# /pricing-analysis — Pricing Strategy Analysis

## Usage
```
/pricing-analysis            # Full pricing analysis (default)
/pricing-analysis benchmark  # Just benchmark against competitors
/pricing-analysis optimize   # Suggest optimizations for current pricing
```

## Input
- `$ARGUMENTS` — optional: "benchmark" or "optimize"

## Process

### Step 1: Load Context
1. Read `data/competitors.json` — all competitor pricing data
2. Read `data/business-context.json` — our current pricing and monetization
3. Read `data/positioning.json` — our target audience and differentiators
4. Read `data/hypotheses.json` — existing pricing hypotheses

### Step 2: Gather Pricing Data
If competitor pricing data is incomplete, run WebSearch:
- `"[product category] pricing [year]"`
- `"how much does [similar product] cost"`
- `"[product category] pricing benchmarks SaaS"`
- For each competitor missing pricing: `"[competitor name] pricing"`

### Step 3: Value Metric Analysis
Identify what customers pay for in this market:
- Per seat/user?
- Per usage (API calls, messages, etc.)?
- Per feature tier?
- Flat rate?

Map value metrics across all competitors:

| Competitor | Model | Entry Price | Mid Tier | Enterprise | Free Tier |
|-----------|-------|------------|----------|------------|-----------|
| ... | per seat | $X/mo | $Y/mo | Custom | Yes/No |

### Step 4: Pricing Gap Analysis
Identify:
1. **Underpriced features** — things we offer that competitors charge more for
2. **Missing tiers** — market segments we're not addressing
3. **Anchoring opportunities** — how to frame our pricing against competitors
4. **Value perception** — is our pricing aligned with our positioning?

### Step 5: Pricing Psychology
Apply pricing psychology principles:
- **Anchoring**: should we add a decoy tier?
- **Charm pricing**: $99 vs $100 effects
- **Tier naming**: does our naming match our audience?
- **Feature gating**: what features should gate each tier?
- **Annual vs monthly**: optimal discount for annual billing

If needed, WebSearch for:
- `"SaaS pricing psychology best practices [year]"`
- `"pricing page conversion optimization"`

### Step 6: Generate Recommendations
Create specific pricing recommendations:

1. **Quick wins** — changes we can make immediately
2. **Tier restructuring** — if current tiers are suboptimal
3. **New pricing page** — if the pricing page needs redesign
4. **A/B test hypotheses** — experiments to validate pricing changes

### Step 7: Create Hypothesis
If pricing changes are recommended, create a hypothesis in `data/hypotheses.json`:
```json
{
  "title": "Pricing change: [specific change]",
  "statement": "If we [change], then [metric] will [improve] because [reason]",
  "funnel_stage": "monetization",
  "priority": "high",
  "effort_to_test": "s",
  "tags": ["pricing"]
}
```

### Step 8: Output
Print:
- Competitive pricing comparison table
- Value metric analysis
- Current pricing assessment (underpriced / overpriced / well-positioned)
- Top 3 pricing recommendations with rationale
- Expected revenue impact
- A/B test hypothesis (if generated)

## Output Files
- `data/hypotheses.json` — pricing test hypotheses (if applicable)