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Data Analyst

Analyze marketing data and track KPIs with Python scripts.

6|5|Updated Mar 9, 2026
Search Tags:#data analysis#marketing analytics#KPI tracking#dashboard design#GA4 insights
One-click install
npx skills add https://github.com/OpenAnalystInc/Vibe-Marketer --skill data-analyst-openanalystinc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Data Analyst
Source: https://github.com/OpenAnalystInc/Vibe-Marketer/tree/main/.agents/skills/data-analyst
Command: npx skills add https://github.com/OpenAnalystInc/Vibe-Marketer --skill data-analyst-openanalystinc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides data-driven marketing insights and analytics, enabling users to analyze marketing data, track KPIs, design dashboards, and perform attribution modeling.

Core Features & Use Cases

  • Marketing Analytics: Track and report on marketing KPIs, including CAC, LTV, ROAS, and conversion rates.
  • Attribution Modeling: Perform multi-touch attribution and channel contribution analysis.
  • Dashboard Design: Create GA4 custom reports and Looker Studio dashboards.
  • Campaign Performance Analysis: Analyze campaign ROI, incrementality testing, and lift analysis.
  • Cohort & Segmentation Analysis: Conduct user cohort analysis and behavioral segmentation.
  • Funnel Analytics: Analyze conversion funnels and identify bottlenecks.
  • Competitive Benchmarking: Compare industry benchmarks and market share estimation.
  • Forecasting & Modeling: Traffic forecasting and revenue projection.
  • Data Quality & Governance: Ensure data quality and compliance with privacy standards.

Quick Start

Use the Data Analyst skill to analyze the campaign performance metrics from the last quarter.

Frequently Asked Questions about Data Analyst

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

FAQPage Schema
How do I track marketing KPIs like CAC, LTV, and ROAS for my campaigns?▼

Tracking marketing KPIs like CAC, LTV, and ROAS requires analyzing campaign performance metrics using Python scripts. This process calculates conversion rates and channel contributions to provide data-driven marketing measurement for your campaigns.

How does multi-touch attribution modeling work for marketing analytics?▼

Multi-touch attribution modeling works by analyzing channel contribution across the customer journey using Python data processing. It assigns credit to various marketing touchpoints to identify which channels drive conversion rates and campaign ROI.

What's the best way to create GA4 custom reports and Looker Studio dashboards?▼

Creating GA4 custom reports and Looker Studio dashboards involves processing GA4 insights and visualization data with Python libraries like matplotlib and seaborn. This approach designs dashboards that visualize marketing analytics and funnel bottlenecks.

Can I use Python and pandas for cohort analysis and behavioral segmentation?▼

Yes, you can use Python and pandas for cohort analysis and behavioral segmentation. By processing user data with pandas and numpy, this approach segments users into cohorts to analyze behavioral patterns and conversion funnels.

Do I need Python scripts to perform traffic forecasting and revenue projection?▼

Yes, you need Python scripts utilizing pandas and numpy to perform traffic forecasting and revenue projection. These scripts process historical marketing analytics data to model future campaign performance metrics and project revenue outcomes.

Why should I use Python for competitive benchmarking and market share estimation?▼

Using Python for competitive benchmarking compares industry benchmarks against your marketing analytics data to estimate market share. This approach leverages data-driven insights to evaluate campaign performance metrics relative to industry standards.

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