industry-benchmarking

What this skill does

# Industry Benchmarking

You perform autonomous industry benchmarking analysis. You research benchmark data yourself — do not ask the user for data they would need to look up. If the user provides their own performance metrics, compare them against researched benchmarks. Only ask the user for decisions and confirmations.

## Input handling

Follow shared foundation §7 — interview mode. When input is missing or insufficient, interview to gather at minimum:

| Dimension | Required | Default |
|---|---|---|
| **Subject** (company, product, or business unit) | Yes | — |
| **Industry/market** | Yes | — |
| **Company size/stage** | No | Inferred from subject |
| **Geographic scope** | No | Global |
| **Domains to benchmark** (financial, operational, etc.) | No | Auto-selected by industry |
| **Own performance metrics** | No | Benchmark standards only |
| **Known peers** | No | Will be researched |

**Exit interview when**: Subject, industry, and size/stage are clear.

## Phase 1 — Setup

### 1. Collect input

Accept one of:
- A company, product, or business unit name/description
- A file path to a business case document
- Pasted business case content with optional own metrics
- No input or vague input → enter interview mode (§7)

### 2. Detect scope

From the input (or interview results), identify:
- **Subject**: The company, product, or business unit
- **Industry/market**: Sector and segment
- **Size/stage**: Revenue band, employee count, maturity (startup/growth/mature)
- **Geographic scope**: Region or global
- **Domains**: Which metric domains to benchmark
- **Own metrics**: Any performance data the user provided

### 3. Confirm scope

Present detected scope:

```
**Subject**: [name]
**Industry**: [industry/segment]
**Size/stage**: [size band / maturity]
**Geographic scope**: [scope]
**Benchmark domains**: [listed or "auto-selected"]
**Own metrics provided**: [yes — listed / no — benchmark standards only]
```

Ask the user to confirm or adjust.

Ask diagram render mode and output path per the `diagram-rendering` and `autonomous-research` mixins.

## Phase 2 — Research

Use WebSearch and WebFetch per the `autonomous-research` mixin.

### 2a. Industry benchmark data

Research:
- Industry reports with performance benchmarks (Gartner, McKinsey, Deloitte, PwC, APQC)
- Public company financials and filings (SEC EDGAR, annual reports)
- Sector-specific benchmark databases (OpenView for SaaS, Damodaran for financials)
- Professional association benchmarks (ASQ, APQC PCF)
- Government statistics (BLS, Census, Eurostat)

### 2b. Peer identification

Identify 8-15 comparable organizations based on:
- **Size**: Revenue band within 0.5x-2x of subject
- **Geography**: Same regulatory/cost environment
- **Business model**: Subscription vs transactional, product vs services
- **Maturity stage**: Startup vs growth vs mature

Present peer group for user confirmation.

### 2c. Data freshness

Flag any data older than 18 months with `[Dated: YYYY]`. Prefer recent sources.

## Phase 3 — Metric Selection

Select 8-12 key metrics based on industry and business model. Use domain-specific metric sets:

### SaaS/Tech
| Metric | Good | Great | Elite | Source |
|---|---|---|---|---|
| ARR Growth | 40%+ | 80%+ | 100%+ | OpenView/KeyBanc |
| Gross Churn (monthly) | <2% | <1% | <0.5% | Industry benchmark |
| CAC:LTV Ratio | >3:1 | >5:1 | >7:1 | Industry benchmark |
| CAC Payback | <18 mo | <12 mo | <6 mo | Industry benchmark |
| Rule of 40 | 40+ | 60+ | 80+ | Bessemer |
| Net Dollar Retention | >100% | >120% | >140% | Industry benchmark |

### Financial (general)
| Metric | What it measures |
|---|---|
| Gross Margin | Production efficiency |
| EBITDA Margin | Operating profitability |
| Net Margin | Bottom-line profitability |
| Revenue per Employee | Labor efficiency |
| ROIC | Capital efficiency |
| Current Ratio | Liquidity |
| Debt-to-Equity | Leverage |

### Operational
| Metric | What it measures |
|---|---|
| Cycle Time | Process speed |
| Defect Rate | Quality |
| Capacity Utilization | Resource efficiency |
| On-time Delivery | Reliability |

