validation results.
Name your channels to compare and get a conversion funnel analysis across paid, organic, and direct traffic with multi-channel attribution data in one report.
