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Dashboard example

Account activation funnel dashboard

Count completed account milestones in order. Show how many accounts were eligible for each step.

Reviewed by the Telemetry product team on . We checked what each event and metric represents, the SQL and sample results, and the assumptions and data freshness needed to interpret them. Who reviews this page

What one row represents

One row per account with first or achieved milestone state.

Decision this helps you make

Select one transition and cohort for qualitative and instrumentation review.

Metric definitions

Put the denominator and units beside the chart

Use your own field names, but keep the same definition of one row. Check the linked schema before mapping your production events.

  • Signed-up accounts
  • Sources connected
  • First queries run
  • Dashboards created
Inspect the event schema
Complete DataFusion SQL

Review the query before adapting the fields

Adapt this query to your events before using it in production. Set a time range and environment filter, decide how to handle incomplete time buckets, and require enough events for a useful comparison.

WITH milestones AS (
  SELECT
    account_id,
    MAX(CASE WHEN event_name = 'source_connected'
      AND status = 'success' THEN 1 ELSE 0 END) AS connected,
    MAX(CASE WHEN event_name = 'query_completed'
      AND status = 'success' THEN 1 ELSE 0 END) AS queried,
    MAX(CASE WHEN event_name = 'dashboard_created'
      AND status = 'success' THEN 1 ELSE 0 END) AS dashboarded
  FROM product_events
  GROUP BY account_id
)
SELECT 'signed_up' AS step, COUNT(*) AS accounts FROM accounts
UNION ALL
SELECT 'source_connected', SUM(connected) FROM milestones
UNION ALL
SELECT 'query_completed', SUM(queried) FROM milestones
UNION ALL
SELECT 'dashboard_created', SUM(dashboarded) FROM milestones;
Data assumptions

Check these before publishing the result

  • Every milestone represents committed product state, not a UI click.
  • The same pseudonymous account identifier is used at every step.
  • Signup cohort and observation window are fixed before comparison.
Query review

Check the query and metric definitions

  • Use one account-level row before counting funnel steps.
  • Make each step denominator explicit when calculating conversion.
  • Segment by cohort before attributing a drop to onboarding changes.

What the results mean

What this result cannot prove by itself

A funnel describes observed milestones, not why accounts stopped. Pair it with qualitative research and segment by cohort before changing onboarding.

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API reliability and latency

Which endpoints have rising error rates or p95 latency, and enough requests to trust the comparison?

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Incident customer impact

Which accounts and operations were directly represented in a declared incident window?

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Validate the definition with known data

Test the query with known successes, failures, missing values, duplicates, and values at its limits. Check the results before using it in a production dashboard or alert.

Open the SQL LabRead the dashboard guideStart with Telemetry