acquisition-metrics-plan

name	acquisition-metrics-plan
description	Design a complete acquisition metrics plan for any business type. Classifies the business into archetypes (SaaS/App, Fintech, Marketplace, E-commerce/D2C, Lead-to-Sale, Hybrid), defines activation events, builds 4-level metrics hierarchy, sets benchmarks, creates review cadence, and generates a personalized Excel tracking template. Reads company-context, budget, ecps from Context Lake. Writes metrics-plan.md to brand/. Use when onboarding a new client, launching a product, restructuring metrics tracking, or user says design metrics plan, what should we measure, acquisition KPIs, metrics setup, tracking plan. Do NOT use for diagnosing existing metrics (use diagnose) or for designing experiments (use design-experiment).
metadata	{"author":"Alfonso Sainz de Baranda (Growth4U)","version":"1.0","system":"SanchoCMO","phase":"Execute","pillar":"acquisition-metrics-plan","layer":"Execute","depends_on":"company-context","chains_to":"channel-prioritization, diagnose"}

Acquisition Metrics Plan — Design Your Measurement System

"If you can't measure it, you can't improve it. But measuring the WRONG thing is worse than not measuring at all."

This skill designs the measurement system for any business: what to measure, how, with what benchmarks, and at what cadence. It sits between Foundation (understanding the business) and Execution (running campaigns). Without the right metrics framework, channel-prioritization scores blindly and diagnose has no data to work with.

Read ./brand/ per _system/intelligence/brand-memory.md

Follow _system/output/output-format.md

---

Prerequisites

Required (will not run without these):

• ./brand/company-context.md -- Industry, stage, business model, monetization (from company-context)

Recommended (better output with these):

• ./brand/budget.md -- Budget range, team capacity (from budget-constraints)
• ./brand/ecps.md -- Target personas (from niche-discovery-100x)
• ./brand/stack.md -- Available tools and integrations (from sancho-start)
• ./brand/channel-plan.md -- Selected channels (from channel-prioritization)

---

Core Principle

The activation event is NEVER the signup. It is the moment where the user sees real value. All cohorts start here.

---

Workflow: 7 Steps

Step 1: Classify the Business (~2 min)

Load ./brand/company-context.md. Extract monetization model, customer journey, regulatory requirements. If company-context exists, INFER the archetype -- do not ask unless ambiguous.

If company-context is missing, ask these questions then map to archetype:

1. How does it monetize? (subscription / transaction / one-time / ad-supported / service fee)
2. How do customers arrive? (self-serve / sales-assisted / marketplace / local search / referral)
3. Is there regulatory onboarding? (KYC, verification, approval)
4. Is it two-sided? (supply + demand)
5. Digital or physical product?
6. Local/geographic business? (defined catchment area)

Archetype	Examples	Signals
SaaS / App	Slack, Notion, mobile games	Self-serve signup, product-led, subscription/freemium
Fintech	Monzo, Revolut, Example	KYC/regulatory onboarding, transactions, deposits
Marketplace	Airbnb, Wallapop, FellowFunders	Two-sided, platform takes cut
E-commerce / D2C	Shopify stores, Nike.com	Product catalog, cart, purchase, shipping
Lead-to-Sale	Agencies, law firms, clinics, SaaS Enterprise	Leads -> qualification -> meeting/appointment -> deal
Hybrid	Crypto exchanges, fintech marketplaces	Combination of 2+ archetypes

Lead-to-Sale sub-variants (same metrics structure, different channels and naming):

• B2B Services: LinkedIn, cold email, networking. Funnel: Lead -> Meeting -> Proposal -> Deal
• SaaS Enterprise: LinkedIn Ads, SDR outbound, events. Funnel: MQL -> SQL -> Demo -> Proposal -> Won
• Local Services: Google Business Profile, local SEO, directories (Doctoralia, etc). Funnel: Search -> Call/Form -> Appointment -> Visit -> Treatment/Service
• Lead Gen: Paid ads, SEO, content. Funnel: Visit -> Form Fill -> MQL -> SQL -> Appointment

Present classification: "Based on company-context, I classify this as [Archetype]. Validate or correct."

---

Step 2: Define the Activation Event (~1 min)

Archetype	Activation Event	Why
SaaS / App	First core feature used	Predicts retention
Fintech	First transaction (deposit or spend)	No money = no visible value
Marketplace	First completed transaction (buy OR sell)	Proves platform utility
E-commerce / D2C	First purchase	Product-market fit
Lead-to-Sale	First qualified meeting/appointment/demo	Prospect experiences value

For Hybrid: use the primary archetype's activation event, add secondary archetype steps to the funnel.

---

Step 3: Build the Metrics Hierarchy (~3 min)

Level 1 - Primary Acquisition KPI (one metric, the one that rules):

• = Activation Event count (e.g., First Transactions, SQLs, First Purchases)

Level 2 - Quality KPIs (2-3 metrics):

• Activation Rate = Primary KPI / Signups (or equivalent top-of-funnel)
• CAC = Total Spend / Primary KPI
• Business-specific value metric (Amount Deposited, AOV, Deal Size, Treatment Value)

Level 3 - Funnel Steps (diagnostic, to find bottlenecks):

Archetype	Funnel
SaaS / App	Visit/Install -> Signup -> Onboarding -> Core Feature Used -> Paid/D7 Return
Fintech	Download -> Signup -> KYC -> First Deposit -> First Transaction
Marketplace	Visit -> Signup -> First Listing/Search -> First Transaction
E-commerce / D2C	Visit -> Add to Cart -> Checkout Started -> Purchase
Lead-to-Sale	Contact -> Lead -> Qualified Lead -> Meeting/Cita/Demo -> Proposal -> Deal

Level 4 - Return/Sustainability KPIs (validate quality):

• LTV/CAC Ratio (target: >3x, enterprise >5x)
• ROAS per channel (7d, 30d, 90d windows)
• Payback Period = CAC / monthly ARPU
• Cohort retention (transaction-based, NOT activity-based)

---

Step 4: Define Channel Tracking (~2 min)

Read ./brand/channel-plan.md if it exists -- use the selected channels. If not, use standard channel groups:

Standard channel groups:

• Affiliates (partners, influencers, ranking sites)
• Paid SRN (Meta, Google, TikTok, Apple Search, Snapchat)
• Adnetworks (programmatic)
• Organic (SEO, direct)
• Brand (web, blog, social organic)
• Referral (referral program)
• Offline (events)

Lead-to-Sale channel additions by sub-variant:

• Local: Google Business Profile, Local SEO, Local Ads (geo-targeted), Directories, Word of Mouth
• Enterprise: LinkedIn Ads, SDR Outbound, Partnerships, Conferences
• B2B Services: LinkedIn organic, Cold email, Networking, Referral partners

Per channel, track: Fixed Payment + VAT, Variable Fee per activation, Variable Total, Total Spend, each funnel step count, CAC, Activation Rate, Value/User, estimated ARPU.

---

Step 5: Map Data Sources (~3 min)

Read ./brand/stack.md if it exists -- adapt sources to client's actual tools.

For each metric in the hierarchy, identify WHERE the data comes from and HOW to collect it.

Common data sources by metric type:

Metric Category	Source	Collection Method
Web traffic (sessions, users, bounce)	Google Analytics 4, PostHog	API (daily cron)
Product usage (events, funnels, retention)	PostHog, Amplitude	API (daily cron)
SEO (keywords, CTR, positions)	Google Search Console	API (daily cron)
Paid ads (spend, clicks, CPA, ROAS)	Google Ads, Meta Ads Manager	API (daily cron)
Social media (posts, engagement, reach)	Metricool	API (daily cron)
Outbound (emails sent, opens, replies)	Instantly, Lemlist	API (daily cron)
CRM / Pipeline (leads, meetings, deals)	HubSpot, Pipedrive, Notion	API or manual
Revenue, payments, churn	Stripe, billing system	API or manual
Manual metrics (not in any tool)	Google Sheets	Manual entry by client/team

