Corey Haines
ec31133ad9
A new skill (`/plan {client-name}`) that produces an exhaustive 13-section
Notion-paste-ready marketing plan structured by AARRR (Acquisition,
Activation, Retention, Referral, Revenue), customized to the client's
current budget, team, stage, and tooling stack, mapped to future
funding-stage capability unlocks.
Three-phase workflow:
- INIT: research + structured 10-topic intake; pulls data from wired
MCPs (Ahrefs, GA4, Stripe, Customer.io, etc.); offers to run
/audit-marketing first
- REVIEW: section-by-section interactive draft with user confirmation
on each of the 13 sections; resumable across sessions
- FINALIZE: compile, verification pass (cross-references, machine paths,
brand voice), and optional publish to a shared GitHub repo
The 13 sections:
1. Executive summary
2. Strategic frame
3. Current state (scored against the 17-section audit-marketing rubric)
4. Acquisition
5. Activation
6. Retention
7. Referral
8. Revenue
9. 90-day roadmap (AARRR-tagged, owner-assigned)
10. 12-month outlook (with funding-stage milestones)
11. Marketing operations stack (skills + MCPs per AARRR stage)
12. Tactical idea bank (all 139 ideas from `marketing-ideas` mapped to
AARRR + client-specific status: Now / Q2 / Q3+ / Q4+ / Skip)
13. Measurement, RACI, open decisions, appendix
References:
- methodology.md — full three-phase workflow
- plan-template.md — 13-section template with per-section guidance
- aarrr-framework.md — AARRR primer + diagnostic rules for which stage
is the binding constraint
- current-state-rubric.md — 17-section audit-marketing rubric mapped
to AARRR
- ops-stack-mapping.md — all 26+ marketing skills + MCP/API integrations
mapped to AARRR stages
- idea-cross-reference.md — all 139 marketing-ideas mapped to AARRR
with timing defaults
- funding-stage-unlocks.md — 5 standard funding tiers (pre-seed through
Series B+) with budget profiles and capability unlocks
- measurement-framework.md — north-star patterns by business model,
leading indicators by AARRR stage, review cadence
- client-types.md — variations by client archetype (B2B SaaS, D2C
consumer app, hybrid hardware+software, marketplace, dev tool,
deep-tech/clinical, commerce)
- example-olo.md — canonical reference plan from a real fCMO engagement
Updates:
- plugin.json + marketplace.json bumped to 2.3.0
- marketplace description updated to 43 skills
- VERSIONS.md entry added
Total skills: 43.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
188 lines
8.6 KiB
Markdown
188 lines
8.6 KiB
Markdown
# Measurement Framework — KPIs, North Stars, Cadence
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Every plan needs a measurement section that tells the team how to know if the plan is working. This doc is the source for Section 13's measurement subsection.
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## The north-star principle
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A north star is one metric that captures the business-model thesis at the highest level. It should:
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- Be derivable from the funnel + revenue model
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- Move slowly enough to be a strategic compass (not whipsawed by weekly noise)
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- Trade off correctly against other metrics — improving the north star should generally improve the business
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Don't default to "ARR" or "MRR" alone. Those are outcomes, not norths. Pick something that captures the business model.
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## North-star patterns by business model
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### B2B SaaS (subscription)
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- **Net Revenue Retention (NRR)** — keeps existing customers + expansion in focus
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- Alternative: "Logo retention × expansion ARR"
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- Why: ARR alone hides churn / lets gross-add growth mask product fit problems
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### D2C consumer app (subscription)
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- **Blended LTV / blended CAC** — keeps unit economics honest as paid layer scales
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- Alternative: "Day-35 paid users from cohort × LTV"
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- Why: monthly subscription metrics are volatile; cohort × LTV smooths it
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### Hybrid hardware + software (e.g., Olo)
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- **Blended LTV / blended CAC across hardware + software** — captures the wedge thesis
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- Alternative: "Hardware-buyers-to-subscriber conversion × blended margin"
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- Why: hardware revenue isn't free (cost to make); subscription revenue isn't expensive to acquire if hardware funds it
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### Marketplace (two-sided)
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- **Liquidity ratio × take-rate** — captures both sides + monetization
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- Alternative: "Monthly transacting users × take-rate × repeat frequency"
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- Why: GMV alone doesn't capture whether the marketplace is becoming a habit
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### Developer tool / open source
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- **Weekly active developers × paid-conversion** — captures both adoption and monetization
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- Alternative: "Weekly active orgs × seats per org × ARPU"
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### Content / media business
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- **Daily active readers / listeners × ad revenue per session** — captures both reach and monetization
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- Alternative: "Subscriber count × retention × ARPU"
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### Commerce (DTC, non-subscription)
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- **Repeat purchase rate × AOV × frequency** — captures monetization layered on quality of customer
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- Alternative: "Customer LTV / CAC × payback period"
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## Leading indicators by AARRR stage
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After the north star, every plan needs leading indicators per AARRR stage. These move faster than the north star and trigger investigations.
