performance-report

# Performance Report

Generate weekly or monthly affiliate performance reports — earnings, clicks, conversions, EPC, top performers, underperformers, and trend analysis. Output is a Markdown report with KPI dashboard, program rankings, and actionable recommendations.

## Stage

S6: Analytics — Data without analysis is just noise. This skill transforms raw affiliate numbers into insights — which programs are worth your time, which are dragging your portfolio down, and where to focus next. Professional affiliates review performance weekly.

## When to Use

- User wants to review their affiliate earnings for a period
- User asks "how are my programs doing?" or "show me my affiliate report"
- User has click/conversion/revenue data and wants analysis
- User wants to compare performance across multiple programs
- User says "weekly report", "monthly report", "earnings breakdown"
- Chaining from S6.1 (conversion-tracker) — analyze the data those links collected

## Input Schema

```yaml
programs:
  - name: string               # REQUIRED — program name (e.g., "HeyGen")
    clicks: number             # OPTIONAL — total clicks this period
    conversions: number        # OPTIONAL — total conversions
    revenue: number            # OPTIONAL — total commission earned ($)
    commission: number         # OPTIONAL — commission per sale ($)
    spend: number              # OPTIONAL — money spent on ads/promotion ($)

period: string                 # OPTIONAL — "week" | "month" | "quarter"
                               # Default: "month"

goals:
  revenue_target: number       # OPTIONAL — target revenue for the period ($)
  conversion_target: number    # OPTIONAL — target conversions

previous_period:               # OPTIONAL — last period's data for trend analysis
  - name: string
    clicks: number
    conversions: number
    revenue: number

notes: string                  # OPTIONAL — context about the period
                               # (e.g., "launched new blog post week 2")
```

**Chaining context**: If S1 program data or S6.1 tracking data exists in conversation, pull program names and any available metrics.

## Workflow

### Step 1: Collect Program Data

Gather data from user input. If data is incomplete, work with what's available and note gaps:
- "You provided revenue but not clicks — I can calculate revenue per program but not EPC or conversion rate."

### Step 2: Calculate KPIs

For each program:
- **EPC** (Earnings Per Click): revenue / clicks
- **Conversion Rate**: conversions / clicks × 100
- **Revenue Share**: program revenue / total revenue × 100
- **CPA** (Cost Per Acquisition): spend / conversions (if spend provided)
- **ROAS** (Return on Ad Spend): revenue / spend (if spend provided)
- **Commission Per Sale**: revenue / conversions

Portfolio-level:
- **Total Revenue**: sum of all program revenue
- **Blended EPC**: total revenue / total clicks
- **Blended Conversion Rate**: total conversions / total clicks × 100
- **Top Performer**: highest EPC program
- **Underperformer**: lowest EPC program

### Step 3: Rank Programs

Sort programs by ROI efficiency:
1. EPC (primary sort)
2. Total revenue (secondary)
3. Conversion rate (tertiary)

Assign labels:
- **Star**: High EPC + high volume → double down
- **Cash Cow**: Moderate EPC + high volume → maintain
- **Question Mark**: High EPC + low volume → scale up
- **Dog**: Low EPC + low volume → consider dropping

### Step 4: Identify Trends

If `previous_period` data is provided:
- Revenue trend: up/down/flat (with percentage)
- Click trend: up/down/flat
- Conversion trend: up/down/flat
- Per-program trends

### Step 5: Generate Recommendations

Based on data:
- **Double down**: Programs with high EPC that need more traffic
- **Optimize**: Programs with high traffic but low conversion (content issue)
- **Phase out**: Programs with low EPC and low volume
- **Investigate**: Programs with unusual patterns (sudden drops)

### Step 6: Self-Validation

Before presenting output, verify:

- [ ] EPC calculation correct: revenue ÷ clicks
- [ ] Conversion rate percentages are accurate
- [ ] Revenue shares across programs sum to ~100%
- [ ] Labels match metrics: Star (high EPC + growth), Cash Cow (high revenue + stable), Question Mark (low data), Dog (declining)
- [ ] Recommendations are specific and reference concrete next steps