Source mapping by archetype (defaults):

Archetype	Typical Sources
SaaS / App	PostHog/Amplitude (product), GA4 (web), Stripe (revenue), Google Ads + Meta (paid)
Fintech	Internal DB (transactions/KYC), GA4 (web), Google Ads + Meta + TikTok (paid)
Marketplace	Internal DB (transactions), GA4 (web), PostHog (product), paid platforms
E-commerce / D2C	Shopify/WooCommerce (orders), GA4 (web), Meta + Google (paid), Metricool (social)
Lead-to-Sale	CRM (pipeline), GA4 (web), Instantly (outbound), Metricool (social), Google Ads (paid)

For each metric, document:

• Metric name
• Source tool
• Collection method: api-auto (cron), api-manual (on-demand), manual (human entry)
• Frequency: daily, weekly, monthly
• Owner: who is responsible for the data being accurate

Storage: metric_snapshots DB as the runtime source of truth, exposed through /api/metrics?slug={slug}. Google Sheets can be used as a client-facing export/view, not as the canonical store.

---

Step 5.5: Credential Collection & Connection Setup (~5-15 min)

Purpose: For each data source marked api-auto in Step 5, collect the client's credentials and verify the connection works.

Reference files:

• skills/acquisition-metrics-plan/schemas/api-catalog.json — Master catalog: what to ask per source
• skills/acquisition-metrics-plan/schemas/integrations-schema.json — Storage schema

Flow:

1. Check existing integrations: Read ./brand/integrations.json if it exists. Skip sources already connected.

2. Present the list: Show the client which integrations are needed, grouped by priority:

   • 🔴 Critical (blocks metrics): Primary KPI source, web analytics
   • 🟡 Important (blocks channels): Paid ads platforms, CRM
   • 🟢 Nice to have: Social, outbound tools

3. Collect credentials one by one: For each source, use the api-catalog.json to:

   • Tell the client exactly what to provide (key name, where to find it, what permissions)
   • Accept the credential in the thread
   • Store secrets in ./brand/.env with naming: {SLUG_UPPER}_{SOURCE}_{KEY} (e.g. EXAMPLE_GA4_SA_KEY)
   • Store non-sensitive config in ./brand/integrations.json

4. Test each connection: After receiving credentials:

   node skills/acquisition-metrics-plan/scripts/test-connection.js --slug {slug} --source {source}
   

   • ✅ Connected → update status, move to next
   • ❌ Error → show error to client, ask to verify and retry

5. Generate integrations.json: Create/update ./brand/integrations.json with all sources, their status, and env var references.

6. Summary: Present final status table:

   📊 Integration Status for [Client]:
   ✅ GA4 — Connected (Property: 123456789)
   ✅ GSC — Connected (Site: https://example.com)
   ❌ Meta Ads — Error: Invalid token
   ⚫ Metricool — Not configured (client doesn't use)
   

Three-level API resolution (for system-level APIs like LLMs):

Client has own key? → Use client's key
No client key? → Fallback to system key (Growth4U)
No system key? → Mark "not_configured"

Security rules:

• NEVER log or display full API keys/tokens in chat — show only last 4 chars
• Secrets go in .env files ONLY, never in JSON, never in markdown
• .env files must be gitignored

If client doesn't have credentials ready: Mark as pending, set a note with what's needed, and continue with other sources. The skill can run with partial data — just flag which metrics won't populate.

---

Step 6: Set Benchmarks & Decision Criteria (~2 min)

Choose ONE quick decision framework:

1. Theoretical CAC: From business plan or competitor benchmarks. Day 1 ready.
2. CAC + Transaction Cohort: Take CAC of channels with good cohorts as benchmark. Needs ~3 months data.
3. Theoretical Payback Period: Estimate ARPU -> desired payback -> max CAC. Aligns acquisition with sustainability.