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### Acquisition leading indicators
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- Organic visits/month, total + per pillar (SEO health)
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- App Store / Play Store visit-to-install rate (ASO health)
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- Founder-led social channel growth → email subscriber conversion (LinkedIn / X / Substack funnels)
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- Event-to-app conversion rate (event ROI)
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- Ambassador-attributed visits (referral funnel)
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- Paid CAC by channel (when paid is firing)
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### Activation leading indicators
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- Day 1 / Day 7 / Day 35 → paid conversion rate
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- Onboarding session-completion rate
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- First key-action completion (post-signup activation event)
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- App Store conversion rate (install → trial → paid)
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- Trial → paid conversion rate
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### Retention leading indicators
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- Day 30 / Day 60 / Day 90 retention
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- Monthly churn rate (gross + net)
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- Lifecycle email engagement (open / click / unsubscribe by flow)
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- Hardware → app activation rate (for hybrid businesses)
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- Win-back / reactivation rate
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### Referral leading indicators
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- Ambassador-attributed new subs (via Dub or similar)
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- Share-after-value moment rate (% of users sharing)
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- Two-sided referral completion rate
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- Guides program referrals (when live)
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- NPS score (if surveyed)
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### Revenue leading indicators
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- ARPU by cohort
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- Annual plan adoption %
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- Cohort LTV by source
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- Plan mix shifts
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- Eye-mask / hardware attach rate (for hybrid)
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- Expansion revenue (B2B)
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## Review cadence
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The plan should specify three rhythms:
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### Weekly (operational sync)
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- **Who:** fCMO ↔ founder (CEO usually)
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- **Duration:** 30 min
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- **Format:** AARRR scoreboard (current vs. last week numbers across the leading indicators) + this week's ships + blockers
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- **Output:** Action items, decisions made
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### Monthly (metrics review)
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- **Who:** fCMO + founder + extended team (CXO, product lead, designer if applicable)
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- **Duration:** 60–90 min
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- **Format:** Full metrics review + comparison against quarterly KPI targets + qualitative learnings + idea bank reprioritization
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- **Output:** Possible plan adjustments, hire decisions
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### Quarterly (plan recalibration)
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- **Who:** fCMO + founders + key advisors
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- **Duration:** 2–3 hours
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- **Format:** Full plan review against 90-day and 12-month outcomes, channel-level analysis, funding-stage transition check, recalibration of next 90 days
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- **Output:** Updated plan (could be v2 / v3 document iteration)
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## KPI target setting
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For each quarter in Section 10, the plan must include 3–5 specific KPI targets. These should be:
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- **Specific** — not "improve retention," but "Day 30 retention from 22% → 30%"
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- **Measurable** — pull from a wired data source
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- **Stretch but plausible** — based on funnel state + historical patterns
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- **Decision-triggering** — if missed, what does that mean? (Adjust strategy, kill a channel, etc.)
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### KPI target patterns by quarter
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**Q1 (foundation quarter):**
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- Mostly *bedrock* metrics — fixing leaks. "Headphones-gate conversion drop reverses." "Day 1 → paid +25–50%."
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- Some *foundation* metrics — laying tracks. "4 SEO pillars staked." "App Store rewrite shipped."
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- Avoid bold growth targets — the foundations aren't in yet
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**Q2 (validation quarter):**
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- Mostly *validation* metrics — does what we built work? "Paid CAC < $X blended." "Organic traffic 1,500–3,500/mo."
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- Some *cohort* metrics — do new cohorts behave better? "Day 7 retention for Q2 cohort vs. Q1."
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**Q3 (scaling quarter):**
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- Mostly *scaling* metrics — how far does it go? "Paid scaling to $20–30K/mo with CAC steady." "First B2B install reference case live."
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- Some *capability* metrics — what new things are live? "First Guides pilot launched."
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**Q4 (compound quarter):**
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- Mostly *compound* metrics — is the flywheel turning? "50%+ of new subs from non-paid channels." "Ambassador-driven 15–25% of new subs."
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- Some *narrative* metrics — does the Series A story write itself? "Blended LTV/CAC > 3."
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## Kill criteria
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For every channel or initiative, the plan should specify when to stop. Often missing from plans, kill criteria force discipline.
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Examples:
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- "If a paid channel has CAC > 2× target after 30 days at meaningful spend, pause."
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- "If onboarding Variant 3 doesn't show statistically meaningful lift (or directional lift + congruent qualitative signal) after 4 weeks, move to Variant 1."
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- "If lifecycle Flow 4 has open rate < 12% after 6 weeks, redo subject lines + audience segmentation."
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## Guardrail metrics
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Some metrics get a hard guardrail (cannot drop below threshold). Useful for protecting brand or unit economics during aggressive growth.
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Examples:
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- "Brand voice complaint rate > 1% of customer feedback triggers content review."
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- "Paid CAC > $X for two consecutive months pauses paid scaling pending audit."
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- "App Store rating drops below 4.5 triggers product review."
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## Data sources mapping
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The plan should name where each metric comes from. This makes it auditable.
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| Metric | Source |
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| Organic traffic | GA4 / Ahrefs |
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| App Store conversion | App Store Connect |
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| Funnel conversion (Day N → paid) | Internal analytics (Mixpanel / Amplitude) or App Store Connect cohort export |
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| Retention | Customer.io segments + product analytics |
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| MRR / ARR | Stripe (via MCP if wired) |
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| Plan mix | Stripe |
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| Lifecycle email metrics | Customer.io |
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| Ambassador attribution | Dub.co |
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| Hardware → app activation | Shopify + App Store + internal join |
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| NPS | Survey tool (Customer.io / Typeform / SurveyMonkey) |
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## When data isn't wired
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If a metric can't currently be measured, flag it in Section 13's open decisions. Example:
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> "Hardware → app activation rate not currently visible in the App Store dashboard. Requires Shopify ↔ App Store Connect join. Q1 work item."
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A plan with un-measurable goals is a plan that can't be validated. Surface the instrumentation work explicitly.
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## Reporting cadence + automation
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Where possible, auto-generate the metrics review rather than building it manually each time. Stripe MCP + GA4 MCP + Customer.io MCP can pull most of what's needed.
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For Tier 1 clients, a simple weekly metrics email to the team (Markdown table, generated via skills + MCPs) costs nothing and creates discipline.
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For Tier 2+ clients, consider a real dashboard (Hex, Metabase, Looker, or internal tool).
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