marketing-plan · measurement-framework

Measurement Framework — KPIs, North Stars, Cadence

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.

Related docs:

• growth-patterns.md — the 3-3-2-2-2 VC growth path (3× in years 1–2, 2× in years 3–7 from $1M ARR) and which phase of SaaS growth the company is in ($0–10K / $10K–100K / $100K–1M+)
• budget-planning.md — CAC calculation (blended, not paid-only) and the forecasting reality check (forecasts under $100M ARR are educated guesses, not precise predictions)

The north-star principle

A north star is one metric that captures the business-model thesis at the highest level. It should:

• Be derivable from the funnel + revenue model
• Move slowly enough to be a strategic compass (not whipsawed by weekly noise)
• Trade off correctly against other metrics — improving the north star should generally improve the business

Don't default to "ARR" or "MRR" alone. Those are outcomes, not norths. Pick something that captures the business model.

North-star patterns by business model

B2B SaaS (subscription)

• Net Revenue Retention (NRR) — keeps existing customers + expansion in focus
• Alternative: "Logo retention × expansion ARR"
• Why: ARR alone hides churn / lets gross-add growth mask product fit problems

D2C consumer app (subscription)

• Blended LTV / blended CAC — keeps unit economics honest as paid layer scales
• Alternative: "Day-35 paid users from cohort × LTV"
• Why: monthly subscription metrics are volatile; cohort × LTV smooths it

Hybrid hardware + software (e.g., Quietude)

• Blended LTV / blended CAC across hardware + software — captures the wedge thesis
• Alternative: "Hardware-buyers-to-subscriber conversion × blended margin"
• Why: hardware revenue isn't free (cost to make); subscription revenue isn't expensive to acquire if hardware funds it

Marketplace (two-sided)

• Liquidity ratio × take-rate — captures both sides + monetization
• Alternative: "Monthly transacting users × take-rate × repeat frequency"
• Why: GMV alone doesn't capture whether the marketplace is becoming a habit

Developer tool / open source

• Weekly active developers × paid-conversion — captures both adoption and monetization
• Alternative: "Weekly active orgs × seats per org × ARPU"

Content / media business

• Daily active readers / listeners × ad revenue per session — captures both reach and monetization
• Alternative: "Subscriber count × retention × ARPU"

Commerce (DTC, non-subscription)

• Repeat purchase rate × AOV × frequency — captures monetization layered on quality of customer
• Alternative: "Customer LTV / CAC × payback period"

Leading indicators by AARRR stage

After the north star, every plan needs leading indicators per AARRR stage. These move faster than the north star and trigger investigations.

Acquisition leading indicators

• Organic visits/month, total + per pillar (SEO health)
• App Store / Play Store visit-to-install rate (ASO health)
• Founder-led social channel growth → email subscriber conversion (LinkedIn / X / Substack funnels)
• Event-to-app conversion rate (event ROI)
• Ambassador-attributed visits (referral funnel)
• Paid CAC by channel (when paid is firing)

Activation leading indicators

• Day 1 / Day 7 / Day 35 → paid conversion rate
• Onboarding session-completion rate
• First key-action completion (post-signup activation event)
• App Store conversion rate (install → trial → paid)
• Trial → paid conversion rate

Retention leading indicators

• Day 30 / Day 60 / Day 90 retention
• Monthly churn rate (gross + net)
• Lifecycle email engagement (open / click / unsubscribe by flow)
• Hardware → app activation rate (for hybrid businesses)
• Win-back / reactivation rate

Referral leading indicators

• Ambassador-attributed new subs (via Dub or similar)
• Share-after-value moment rate (% of users sharing)
• Two-sided referral completion rate
• Guides program referrals (when live)
• NPS score (if surveyed)

Revenue leading indicators

• ARPU by cohort
• Annual plan adoption %
• Cohort LTV by source
• Plan mix shifts
• Eye-mask / hardware attach rate (for hybrid)
• Expansion revenue (B2B)

Review cadence

The plan should specify three rhythms:

Weekly (operational sync)

• Who: fCMO ↔ founder (CEO usually)
• Duration: 30 min
• Format: AARRR scoreboard (current vs. last week numbers across the leading indicators) + this week's ships + blockers
• Output: Action items, decisions made

Monthly (metrics review)

• Who: fCMO + founder + extended team (CXO, product lead, designer if applicable)
• Duration: 60–90 min
• Format: Full metrics review + comparison against quarterly KPI targets + qualitative learnings + idea bank reprioritization
• Output: Possible plan adjustments, hire decisions

Quarterly (plan recalibration)

• Who: fCMO + founders + key advisors
• Duration: 2–3 hours
• Format: Full plan review against 90-day and 12-month outcomes, channel-level analysis, funding-stage transition check, recalibration of next 90 days
• Output: Updated plan (could be v2 / v3 document iteration)

KPI target setting

For each quarter in Section 10, the plan must include 3–5 specific KPI targets. These should be:

• Specific — not "improve retention," but "Day 30 retention from 22% → 30%"
• Measurable — pull from a wired data source
• Stretch but plausible — based on funnel state + historical patterns
• Decision-triggering — if missed, what does that mean? (Adjust strategy, kill a channel, etc.)