Default benchmarks by archetype:

Archetype	Activation Rate	CAC Payback	LTV/CAC
SaaS / App	15-30%	6-12 months	>3x
Fintech	20-25%	3-6 months	>4x
Marketplace	10-20%	Variable	>3x
E-commerce / D2C	2-5% (visit->purchase)	Immediate-3m	>3x
Lead-to-Sale	10-25% (lead->meeting)	1-6 months	>3x (SMB), >5x (enterprise)

---

Step 7: Define Review Cadence (~1 min)

Weekly:

• Primary KPI (activations) total and by channel
• Activation Rate by channel -- kill bad channels, double down on good ones
• Funnel step-by-step conversion rates (diagnostic)

Monthly:

• CAC per activation by channel
• ARPU global and per channel
• Step-by-step activation rate (signup->KYC->transaction, etc.)
• Share of Search / Share of Voice (awareness leading indicator)

Quarterly:

• Cohort analysis (transactional retention, not activity-based)
• LTV/CAC ratio updated with real data
• Payback period actual vs theoretical
• ROAS per channel at 90 days
• Recalibrate benchmarks and decision criteria

---

Step 7.5: Generate metrics-plan.json (~1 min)

Run the plan generator to create the machine-readable metrics plan that the dashboard reads:

node skills/acquisition-metrics-plan/scripts/generate-plan.js \
  --slug "{client-slug}" \
  --archetype "{archetype}" \
  [--sub-variant "{variant}"]

This reads brand/{slug}/integrations.json and schemas/integration-mappings.json to:

1. Map each funnel step to a connected integration (or mark as manual)
2. Define KPIs with formulas that reference specific integrations
3. Identify missing integrations the client should connect
4. Generate brand/{slug}/metrics-plan.json

The dashboard in Mission Control reads this file to:

• Show the funnel with real numbers from connected APIs
• Calculate cross-source KPIs (CPL = ad spend / new contacts)
• Show which metrics are automated vs manual
• Flag missing integrations with setup links

If integrations change later (client connects a new source), re-run this step to update the plan.

---

Step 8: Integration Discovery Flow

When running Steps 5-5.5 (Map Data Sources + Credential Collection), use the integration discovery flow:

For each metric category in the plan, ask the client what tool they use:

"Para medir [categoría] necesito una fuente de datos. ¿Qué usas?"

• Web traffic: "¿Tienes Google Analytics configurado?" → Si sí → pedir Property ID → conectar
• Leads/CRM: "¿Dónde guardas tus leads? ¿HubSpot, GoHighLevel, Pipedrive, Sheets?" → conectar la que use
• Ads: "¿Usas ads? ¿Meta, Google, LinkedIn?" → conectar las que use
• Social: "¿Gestionas redes con algún tool? ¿Metricool, Buffer?" → conectar
• SEO: "¿Tienes Search Console verificado?" → conectar
• Email/Outreach: "¿Usas cold email? ¿Instantly, Lemlist?" → conectar

For each tool they mention:

1. Check schemas/api-catalog.json for supported integrations
2. If supported → send MC link to connect: [MC connect link]
3. If not supported → mark as manual and note for future development
4. If they don't have the tool → recommend one based on archetype + budget

After all integrations are collected → run Step 7.5 to generate the plan.

Available integration mappings (see schemas/integration-mappings.json):

• Web: GA4, PostHog
• CRM: GoHighLevel, HubSpot
• Ads: Meta Ads, Google Ads, LinkedIn Ads
• Social: Metricool
• Outreach: Instantly
• E-commerce: Shopify
• Payments: Stripe
• SEO: Google Search Console
• Manual: Google Sheets

---

Output

Generate THREE deliverables:

1. Metrics Plan Document

Save to ./brand/metrics-plan.md:

# Acquisition Metrics Plan: [Business Name]

## Last Updated
[date] by /acquisition-metrics-plan

**Archetype**: [Primary] (+ [Secondary] if Hybrid)

## Business Profile
- **Monetization**: [model]
- **Customer journey**: [how they arrive]
- **Activation Event**: [specific event]

