# Growth KPI Dashboard -- Q1 2026 QBR **Prepared:** 2026-04-10 | **Presented:** 2026-04-11 | **Owner:** Growth Team --- ## CAC / Activation Mismatch Fix **The problem:** CAC denominator uses *all new signups* while activation rate uses *activated users only*. This inflates LTV/CAC (looks better than reality) and deflates activation rate when used alongside blended CAC. The two metrics tell contradictory stories in the same dashboard. **Root cause:** CAC = `total_spend / all_signups`, but LTV is calculated on *activated* customers only. When you divide an activated-customer LTV by a blended-signup CAC, the ratio is artificially favorable. **The fix -- use two CAC variants consistently:** | Metric | Formula | Use It For | |---|---|---| | **Blended CAC** | `total_spend / all_signups` | Channel efficiency, top-of-funnel cost | | **Activated CAC** | `total_spend / activated_users` | Unit economics, LTV/CAC ratio, payback | **Rule:** LTV/CAC and Payback Period MUST use Activated CAC. Channel View cards show Blended CAC. Both are labeled explicitly on every card. ```sql -- Blended CAC (channel efficiency) SELECT channel, DATE_TRUNC('month', u.created_at) AS month, SUM(ms.spend) / NULLIF(COUNT(DISTINCT u.id), 0) AS blended_cac FROM users u JOIN marketing_spend ms ON ms.channel = u.acquisition_channel AND ms.month = DATE_TRUNC('month', u.created_at) WHERE u.created_at >= DATE_TRUNC('quarter', CURRENT_DATE) - INTERVAL '1 quarter' GROUP BY channel, DATE_TRUNC('month', u.created_at); -- Activated CAC (unit economics) SELECT DATE_TRUNC('month', u.created_at) AS month, SUM(ms.spend) / NULLIF(COUNT(DISTINCT u.id) FILTER ( WHERE u.id IN (SELECT user_id FROM activation_events WHERE milestone = 'activated') ), 0) AS activated_cac FROM users u JOIN marketing_spend ms ON ms.channel = u.acquisition_channel AND ms.month = DATE_TRUNC('month', u.created_at) GROUP BY DATE_TRUNC('month', u.created_at); ``` --- ## View 1: Executive Summary One-page view. 6 headline KPIs with QoQ trend, two charts, one alert strip. ``` ┌──────────────────────────────────────────────────────────────────┐ │ GROWTH EXECUTIVE SUMMARY Q1 2026 [Quarter ▼] │ ├───────────┬───────────┬───────────┬──────────┬────────┬─────────┤ │ MRR │ MRR │ Activated │ LTV/CAC │Payback │ Net Rev │ │ │ Growth │ CAC * │ Ratio * │Period *│ Churn │ │ $X.XM │ X.X% │ $XXX │ X.Xx │ X mo │ -X.X% │ │ ▲ X% QoQ │ ▲ X.Xpp │ ▼ $XX │ ▲ X.Xx │ ▼ Xmo │ ▲ X.Xpp│ ├───────────┴───────────┴───────────┴──────────┴────────┴─────────┤ │ * Uses Activated CAC (spend / activated users), not blended │ ├─────────────────────────────────┬────────────────────────────────┤ │ MRR TREND (12 months) │ MRR BRIDGE (Q1) │ │ ┌───────────────────────────┐ │ │ │ │ ╱── │ │ Start MRR $X.XM │ │ │ ╱───╱ │ │ + New $XXXk │ │ │ ╱───╱ │ │ + Expansion $XXXk │ │ │ ╱───╱ │ │ - Contraction ($XXk) │ │ │ ╱───╱ │ │ - Churned ($XXk) │ │ └───────────────────────────┘ │ = End MRR $X.XM │ │ Apr May Jun Jul Aug Sep Oct │ │ │ Nov Dec Jan Feb Mar │ Net New MRR: +$XXXk (+X.X%) │ ├────────────────────────────────┴────────────────────────────────┤ │ ALERTS │ │ [red] Churn spiked to X.X% in Mar (threshold: X.X%) │ │ [ylw] Expansion revenue slowing -- down XX% MoM │ │ [grn] Activated CAC improved $XX QoQ after channel rebalance │ └─────────────────────────────────────────────────────────────────┘ ``` **Headline KPI definitions:** | KPI | Definition | Target | |---|---|---| | MRR | Sum of normalized monthly subscription revenue (annual/12, quarterly/3) | Track trend | | MRR Growth | `(MRR_current - MRR_prior) / MRR_prior` month-over-month | > 5% MoM | | Activated CAC | `total_channel_spend / activated_users` | < $XXX | | LTV/CAC | `LTV / activated_cac` -- both sides use activated-user basis | > 3.0x | | Payback Period | `activated_cac / (ARPU * gross_margin)` in months | < 12 mo | | Net Revenue Churn | `(contraction + churned_mrr - expansion) / start_mrr` | < -2% (negative = expansion) | --- ## View 2: Channel View One row per acquisition channel. Blended CAC here (all signups), since this view evaluates channel top-of-funnel efficiency. ``` ┌──────────────────────────────────────────────────────────────────┐ │ CHANNEL PERFORMANCE Q1 2026 [Quarter ▼] │ ├──────────────────────────────────────────────────────────────────┤ │ Filter: [All Channels ▼] [Compare: Q4 2025 ▼] │ ├──────────┬────────┬──────────┬────────┬──────────┬──────────────┤ │ Channel │ Spend │ Signups │Blended │Activated │ Act. Rate │ │ │ │ │ CAC │ CAC │ │ ├──────────┼────────┼──────────┼────────┼──────────┼──────────────┤ │ Organic │ $XXk │ X,XXX │ $XX │ $XXX │ XX% │ │ Paid SEM │ $XXk │ X,XXX │ $XXX │ $XXX │ XX% │ │ Paid Soc │ $XXk │ X,XXX │ $XXX │ $XXX │ XX% │ │ Content │ $XXk │ X,XXX │ $XX │ $XXX │ XX% │ │ Referral │ $XXk │ XXX │ $XX │ $XX │ XX% │ │ Partner │ $XXk │ XXX │ $XXX │ $XXX │ XX% │ ├──────────┴────────┴──────────┴────────┴──────────┴──────────────┤ │ │ │ SPEND vs ACTIVATED CAC BY CHANNEL CHANNEL MIX SHIFT │ │ (bubble = signup volume) (Q4 vs Q1 stacked) │ │ ┌────────────────────────────┐ ┌──────────────────┐ │ │ │ y: Activated CAC │ │ Q4 ████████████ │ │ │ │ ●Paid Soc │ │ Q1 ████████████ │ │ │ │ ●PaidSEM │ │ │ │ │ │ ●Partner │ │ Organic ■ │ │ │ │ ●Content │ │ Paid ■ │ │ │ │ ●Organic ●Referral │ │ Content ■ │ │ │ │ x: Spend ──────> │ │ Referral■ │ │ │ └────────────────────────────┘ └──────────────────┘ │ ├──────────────────────────────────────────────────────────────────┤ │ INSIGHT: Paid Social blended CAC looks fine ($XX) but │ │ activation rate is only XX%, making activated CAC $XXX -- │ │ worst unit economics of any channel. Recommend reallocating │ │ $XXk to Content + Referral which activate at XX%+. │ └──────────────────────────────────────────────────────────────────┘ ``` **Why both CAC columns matter here:** A channel can look cheap on blended CAC but produce users who never activate, making its *real* cost (activated CAC) the highest in the portfolio. Showing both side-by-side exposes this. --- ## View 3: Activation Funnel Step-by-step conversion from signup to activated, with channel-level breakdown. ``` ┌──────────────────────────────────────────────────────────────────┐ │ ACTIVATION FUNNEL Q1 2026 [Quarter ▼] │ ├──────────────────────────────────────────────────────────────────┤ │ Segment: [All Users ▼] Channel: [All ▼] Cohort: [Q1 2026 ▼]│ ├──────────────────────────────────────────────────────────────────┤ │ │ │ FUNNEL STEPS │ │ │ │ Signup ████████████████████████████████████████████ X,XXX 100% │ │ │ │ Onboarding ▼ XX% drop-off │ │ Complete ██████████████████████████████████ X,XXX XX% │ │ │ │ First Key ▼ XX% drop-off │ │ Action ████████████████████████ X,XXX XX% │ │ │ │ Aha Moment ▼ XX% drop-off │ │ Reached ███████████████████ X,XXX XX% │ │ │ │ Activated ▼ XX% drop-off │ │ (Day 7) █████████████████ XXX XX% │ │ │ ├──────────────────────────────┬───────────────────────────────────┤ │ DROP-OFF BY CHANNEL │ TIME TO ACTIVATE (distribution) │ │ │ │ │ Step w/ biggest leak: │ Median: X.X days │ │ "First Key Action" │ P75: X.X days │ │ │ P90: XX.X days │ │ Channel Drop vs Avg │ │ │ Paid Soc XX% +XXpp │ ┌─────────────────────────────┐ │ │ Paid SEM XX% +Xpp │ │ ▁▃▇█▇▅▃▂▁▁ │ │ │ Partner XX% +Xpp │ │ 1 3 5 7 10 14 21 day│ │ │ Content XX% -Xpp │ └─────────────────────────────┘ │ │ Organic XX% -Xpp │ │ │ Referral XX% -XXpp │ Users activating after day 7 │ │ │ are XX% of total -- consider │ │ │ extending window to 10 days. │ ├──────────────────────────────┴───────────────────────────────────┤ │ ACTIVATION DEFINITION │ │ A user is "activated" when they complete ALL of: │ │ 1. Finish onboarding (profile + team setup) │ │ 2. Perform first key action (e.g., create first [object]) │ │ 3. Reach aha moment (e.g., receive first [value event]) │ │ 4. Return on a second day within 7 calendar days of signup │ │ │ │ This definition feeds into Activated CAC across all views. │ └──────────────────────────────────────────────────────────────────┘ ``` **Activation definition governance:** The activation milestones above are the single source of truth for "activated user" across all three views. Any change to this definition must update the CAC calculations simultaneously. --- ## Implementation: Activation Query ```sql -- Activation funnel with per-step counts WITH signup AS ( SELECT id AS user_id, acquisition_channel, created_at FROM users WHERE created_at >= DATE_TRUNC('quarter', CURRENT_DATE - INTERVAL '1 quarter') AND created_at < DATE_TRUNC('quarter', CURRENT_DATE) ), steps AS ( SELECT s.user_id, s.acquisition_channel, s.created_at AS signup_at, oe.completed_at AS onboarding_at, fka.completed_at AS first_action_at, aha.completed_at AS aha_at, d2.event_at AS day2_return_at, -- Fully activated = all four milestones within 7 days CASE WHEN oe.completed_at IS NOT NULL AND fka.completed_at IS NOT NULL AND aha.completed_at IS NOT NULL AND d2.event_at IS NOT NULL AND d2.event_at <= s.created_at + INTERVAL '7 days' THEN TRUE ELSE FALSE END AS is_activated FROM signup s LEFT JOIN onboarding_events oe ON oe.user_id = s.user_id LEFT JOIN ( SELECT user_id, MIN(created_at) AS completed_at FROM key_actions GROUP BY user_id ) fka ON fka.user_id = s.user_id LEFT JOIN ( SELECT user_id, MIN(created_at) AS completed_at FROM aha_events GROUP BY user_id ) aha ON aha.user_id = s.user_id LEFT JOIN ( SELECT user_id, MIN(event_at) AS event_at FROM user_sessions WHERE event_at::date > (SELECT created_at::date FROM users u2 WHERE u2.id = user_sessions.user_id) GROUP BY user_id ) d2 ON d2.user_id = s.user_id ) SELECT acquisition_channel, COUNT(*) AS signups, COUNT(*) FILTER (WHERE onboarding_at IS NOT NULL) AS onboarded, COUNT(*) FILTER (WHERE first_action_at IS NOT NULL) AS first_action, COUNT(*) FILTER (WHERE aha_at IS NOT NULL) AS aha_reached, COUNT(*) FILTER (WHERE is_activated) AS activated, ROUND(100.0 * COUNT(*) FILTER (WHERE is_activated) / NULLIF(COUNT(*), 0), 1) AS activation_rate FROM steps GROUP BY ROLLUP (acquisition_channel) ORDER BY acquisition_channel NULLS FIRST; ``` --- ## Data Consistency Checklist Before QBR presentation, verify: - [ ] Activated CAC is used in LTV/CAC and Payback (not blended) - [ ] Channel View labels distinguish "Blended CAC" vs "Activated CAC" - [ ] Activation definition in funnel view matches the WHERE clause in CAC query - [ ] MRR normalization matches Finance (annual/12, quarterly/3) - [ ] Date ranges are identical across all three views (Q1 = Jan 1 - Mar 31) - [ ] Channel spend totals reconcile with Finance's marketing spend report - [ ] Cohort boundaries use `DATE_TRUNC('month')`, not daily granularity --- ## QBR Talking Points 1. **CAC/Activation mismatch is now fixed.** We split into Blended CAC (channel efficiency) and Activated CAC (unit economics). LTV/CAC uses activated basis -- expect the ratio to look X.Xx lower than last quarter's deck, but it is now accurate. 2. **Channel rebalance opportunity.** Paid Social has the lowest blended CAC but worst activation rate, making its activated CAC the highest. Shifting budget to Content/Referral improves true unit economics. 3. **Activation funnel bottleneck.** The biggest drop-off is at "First Key Action." This is where channel quality diverges -- Referral users convert at XXpp above average, Paid Social at XXpp below. 4. **Net revenue churn.** Watch contraction trend -- expansion revenue is slowing, which will flip net churn positive if not addressed.