Get a signup-to-activation diagnostic with step-by-step drop-off rates, onboarding flow analysis, tracking validation, and prioritized recommendations to turn more signups into active users.
Name the product and describe the activation path. The flow returns a branded PDF with an activation funnel chart, per-step diagnosis, a cross-reference check that flags broken instrumentation before the team acts on misleading data, and severity-ranked recommendations.
The team gets a clear answer to "where exactly are we losing people after signup?" The report traces the user journey from first signup through each onboarding step to the moment someone takes their first meaningful action. Each step shows how many users arrived, how many made it through, and how severe the drop-off is, color-coded so the critical losses stand out immediately.
Beyond the numbers, the diagnostic looks for patterns the team might not spot in a dashboard. Looping behavior, where users cycle through the same onboarding steps repeatedly, often signals a confusing or circular flow rather than low motivation. Exit points that correlate with specific steps (like a survey or a verification email) reveal friction the team can remove or delay. When data from multiple sources is available, event counts are cross-referenced to catch a common but expensive misdiagnosis: a steep drop-off that is actually a broken tracking event, not real user churn. The difference between "users aren't reaching this step" and "the event for this step isn't firing" changes the fix from product work to engineering work.
Each recommendation is ranked by expected impact on the end-to-end activation rate, so the team starts with the change most likely to move the number.
No product analytics tool connected? Share event data, screenshots, or a description of the onboarding flow, and the diagnostic works from that. Live connections to PostHog or GA4 pull the real numbers automatically.
A 98% drop between "completed onboarding" and "activated user" looks like a catastrophic product problem. But when the analysis cross-references messaging events (26,000 in the same period) against the activation event (17 in the same period), the diagnosis changes entirely: users ARE using the product, the activation event is just misfiring. Without this cross-reference, the team spends months redesigning an onboarding flow that was never the problem. Tracking validation catches these gaps before they become expensive misdiagnoses. It compares related events that should correlate, flags ratios that are statistically implausible, and separates real user behavior problems from instrumentation problems. The distinction between "users aren't reaching this step" and "the event for this step isn't firing" changes the fix from product work to engineering work.
The diagnostic flagged a potential tracking gap. This digs deeper to determine whether the activation event is too narrowly defined, misconfigured, or missing from parts of the onboarding flow.
The activation event count looks too low relative to other engagement signals. Audit the event definition and suggest what the activation metric should actually track.
The funnel showed where users drop off. This examines the onboarding flow itself to understand why: which steps take too long, which ones cause users to loop back, and which ones act as unexpected exit points.
Break down the onboarding flow step by step. Show median time per step, completion rates, and flag any steps where users loop back or exit unexpectedly.
Some users complete onboarding and then go quiet. This identifies that cohort, how large it is, when they drop off, and what a re-engagement strategy could look like.
How many users completed onboarding in the last 30 days but never took a meaningful action? Show the size of this cohort, how far they got, and suggest a re-engagement approach.
The diagnostic surfaced the problems. This turns findings into a prioritized list of changes with expected impact on activation rate, effort level, and a suggested timeline.
Turn the activation funnel findings into a prioritized action plan. For each recommendation, include expected impact on activation rate, effort level, and a suggested timeline.
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.
Activation Event Definitions
What counts as "activated" for this product, mapped to the analytics event name. Also include any intermediate events that define the onboarding journey (signup, email verified, profile completed, first action). When this exists, the diagnostic uses the client's actual activation criteria rather than inferring them.
Onboarding Flow Description
The intended onboarding sequence: which steps a new user goes through, what each step asks them to do, and where the team expects the biggest friction. This gives the analysis a map to compare against the data, so it can flag steps where real behavior diverges from the designed flow.
Activation Targets
Target activation rate, acceptable time-to-activate, and historical baselines. With this in the project, every diagnostic measures against the client's own goals instead of SaaS industry 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:
