campaign-analytics

Distribute conversion credit across marketing touchpoints using five attribution models.

Updated Mar 21, 2026
One-click install
npx skills add https://github.com/AgLyx3/My-Note-App-Not-Just-a-Note-App --skill campaign-analytics-aglyx3
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: campaign-analytics
Source: https://github.com/AgLyx3/My-Note-App-Not-Just-a-Note-App/tree/main/.cursor/skills/marketing-skill/campaign-analytics
Command: npx skills add https://github.com/AgLyx3/My-Note-App-Not-Just-a-Note-App --skill campaign-analytics-aglyx3

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Analyzes marketing campaign performance by distributing revenue credit across multiple channels through multi-touch attribution, revealing true channel value and ROI.

Core Features & Use Cases

  • Multi-Touch Attribution: five models (first-touch, last-touch, linear, time-decay, position-based) with per-model credits.
  • Funnel & ROI Insights: analyses funnel progression and computes ROI/ROAS, with A/B test templates and cross-channel comparisons.
  • Reproducible CLI Tools: deterministic Python CLI tools that operate on JSON data, no external API calls, suitable for offline analysis.
  • Use Case: Run analytics on a dataset of journeys to identify which channels drive conversions across the full funnel, and generate executive-ready reports.

Quick Start

Use the skill by running the attribution analyzer on a data file to produce multi-model results.

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 to compare channel value across my marketing campaigns?

Multi-touch attribution distributes conversion credit across marketing touchpoints to reveal true channel value. You apply five attribution models—first-touch, last-touch, linear, time-decay, and position-based—to journey datasets and compare per-channel credits side by side.

What's the difference between first-touch, last-touch, linear, time-decay, and position-based attribution models?

First-touch credits the initial interaction; last-touch credits the final one; linear spreads credit evenly; time-decay weights later touchpoints more heavily; position-based splits credit between first, middle, and last interactions. The analyzer outputs a cross-model comparison to highlight these differences.

How do I run campaign attribution analysis on my customer journey data without external API calls?

You run deterministic Python CLI tools on JSON data files containing journeys, touchpoints, conversions, and revenue. The tools operate fully offline with no external API calls, producing per-channel credits, model summaries, and cross-model comparison outputs.

Can I use this multi-touch attribution tool to compute ROI and funnel progression for multiple marketing channels?

Yes, multi-touch attribution analyzes funnel progression and computes ROI and ROAS across channels. It applies five attribution models to your journey data and generates per-channel credits alongside funnel insights and cross-channel comparisons.

What data format do I need for multi-touch attribution analysis of campaign performance?

Multi-touch attribution requires JSON datasets containing marketing journeys with touchpoints, conversions, and revenue. The deterministic CLI tools read these JSON files directly and output per-model credits, model summaries, and cross-model comparison reports.

Why does last-touch attribution hide the true value of earlier marketing channels in my funnel?

Last-touch attribution assigns all conversion credit to the final interaction, ignoring upstream touchpoints. Multi-touch attribution solves this by distributing credit across the entire journey, revealing which earlier channels actually contribute to ROI and funnel progression.