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startup-metrics-framework — Claude Code skill: metrics and setup

This skill helps choose and calculate startup metrics such as CAC, LTV, burn multiple, retention, and growth. Use it for dashboards and investor reporting.

Install into Claude Code · Plugin marketplace All methods and platforms ↓
/plugin marketplace add wshobson/agents
/plugin install startup-business-analyst@claude-code-workflows
Open on GitHub

What startup-metrics-framework does

This skill provides a metrics framework for SaaS, marketplace, consumer, and B2B startups from seed through Series A. It covers GMV, take rate, DAU/MAU, retention, K-factor, win rate, pipeline coverage, CAC, LTV, payback, burn multiple, and related measures. The recommended set changes with the business model and company stage.

The framework includes formulas, model-specific benchmarks, stage-based priorities, segmentation guidance, and reporting cadence. It names Mixpanel, Amplitude, ChartMogul, Baremetrics, Looker, and Tableau as possible analytics or dashboard tools. A single source of truth, daily or real-time updates, automated calculations, and historical tracking are part of the suggested setup.

Install it when you need a practical metrics system or an investor and board dashboard. It is a poor fit for a one-off assessment with no underlying business data.

What it helps with

  • choose 5-7 core metrics for a startup stage
  • calculate CAC, LTV, payback, and burn multiple
  • measure retention across user cohorts
  • prepare metrics for investor or board reporting
  • benchmark SaaS, marketplace, consumer, or B2B performance

Example prompts for the agent

  • Choose 5-7 core metrics for our Seed-stage SaaS company and define a reporting cadence.
  • Calculate CAC, LTV, payback, and burn multiple from this data: ...
  • Draft a monthly board dashboard with current values, trends, and relevant benchmarks.

How to install startup-metrics-framework

1Plugin marketplace

/plugin marketplace add wshobson/agents
/plugin install startup-business-analyst@claude-code-workflows

This installs the whole startup-business-analyst plugin with all its skills

2Skills CLI (Vercel)

npx skills add wshobson/agents --skill startup-metrics-framework -a claude-code

3Manually: put the files in place

~/.claude/skills/startup-metrics-framework/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/startup-metrics-framework/

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Questions about startup-metrics-framework

How do I install startup-metrics-framework in Claude Code?

Run npx skills add wshobson/agents --skill startup-metrics-framework -a claude-code. The skill will be available at ~/.claude/skills/startup-metrics-framework/SKILL.md.

What does startup-metrics-framework do?

It helps select metrics by business model and startup stage, apply the relevant formulas, and structure dashboards for teams, investors, or board reporting.

Which analytics tools does the skill mention?

It lists Mixpanel and Amplitude for product analytics, ChartMogul and Baremetrics for SaaS metrics, and Looker and Tableau for BI dashboards.

Source text

SKILL.md · MIT
---
name: startup-metrics-framework
description: Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management. Use this skill when defining a metrics framework, calculating CAC/LTV/burn multiple, benchmarking business health, or preparing metrics dashboards for investors or board reporting.
version: 1.0.0
---

# Startup Metrics Framework

Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.

## Overview

Track the right metrics at the right stage. Focus on unit economics, growth efficiency, and cash management metrics that matter for fundraising and operational excellence.

For every startup analysis, first read [universal revenue, unit-economics, and cash metrics](references/universal-and-saas-metrics.md); also apply its SaaS section when relevant.

## Marketplace Metrics

### GMV (Gross Merchandise Value)

**Total Transaction Volume:**

```
GMV = Σ (Transaction Value)
```

**Growth Rate:**

```
GMV Growth Rate = (Current Period GMV - Prior Period GMV) / Prior Period GMV
```

**Target:** 20%+ MoM early-stage

### Take Rate

```
Take Rate = Net Revenue / GMV
```

**Typical Ranges:**

- Payment processors: 2-3%
- E-commerce marketplaces: 10-20%
- Service marketplaces: 15-25%
- High-value B2B: 5-15%

### Marketplace Liquidity

**Time to Transaction**
How long from listing to sale/match?

**Fill Rate**
% of requests that result in transaction

**Repeat Rate**
% of users who transact multiple times

**Benchmarks:**

- Fill rate > 80% = Strong liquidity
- Repeat rate > 60% = Strong retention

### Marketplace Balance

**Supply/Demand Ratio:**
Track relative growth of supply and demand sides.

**Warning Signs:**

- Too much supply: Low fill rates, frustrated suppliers
- Too much demand: Long wait times, frustrated customers

**Goal:** Balanced growth (1:1 ratio ideal, but varies by model)

## Consumer/Mobile Metrics

### Engagement Metrics

**DAU (Daily Active Users)**
Unique users active each day

**MAU (Monthly Active Users)**
Unique users active each month

**DAU/MAU Ratio**

```
DAU/MAU = DAU / MAU
```

**Benchmarks:**

- > 50% = Exceptional (daily habit)
- 20-50% = Good
- < 20% = Weak engagement

**Session Frequency**
Average sessions per user per day/week

**Session Duration**
Average time spent per session

### Retention Curves

**Day 1 Retention:** % users who return next day
**Day 7 Retention:** % users active 7 days after signup
**Day 30 Retention:** % users active 30 days after signup

