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campaign-analytics

Distribute revenue credit across marketing touchpoints using five attribution models.

Updated Mar 5, 2026
Search Tags:#attribution#roi#datasets#marketing-analytics#funnel#multi-touch#credit-allocation
One-click install
npx skills add https://github.com/theandyalvarez7-ruby/claude-skills --skill campaign-analytics-theandyalvarez7-ruby
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: campaign-analytics
Source: https://github.com/theandyalvarez7-ruby/claude-skills/tree/main/marketing-skill/campaign-analytics
Command: npx skills add https://github.com/theandyalvarez7-ruby/claude-skills --skill campaign-analytics-theandyalvarez7-ruby

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Marketing data is spread across channels and models; this Skill analyzes campaigns by distributing revenue credit to touchpoints via multi-touch attribution, funnels, and ROI calculations, enabling data-driven decisions.

Core Features & Use Cases

  • Multi-Touch Attribution: five models (first-touch, last-touch, linear, time-decay, position-based) with per-model credits.
  • Funnel Analysis: stage-wise conversion and bottlenecks to optimize paths.
  • ROI & Performance: compute ROI, ROAS, CPA, CPL, CAC with benchmark guidance.
  • Reference & Reporting: ready-made templates and cross-model comparison reports.
  • Offline CLI Tools: Python-based, no external API calls; works on static JSON data.

Quick Start

Run the attribution, funnel, and ROI tools on your campaign data using the bundled Python scripts.

Frequently Asked Questions about campaign-analytics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate multi-touch attribution for marketing campaigns?▼

Multi-touch attribution distributes revenue credit across touchpoints using five models: first-touch, last-touch, linear, time-decay, and position-based. The Skill processes campaign data to provide per-model credit and cross-model comparison for ROI insights.

What marketing data do I need for multi-touch attribution analysis?▼

Multi-touch attribution requires campaign data containing touchpoints, timestamps, revenue, and conversion flags. The Skill processes this static JSON data offline using bundled Python scripts to compute attribution credits and ROI.

How does time-decay attribution differ from linear and position-based models?▼

Time-decay attribution assigns more credit to touchpoints closer to conversion, while linear distributes credit evenly and position-based weights the first and last touchpoints heavier. The Skill computes all five models for cross-model comparison.

Can I analyze funnel bottlenecks and compute ROAS from static JSON data?▼

Funnel analysis and ROAS computation can be performed on static JSON campaign data using bundled offline Python scripts. The Skill identifies stage-wise conversion bottlenecks and calculates ROI, ROAS, CPA, CPL, and CAC with benchmark guidance.

Do I need external API calls to run campaign attribution and ROI calculations?▼

No external API calls are required for campaign attribution and ROI calculations. The Skill uses offline Python-based CLI tools that operate on local static JSON data to distribute revenue credit and generate cross-model comparison reports.

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