performance-report

name	performance-report
description	Generate affiliate performance reports with KPIs and recommendations. Triggers on: "show my affiliate report", "how are my programs doing", "performance review", "earnings report", "monthly affiliate report", "weekly report", "analyze my affiliate earnings", "which program is best", "EPC report", "conversion rate analysis", "revenue breakdown", "campaign performance".
license	MIT
version	1.0.0
tags	["affiliate-marketing","analytics","optimization","tracking","reporting","kpi"]
compatibility	Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent
metadata	{"author":"affitor","version":"1.0","stage":"S6-Analytics"}

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

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

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

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