Skene
Playbook

Onboarding without mature event instrumentation

Use proxy signals, qualitative insight, and a staged instrumentation roadmap to move from guesses to measurable onboarding.

Job-to-be-done: Design and improve onboarding journeys when product analytics are missing, incomplete, or unreliable.

Updated 2026-01-05

Problem context

You want to improve onboarding and activation but do not trust your event data, or do not have any product analytics set up.

Teams rely on support tickets, anecdotal feedback, or gut feel to judge whether onboarding changes are working.

Engineering is wary of “yet another tracking project”, and no one owns the instrumentation layer end-to-end.

What breaks if this is not solved

  • Onboarding experiments become hard to interpret; teams ship changes and move on without clear evidence of impact.
  • Leadership loses confidence in product metrics because numbers conflict across tools or cannot be reproduced.
  • PLG initiatives stall since core metrics like activation, time-to-value, and retention cannot be measured reliably.

When this playbook applies

  • You have little or no structured product analytics today, or existing events are inconsistent and undocumented.
  • You can access at least some operational data (for example, signups, workspaces created, billing events) from your application database or backend logs.
  • The team is willing to invest in a staged instrumentation effort in parallel with onboarding improvements.

System approach

Separate the problem into two tracks: redesigning onboarding flows using qualitative insight and low-cost proxies, and building a minimum viable instrumentation backbone in parallel.

Start by defining a small set of events tied to real jobs-to-be-done and activation, not an exhaustive catalog of every click.

Use simple, durable mechanisms (for example, backend events or a small SDK) that are easy to maintain as the product evolves.

Execution steps

  1. Document your current onboarding flows in plain language: entry points, key screens, and the outcomes you want new users to reach.
  2. Interview a small sample of recent signups (both successful and churned) to understand where they got stuck or confused during onboarding.
  3. Define a first-pass activation milestone and TTV goal, even if you cannot measure them precisely yet.
  4. Identify 3–5 proxy signals you can measure today without a full analytics setup (for example, projects created, workspaces with any activity, first invoice paid).
  5. Create a minimum event schema focused on onboarding and activation: signup, first key action, activation event, and a handful of high-leverage steps.
  6. Work with engineering to implement these events in the most stable layer you can (often the backend or a central events module).
  7. Stand up a simple reporting path—this can be a warehouse table, a basic dashboard, or even CSV exports—that lets you see funnel and cohort views for these events.
  8. Redesign one onboarding flow using qualitative findings: remove obvious blockers, clarify copy, and guide users toward the proxy signals you can observe.
  9. Compare cohorts before and after the new flow using your proxy metrics while you continue improving event coverage.
  10. Iteratively expand the event schema and dashboards so you can graduate from proxies to direct measurement of activation and time-to-value.

Metrics to watch

  • Coverage of core onboarding events

    Reach and maintain >95% of relevant flows emitting events.

    Track how often expected events are missing in logs for known onboarding paths; missing events are instrumentation bugs.

  • Proxy activation rate based on available signals

    Trend up as you improve flows, with definitions documented and versioned.

    Examples include “workspaces with at least one project created” or “accounts with any active usage in first 7 days”.

  • Median time from signup to first proxy success event

    Trend down as onboarding simplifies.

    This gives an approximate view of time-to-value before you have full activation instrumentation.

  • Share of code paths covered by the new events module

    Increase over time until core journeys are consistently instrumented.

    Measure by scanning routes or services that handle onboarding-related operations.

Failure modes

  • Trying to recreate a full, detailed analytics taxonomy from scratch instead of focusing on a small, high-impact event set.
  • Letting every team add their own events and naming conventions ad hoc, leading to future data debt.
  • Treating instrumentation as a one-off project rather than part of the regular development process and code review.
  • Waiting for “perfect data” before making any improvements to onboarding flows users are clearly struggling with today.

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