**Benchmarks (Day 30):**

- > 40% = Excellent
- 25-40% = Good
- < 25% = Weak

**Retention Curve Shape:**

- Flattening curve = good (users becoming habitual)
- Steep decline = poor product-market fit

### Viral Coefficient (K-Factor)

```
K-Factor = Invites per User × Invite Conversion Rate
```

**Example:**
10 invites/user × 20% conversion = 2.0 K-factor

**Benchmarks:**

- K > 1.0 = Viral growth
- K = 0.5-1.0 = Strong referrals
- K < 0.5 = Weak virality

## B2B Metrics

### Sales Efficiency

**Win Rate**

```
Win Rate = Deals Won / Total Opportunities
```

**Target:** 20-30% for new sales team, 30-40% mature

**Sales Cycle Length**
Average days from opportunity to close

**Shorter is better:**

- SMB: 30-60 days
- Mid-market: 60-120 days
- Enterprise: 120-270 days

**Average Contract Value (ACV)**

```
ACV = Total Contract Value / Contract Length (years)
```

### Pipeline Metrics

**Pipeline Coverage**

```
Pipeline Coverage = Total Pipeline Value / Quota
```

**Target:** 3-5x coverage (3-5x pipeline needed to hit quota)

**Conversion Rates by Stage:**

- Lead → Opportunity: 10-20%
- Opportunity → Demo: 50-70%
- Demo → Proposal: 30-50%
- Proposal → Close: 20-40%

## Metrics by Stage

### Pre-Seed (Product-Market Fit)

**Focus Metrics:**

1. Active users growth
2. User retention (Day 7, Day 30)
3. Core engagement (sessions, features used)
4. Qualitative feedback (NPS, interviews)

**Don't worry about:**

- Revenue (may be zero)
- CAC (not optimizing yet)
- Unit economics

### Seed ($500K-$2M ARR)

**Focus Metrics:**

1. MRR growth rate (15-20% MoM)
2. CAC and LTV (establish baseline)
3. Gross retention (> 85%)
4. Core product engagement

**Start tracking:**

- Sales efficiency
- Burn rate and runway

### Series A ($2M-$10M ARR)

**Focus Metrics:**

1. ARR growth (3-5x YoY)
2. Unit economics (LTV:CAC > 3, payback < 18 months)
3. Net dollar retention (> 100%)
4. Burn multiple (< 2.0)
5. Magic number (> 0.5)

**Mature tracking:**

- Rule of 40
- Sales efficiency
- Pipeline coverage

## Metric Tracking Best Practices

### Data Infrastructure

**Requirements:**

- Single source of truth (analytics platform)
- Real-time or daily updates
- Automated calculations
- Historical tracking

**Tools:**

- Mixpanel, Amplitude (product analytics)
- ChartMogul, Baremetrics (SaaS metrics)
- Looker, Tableau (BI dashboards)

### Reporting Cadence

**Daily:**

- MRR, active users
- Sign-ups, conversions

**Weekly:**

- Growth rates
- Retention cohorts
- Sales pipeline

**Monthly:**

- Full metric suite
- Board reporting
- Investor updates

**Quarterly:**

- Trend analysis
- Benchmarking
- Strategy review

### Common Mistakes

**Mistake 1: Vanity Metrics**
Don't focus on:

- Total users (without retention)
- Page views (without engagement)
- Downloads (without activation)

Focus on actionable metrics tied to value.

**Mistake 2: Too Many Metrics**
Track 5-7 core metrics intensely, not 50 loosely.

**Mistake 3: Ignoring Unit Economics**
CAC and LTV are critical even at seed stage.

**Mistake 4: Not Segmenting**
Break down metrics by customer segment, channel, cohort.

**Mistake 5: Gaming Metrics**
Optimize for real business outcomes, not dashboard numbers.

## Investor Metrics

### What VCs Want to See

**Seed Round:**

- MRR growth rate
- User retention
- Early unit economics
- Product engagement

**Series A:**

- ARR and growth rate
- CAC payback < 18 months
- LTV:CAC > 3.0
- Net dollar retention > 100%
- Burn multiple < 2.0

**Series B+:**

- Rule of 40 > 40%
- Efficient growth (magic number)
- Path to profitability
- Market leadership metrics

### Metric Presentation

**Dashboard Format:**

```
Current MRR: $250K (↑ 18% MoM)
ARR: $3.0M (↑ 280% YoY)
CAC: $1,200 | LTV: $4,800 | LTV:CAC = 4.0x
NDR: 112% | Logo Retention: 92%
Burn: $180K/mo | Runway: 18 months
```

**Include:**

- Current value
- Growth rate or trend
- Context (target, benchmark)


## Quick Start

To implement startup metrics framework:

1. **Identify business model** - SaaS, marketplace, consumer, B2B
2. **Choose 5-7 core metrics** - Based on stage and model
3. **Establish tracking** - Set up analytics and dashboards
4. **Calculate unit economics** - CAC, LTV, payback
5. **Set targets** - Use benchmarks for goals
6. **Review regularly** - Weekly for core metrics
7. **Share with team** - Align on goals and progress
8. **Update investors** - Monthly/quarterly reporting

© Seth Hobson · MIT · wshobson/agents

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