eef020c9e0
Build campaign-analytics, financial-analyst, customer-success-manager, sales-engineer, and revenue-operations skills using the Claude Skills Factory workflow. Each skill includes SKILL.md, Python CLI tools, reference guides, and asset templates. All 16 Python scripts use standard library only with --format json/text support. Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
111 lines
3.8 KiB
JSON
111 lines
3.8 KiB
JSON
{
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"_description": "Expected output from running the 3 scripts against sample_campaign_data.json with --format json",
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"attribution_analyzer": {
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"_command": "python scripts/attribution_analyzer.py assets/sample_campaign_data.json --format json",
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"summary": {
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"total_journeys": 8,
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"converted_journeys": 6,
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"conversion_rate": 75.0,
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"total_revenue": 3700.0,
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"channels_observed": [
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"direct", "display", "email", "organic_search",
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"organic_social", "paid_search", "paid_social", "referral"
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]
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},
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"models": {
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"first-touch": {
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"organic_search": 700.0,
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"paid_social": 1200.0,
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"display": 350.0,
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"organic_social": 800.0,
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"referral": 650.0
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},
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"last-touch": {
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"paid_search": 1500.0,
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"direct": 2000.0,
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"organic_search": 200.0
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},
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"linear": {
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"organic_search": 666.67,
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"email": 1003.33,
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"paid_search": 718.33,
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"paid_social": 300.0,
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"direct": 460.0,
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"display": 175.0,
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"organic_social": 160.0,
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"referral": 216.67
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},
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"time-decay": {
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"organic_search": 582.38,
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"email": 1053.68,
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"paid_search": 881.03,
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"paid_social": 178.4,
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"direct": 638.82,
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"display": 140.62,
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"organic_social": 78.48,
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"referral": 146.59
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},
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"position-based": {
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"organic_search": 520.0,
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"paid_search": 688.33,
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"email": 456.67,
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"paid_social": 480.0,
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"direct": 800.0,
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"display": 175.0,
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"organic_social": 320.0,
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"referral": 260.0
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}
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}
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},
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"funnel_analyzer": {
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"_command": "python scripts/funnel_analyzer.py assets/sample_campaign_data.json --format json",
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"_note": "Uses segment comparison mode since 'segments' key is present in the data",
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"rankings": [
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{"rank": 1, "segment": "organic", "overall_conversion_rate": 5.6, "total_entries": 5000, "total_conversions": 280},
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{"rank": 2, "segment": "paid", "overall_conversion_rate": 3.0, "total_entries": 3000, "total_conversions": 90},
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{"rank": 3, "segment": "email", "overall_conversion_rate": 2.5, "total_entries": 2000, "total_conversions": 50}
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],
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"key_findings": {
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"all_segments_bottleneck_absolute": "Awareness -> Interest",
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"all_segments_bottleneck_relative": "Intent -> Purchase",
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"best_performing_segment": "organic (5.6% overall conversion)",
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"worst_performing_segment": "email (2.5% overall conversion)"
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}
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},
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"campaign_roi_calculator": {
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"_command": "python scripts/campaign_roi_calculator.py assets/sample_campaign_data.json --format json",
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"portfolio_summary": {
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"total_campaigns": 5,
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"total_spend": 34000.0,
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"total_revenue": 99000.0,
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"total_profit": 65000.0,
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"portfolio_roi_pct": 191.18,
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"portfolio_roas": 2.91,
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"blended_ctr_pct": 1.04,
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"blended_cpl": 27.64,
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"blended_cpa": 161.9,
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"top_performer": "Spring Email Campaign",
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"underperforming_campaigns": [
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"Spring Email Campaign",
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"Facebook Awareness Q1",
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"LinkedIn B2B Outreach"
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]
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},
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"channel_summary": {
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"email": {"spend": 5000.0, "revenue": 25000.0, "roi_pct": 400.0, "roas": 5.0},
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"paid_search": {"spend": 12000.0, "revenue": 48000.0, "roi_pct": 300.0, "roas": 4.0},
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"paid_social": {"spend": 14000.0, "revenue": 17000.0, "roi_pct": 21.43, "roas": 1.21},
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"display": {"spend": 3000.0, "revenue": 9000.0, "roi_pct": 200.0, "roas": 3.0}
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},
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"key_findings": {
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"most_profitable_channel": "paid_search ($36,000 profit)",
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"highest_roas_channel": "email (5.0x ROAS)",
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"unprofitable_campaign": "LinkedIn B2B Outreach (-$1,000 loss)",
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"best_ctr": "Spring Email Campaign (5.0%)"
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}
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}
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}
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