What goes in a membership growth dashboard?

A membership dashboard tracks one balance: members joining versus members leaving, and what each side is worth. Patronage and recurring-community businesses live or die on the second derivative — net member change — because gross signups can look healthy while the base quietly shrinks. Tier mix and tenure cohorts explain why.

Which cards belong on this dashboard?

• Active members today and net change vs. last month
• New members and churned members per month, side by side
• Membership MRR and average revenue per member
• Tier mix: members and MRR by tier
• Upgrades and downgrades between tiers per month
• Member lifetime support: cumulative amount paid per member
• Churn rate by tenure cohort (month 1, months 2-3, 4-12, 12+)
• Members at risk: failed payments and expiring cards

What data does this dashboard need?

• A member table with member ID, tier, start date, cancellation date, and monthly amount
• A daily snapshot of active membership state — point-in-time counts cannot be rebuilt from a mutable table
• Tier change events with the old tier, new tier, and effective date
• Payment attempts including failures, so involuntary churn is separable
• Cumulative payments per member for lifetime value views

How do you build it?

1. Land the source data in a database — via a managed connector, scheduled API pulls, or a CSV loaded with mb upload csv — keeping raw IDs, timestamps, currencies, and fee lines intact.
2. Build models at the grain this dashboard needs, with gross, fee, tax, and net amounts as explicit columns rather than per-card arithmetic.
3. Create one saved question per card and certify the shared models so every dashboard downstream inherits the same definitions.
4. Add dashboard filters for Month, tier, acquisition source, voluntary vs. involuntary churn, country.
5. Show data freshness on the dashboard — these sources lag, and every viewer should see by how much.

Example card SQL

WITH months AS (
  SELECT generate_series(
    date_trunc('month', CURRENT_DATE) - INTERVAL '11 months',
    date_trunc('month', CURRENT_DATE),
    INTERVAL '1 month'
  )::date AS month
), state AS (
  SELECT
    m.month,
    -- "Active" is evaluated at month end: started before the month
    -- closed and not yet cancelled at that point.
    COUNT(*) FILTER (
      WHERE mem.started_at < m.month + INTERVAL '1 month'
        AND (mem.cancelled_at IS NULL
             OR mem.cancelled_at >= m.month + INTERVAL '1 month')
    ) AS active_members,
    COUNT(*) FILTER (
      WHERE date_trunc('month', mem.started_at)::date = m.month
    ) AS new_members,
    COUNT(*) FILTER (
      WHERE date_trunc('month', mem.cancelled_at)::date = m.month
    ) AS churned_members,
    SUM(mem.monthly_amount_usd) FILTER (
      WHERE mem.started_at < m.month + INTERVAL '1 month'
        AND (mem.cancelled_at IS NULL
             OR mem.cancelled_at >= m.month + INTERVAL '1 month')
    ) AS membership_mrr
  FROM months m
  JOIN creator_members mem
    ON mem.started_at < m.month + INTERVAL '1 month'
  GROUP BY m.month
)
SELECT
  month,
  active_members,
  new_members,
  churned_members,
  new_members - churned_members AS net_member_change,
  ROUND(membership_mrr, 2) AS membership_mrr,
  ROUND(membership_mrr / NULLIF(active_members, 0), 2) AS arpu,
  ROUND(
    100.0 * churned_members
    / NULLIF(LAG(active_members) OVER (ORDER BY month), 0), 1
  ) AS monthly_churn_pct
FROM state
ORDER BY month;

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