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marketing-analyst

Analyze marketing performance data with Python analytics pipelines.

467|103|Updated Jan 13, 2026
Search Tags:#analytics#marketing#reporting#attribution#roi#campaigns#analyze
One-click install
npx skills add https://github.com/borghei/Claude-Skills --skill marketing-analyst
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marketing-analyst
Source: https://github.com/borghei/Claude-Skills/tree/main/marketing-growth/marketing-analyst
Command: npx skills add https://github.com/borghei/Claude-Skills --skill marketing-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Marketing teams often struggle to quantify campaign impact, attribute conversions across channels, and present ROI clearly to stakeholders.

Core Features & Use Cases

  • Attribution modeling and MMM to allocate credit across touchpoints.
  • ROI measurement and performance dashboards for executives.
  • Forecasting, scenario analysis, and channel optimization across campaigns.

Quick Start

Use the marketing-analyst skill to analyze a campaigns.csv file and generate a performance report.

Frequently Asked Questions about marketing-analyst

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

FAQPage Schema
How do I measure marketing ROI and attribute conversions across multiple channels?▼

Marketing ROI measurement and cross-channel attribution are handled by applying modeling pipelines to campaign data, allocating credit across touchpoints, and generating actionable performance dashboards for stakeholders.

What is the best way to analyze a campaigns.csv file for performance reporting?▼

Analyzing a campaigns.csv file involves running Python-based analytics pipelines that process the data to evaluate channel performance, calculate ROI, and output structured reporting insights for data-driven decisions.

Can I use Python to build attribution modeling and MMM pipelines for campaign optimization?▼

Yes, Python-based analytics pipelines are implemented to support attribution modeling and Marketing Mix Modeling (MMM), enabling scenario analysis and channel optimization across various campaigns and timeframes.

How does marketing forecasting and scenario analysis work for channel optimization?▼

Marketing forecasting and scenario analysis apply analytical pipelines to historical campaign performance data, projecting future outcomes to optimize channel allocation and support data-driven growth decisions.

Do I need specific data formats to generate executive performance dashboards for campaigns?▼

You need structured campaign performance data, such as a campaigns.csv file, to feed into the analytics pipelines that process metrics and generate executive-level ROI and performance dashboards.

Why use Python-based analytics pipelines for marketing data instead of standard reporting tools?▼

Python-based analytics pipelines enable advanced attribution modeling, MMM, and ROI forecasting directly on campaign data, delivering deeper insights than standard reporting tools for data-driven marketing decisions.

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