KPI target patterns by quarter

Q1 (foundation quarter):

• Mostly bedrock metrics — fixing leaks. "Headphones-gate conversion drop reverses." "Day 1 → paid +25–50%."
• Some foundation metrics — laying tracks. "4 SEO pillars staked." "App Store rewrite shipped."
• Avoid bold growth targets — the foundations aren't in yet

Q2 (validation quarter):

• Mostly validation metrics — does what we built work? "Paid CAC < $X blended." "Organic traffic 1,500–3,500/mo."
• Some cohort metrics — do new cohorts behave better? "Day 7 retention for Q2 cohort vs. Q1."

Q3 (scaling quarter):

• Mostly scaling metrics — how far does it go? "Paid scaling to $20–30K/mo with CAC steady." "First B2B install reference case live."
• Some capability metrics — what new things are live? "First Guides pilot launched."

Q4 (compound quarter):

• Mostly compound metrics — is the flywheel turning? "50%+ of new subs from non-paid channels." "Ambassador-driven 15–25% of new subs."
• Some narrative metrics — does the Series A story write itself? "Blended LTV/CAC > 3."

Anchoring against the VC growth path

For VC-backed clients past $1M ARR, anchor 12-month and multi-year targets against the 3-3-2-2-2 rule (3× in years 1 and 2, then 2× in years 3 through 7). Hitting it is rare; most companies don't. Anchoring against it forces the plan to either match it and show how, or explicitly defend choosing a slower trajectory. Full table and context in growth-patterns.md.

For non-VC-backed companies (bootstrapped, founder-funded, profit-focused), the 3-3-2-2-2 doesn't apply. Use linear-pattern targets ("$X MRR added per month") or step-function targets ("$Y revenue jump after the enterprise tier launches") instead.

Forecasting reality check

A plan derives a budget and an annual goal. It does not produce a 12-month month-by-month forecast that's reliably accurate to the dollar.

Unless the company is publicly traded, all forecasts are educated guesses. No startup under $100M ARR consistently hits month-by-month forecasts. Quarterly review is when the plan adjusts — not when variance is treated as failure.

What the plan commits to honestly:

• The annual goal is a defensible direction-of-travel
• The budget is the resource commitment that makes the goal plausible
• The 90-day roadmap (Section 9) is what's actionable now
• Month-to-month projection is illustrative, not promised

Founders who over-engineer the forecast end up explaining variance every month instead of executing. The plan should resist this — name the annual target, the quarterly KPIs, and the kill criteria. Don't promise the month.

Full context in budget-planning.md.

Kill criteria

For every channel or initiative, the plan should specify when to stop. Often missing from plans, kill criteria force discipline.

Examples:

• "If a paid channel has CAC > 2× target after 30 days at meaningful spend, pause."
• "If onboarding Variant 3 doesn't show statistically meaningful lift (or directional lift + congruent qualitative signal) after 4 weeks, move to Variant 1."
• "If lifecycle Flow 4 has open rate < 12% after 6 weeks, redo subject lines + audience segmentation."

Guardrail metrics

Some metrics get a hard guardrail (cannot drop below threshold). Useful for protecting brand or unit economics during aggressive growth.

Examples:

• "Brand voice complaint rate > 1% of customer feedback triggers content review."
• "Paid CAC > $X for two consecutive months pauses paid scaling pending audit."
• "App Store rating drops below 4.5 triggers product review."

Data sources mapping

The plan should name where each metric comes from. This makes it auditable.

When data isn't wired

If a metric can't currently be measured, flag it in Section 13's open decisions. Example:

"Hardware → app activation rate not currently visible in the App Store dashboard. Requires Shopify ↔ App Store Connect join. Q1 work item."

A plan with un-measurable goals is a plan that can't be validated. Surface the instrumentation work explicitly.

Reporting cadence + automation

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.

For Tier 1 clients, a simple weekly metrics email to the team (Markdown table, generated via skills + MCPs) costs nothing and creates discipline.

For Tier 2+ clients, consider a real dashboard (Hex, Metabase, Looker, or internal tool).

Source: https://github.com/coreyhaines31/marketingskills/skills/marketing-plan/references/measurement-framework.md · v1.1.1 · MIT License (c) 2025 Corey Haines.