Does this skill require external API connections?

No, all analysis is performed locally using Python's standard library for deterministic, private, and repeatable results without external dependencies.

Can I integrate these analytics into my existing data pipeline?

Yes, all scripts support a --format json flag, allowing you to easily pipe machine-readable results into other tools or automation workflows.

How does the funnel analyzer identify bottlenecks?

The script automatically calculates both absolute and relative drops between stages and flags the largest exit points to highlight optimization opportunities.

What attribution models does this skill support?

It supports five industry-standard models: First-Touch, Last-Touch, Linear, Time-Decay (with configurable half-life), and Position-Based (40/20/40).

What metrics are included in the ROI calculator?

It calculates ROI, ROAS, CPA (Cost Per Acquisition), CPL (Cost Per Lead), CAC, CTR, and conversion rates, then benchmarks them against industry standards.

Campaign Analytics

Name: Campaign Analytics
Author: micsapp

bymicsapp

0•

マーケティングオートメーション

Analyzes marketing performance using multi-touch attribution models, funnel conversion tracking, and ROI calculations.

This skill provides a suite of production-grade marketing analytics tools for Claude Code, enabling developers and marketers to perform deterministic analysis of campaign data. It features five industry-standard attribution models including First-Touch, Linear, and Time-Decay to help understand channel value, identifies conversion bottlenecks in sales funnels, and calculates critical performance metrics like ROI, ROAS, and CAC against industry benchmarks. Designed to work with standard Python libraries, it ensures repeatable results without external API dependencies or complex machine learning overhead.

主な機能

01Automated funnel bottleneck identification and stage-to-stage drop-off analysis

02Dual output formats supporting human-readable tables and machine-readable JSON

030 GitHub stars

04Five multi-touch attribution models for precise channel credit allocation

05Comprehensive ROI, ROAS, CPA, and CAC calculation with industry benchmarking

06Dependency-free Python implementation for secure, local data processing

ユースケース

01Evaluating channel effectiveness across complex customer journeys using time-decay or linear attribution

02Auditing campaign profitability and ad spend efficiency to guide strategic budget reallocation

03Optimizing conversion rates by pinpointing specific stages where prospects exit the marketing funnel

主な機能

01Automated funnel bottleneck identification and stage-to-stage drop-off analysis

02Dual output formats supporting human-readable tables and machine-readable JSON

030 GitHub stars

04Five multi-touch attribution models for precise channel credit allocation

05Comprehensive ROI, ROAS, CPA, and CAC calculation with industry benchmarking

06Dependency-free Python implementation for secure, local data processing

ユースケース

01Evaluating channel effectiveness across complex customer journeys using time-decay or linear attribution

02Auditing campaign profitability and ad spend efficiency to guide strategic budget reallocation

03Optimizing conversion rates by pinpointing specific stages where prospects exit the marketing funnel