## Metrics Dashboard

### Level 1 - Primary KPI
- **[Metric name]**: [definition and formula]

### Level 2 - Quality KPIs
- **Activation Rate**: [formula] | Benchmark: [X%]
- **CAC**: [formula] | Benchmark: [EUR X]
- **[Value metric]**: [formula]

### Level 3 - Funnel Steps
[Step] -> [Step] -> [Step] -> [Activation Event]
Track conversion rate between each step.

### Level 4 - Return KPIs
- LTV/CAC: target [Xx]
- Payback Period: target [X months]
- ROAS: measure at [7d/30d/90d]

## Channel Tracking
| Channel Group | Channels | Expected Role |
|....|....|....|

## Data Sources
| Metric | Source | Method | Frequency | Owner |
|--------|--------|--------|-----------|-------|
| [Primary KPI] | [tool] | [api-auto/manual] | [daily/weekly] | [who] |
| [Activation Rate] | Calculated | auto | weekly | system |
| [CAC] | Calculated | auto | monthly | system |
| ... | ... | ... | ... | ... |

## Decision Criteria
- **Framework**: [chosen framework]
- **Max CAC**: EUR [X] per [activation event]
- **Min Activation Rate**: [X%]
- **Action**: Kill channels below [threshold] after [timeframe]

## Review Cadence
- Weekly: [what to review]
- Monthly: [what to review]
- Quarterly: [what to review]

## Cohort Design
- **Start event**: [activation event]
- **Cohort types**: [transaction/revenue/balance as applicable]
- **Dimensions**: by channel, by product (if applicable)

2. Machine-Readable Plan (metrics-plan.json)

Generated by generate-plan.js (Step 7.5). Contains:

• Archetype, activation event, primary KPI
• Funnel steps with source mappings (which API provides each number)
• KPI definitions with formulas (cross-source calculations like CPL)
• Connected integration modules
• Missing integrations the client should connect

This file is read by the Mission Control dashboard to render the metrics page.

Save to: ./brand/metrics-plan.json

3. Excel Tracking Template

Run the generator script to create a personalized .xlsx:

node skills/acquisition-metrics-plan/scripts/generate-template.js \
  --name "Business Name" \
  --archetype "fintech" \
  --output "./brand/"

The script generates a .xlsx with sheets adapted to the archetype (Dashboard, Total Expense, Data Sources, Attribution, Cohorts). See scripts/generate-template.js for parameters and customization.

---

Context Lake Integration

Action	File	Description
READ	./brand/company-context.md	Industry, stage, business model, monetization
READ	./brand/budget.md	Budget range, team capacity
READ	./brand/ecps.md	Target personas
READ	./brand/stack.md	Available tools and integrations
READ	./brand/channel-plan.md	Selected channels (from channel-prioritization)
WRITE (owns)	./brand/metrics-plan.md	Metrics hierarchy + benchmarks + cadence (human-readable)
WRITE (owns)	./brand/metrics-plan.json	Machine-readable plan for dashboard (generated by generate-plan.js)
WRITE (owns)	./brand/integrations.json	API integration status + config (no secrets)
WRITE (owns)	./brand/.env	API secrets (gitignored, never committed)
READ	skills/.../schemas/api-catalog.json	Master catalog of supported APIs
READ	skills/.../schemas/integration-mappings.json	How integrations map to metrics
APPEND	./brand/assets.md	Metrics plan summary

---

Frequency

• Initial: After company-context complete (minimum). Better after Foundation complete.
• Quarterly: Re-evaluate benchmarks with real data
• Ad-hoc: When business model changes, new product launches, or archetype shifts

---

Feedback Collection

After generating the metrics plan, ask:

"El plan de metricas refleja bien tu negocio? Hay alguna metrica clave que falte o alguna fuente de datos que no haya considerado?"

Log feedback to ./brand/learnings.md:

[date] acquisition-metrics-plan: [feedback summary]