### Marketing
| Metric | What it measures |
|---|---|
| Customer Acquisition Cost (CAC) | Acquisition efficiency |
| Lifetime Value (LTV) | Customer value |
| Conversion Rate | Funnel efficiency |
| Cost per Lead | Lead generation efficiency |

### Product
| Metric | What it measures |
|---|---|
| Net Promoter Score (NPS) | Customer satisfaction |
| Churn Rate | Retention |
| Feature Adoption Rate | Product-market fit |
| Time-to-Value | Onboarding efficiency |

### HR
| Metric | What it measures |
|---|---|
| Turnover Rate | Retention |
| Revenue per Employee | Productivity |
| Cost-per-Hire | Recruitment efficiency |
| Engagement Score | Culture health |

### Technology
| Metric | What it measures |
|---|---|
| Uptime / Availability | Reliability |
| Deployment Frequency | Delivery speed |
| MTTR | Incident recovery |
| Change Failure Rate | Delivery quality |

Select domains most relevant to the subject. Document why each metric was chosen.

## Phase 4 — Benchmark Data Collection

For each selected metric, gather:

| Metric | P25 | P50 (median) | P75 | Best-in-class | Source | Year |
|---|---|---|---|---|---|---|
| [metric] | [value] | [value] | [value] | [value] | [source] | [year] |

Where quartile data is unavailable, use available data points and label confidence:
- `[Full distribution]` — P25/P50/P75 available
- `[Median only]` — only median found
- `[Estimated]` — derived from limited data points
- `[Proxy]` — from adjacent industry

## Phase 5 — Gap Analysis

If the user provided their own metrics, calculate gaps:

| Metric | Subject | P50 (median) | Best-in-class | Absolute gap | Relative gap | Percentile |
|---|---|---|---|---|---|---|
| [metric] | [value] | [value] | [value] | [diff] | [%] | [est. Pxx] |

**Absolute gap**: Benchmark - Current value
**Relative gap**: (Benchmark - Current) / Benchmark × 100%
**Percentile**: Estimated position in the distribution

If the user did NOT provide their own metrics, present the benchmark ranges as reference standards with guidance on how to self-assess.

### Severity classification (per assessment extension)

| Percentile | Severity | Meaning |
|---|---|---|
| Below P25 | **Critical** | Significantly underperforming — immediate attention needed |
| P25 to P50 | **Warning** | Below average — improvement opportunity |
| P50 to P75 | **Info** | Average to good — optimize selectively |
| Above P75 | — | Strong performance — maintain and leverage |

## Phase 6 — Composite Scoring

Create a weighted composite score:

### Domain weights

Select weights based on strategic relevance (must sum to 100%):

| Domain | Weight | Rationale |
|---|---|---|
| Financial | [X%] | [why] |
| Operational | [X%] | [why] |
| Marketing | [X%] | [why] |
| Product | [X%] | [why] |
| ... | ... | ... |

### Scoring method

1. For each metric, normalize to 0-100 using min-max: `score = (value - P25) / (P75 - P25) × 100` (capped at 0 and 100)
2. Average scores within each domain
3. Apply domain weights for composite score

Present as scorecard:

| Domain | Score (0-100) | Grade | Key insight |
|---|---|---|---|
| Financial | [score] | [A/B/C/D] | [one-line insight] |
| Operational | [score] | [A/B/C/D] | [one-line insight] |
| **Overall** | **[score]** | **[grade]** | |

Grade mapping: A = 75-100, B = 50-74, C = 25-49, D = 0-24.

## Phase 7 — Maturity Assessment (optional)

If relevant, assess organizational maturity across key dimensions:

| Dimension | Level (1-5) | Description |
|---|---|---|
| [dimension] | [1-5] | [what this level means] |

Maturity levels:
1. **Initial** — Ad hoc, reactive
2. **Developing** — Some structure, inconsistent
3. **Defined** — Standardized processes
4. **Managed** — Measured and controlled
5. **Optimizing** — Continuous improvement, industry-leading

## Phase 8 — Prioritization

Plot gaps on an impact-vs-effort matrix:

| Gap | Impact (1-5) | Effort (1-5) | Category |
|---|--