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

## Output Schema

```yaml
output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
report:
  period: string
  total_revenue: number
  total_clicks: number
  total_conversions: number
  blended_epc: number
  blended_conversion_rate: number
  goal_progress: string        # "on_track" | "behind" | "ahead" | "no_goal"

programs:
  - name: string
    clicks: number
    conversions: number
    revenue: number
    epc: number
    conversion_rate: number
    revenue_share: number      # percentage of total
    label: string              # "star" | "cash_cow" | "question_mark" | "dog"
    trend: string              # "up" | "down" | "flat" | "new"

recommendations:
  - program: string
    action: string             # "double_down" | "optimize" | "phase_out" | "investigate"
    reason: string
    next_step: string          # specific action to take
```

## Output Format

1. **KPI Dashboard** — summary table with total revenue, clicks, conversions, blended EPC
2. **Program Rankings** — table sorted by EPC with labels (Star/Cash Cow/Question Mark/Dog)
3. **Trend Analysis** — period-over-period comparison (if previous data provided)
4. **Recommendations** — prioritized list of actions per program
5. **Goal Progress** — progress toward targets (if goals provided)

## Error Handling

- **No data provided**: "I need your affiliate numbers to generate a report. At minimum, provide: program names and revenue. Ideally also clicks and conversions. You can get these from your affiliate dashboard or tracking tool."
- **Only one program**: Generate the report for one program. Note: "With only one program, I can't do comparative analysis. Consider adding more programs to diversify. Use S1 (affiliate-program-search) to find complementary programs."
- **Missing clicks (revenue only)**: "Without click data, I can rank programs by revenue but can't calculate EPC or conversion rate. EPC is the most important affiliate metric — consider setting up tracking with S6.1 (conversion-tracker)."

## Examples

### Example 1: Monthly multi-program report

**User**: "Monthly report: HeyGen — 500 clicks, 15 conversions, $450. Semrush — 1200 clicks, 8 conversions, $320. Notion — 300 clicks, 25 conversions, $125."
**Action**: Calculate KPIs. HeyGen: EPC $0.90, CR 3.0% (Star). Semrush: EPC $0.27, CR 0.7% (Question Mark — high traffic, low conversion). Notion: EPC $0.42, CR 8.3% (Cash Cow — high conversion, low revenue per sale). Recommend: Scale HeyGen traffic, optimize Semrush content (CTAs, landing page), maintain Notion.

### Example 2: Week-over-week comparison

**User**: "This week vs last week: HeyGen clicks went from 100 to 150, but conversions dropped from 5 to 3."
**Action**: Flag conversion rate drop (5% → 2%). Diagnose: more traffic but lower quality? New traffic source? Landing page change? Recommend: Check traffic sources, run S6.4 (seo-audit) on landing page, test CTAs with S6.2 (ab-test-generator).

### Example 3: Revenue-only report

**User**: "My programs last month: HeyGen $450, Semrush $320, Notion $125, Canva $80."
**Action**: Revenue-only analysis. Total $975. Revenue share: HeyGen 46%, Semrush 33%, Notion 13%, Canva 8%. Note concentration risk (79% from 2 programs). Recommend: Set up click tracking (S6.1) for deeper analysis, consider diversifying with S1 research.

## References

- `references/benchmarks.md` — KPI benchmarks by channel, program label thresholds, conversion rate benchmarks, timeline expectations, S1 scoring feedback loop
- `shared/references/affiliate-glossary.md` — KPI definitions (EPC, CTR, ROAS). Referenced in Step 2.
- `shared/references/case-studies.md` — Real-world case studies with conversion rates and timelines. Use as context for setting realistic expectations.
- `shared/references/flywheel-connections.md` — master flywheel connection map

## Flywheel Connections

### Feeds Into
- `niche-opportunity-finder` (S1) — performance data identifies best-performing niches
- `affiliate-program-search` (S1) — which program types convert best
- `content-moat-calculator` (S3) — content performance metrics for moat progress
- `content-decay-detector` (S3) — traffic decline data for decay detection

### Fed By
- `conversion-tracker` (S6) — conversion data for reports
- `social-media-scheduler` (S5) — scheduled posts to measure
- `ab-test-generator` (S6) — test results to include

### Feedback Loop
- Performance insights feed back to S1 Research (which niches/programs to pursue) and S2-S4 (which content types and formats perform best) — the analytics-to-research flywheel

```yaml
chain_metadata:
  skill_slug: "performance-report"
  stage: "analytics"
  timestamp: string
  suggested_next:
    - "affiliate-program-search"
    - "niche-opportunity-finder"
    - "content-decay-detector"
```