marketing

Marketing-Attribution-Models: Accurate Attribution

Name: Marketing-Attribution-Models: Accurate Attribution
Author: DP6

Get accurate digital marketing attribution with Marketing-Attribution-Models, a Python class.

369 stars87 forksPythonGuide quality 9/10Updated 9/3/2026100% free · open source

What it does

Marketing-Attribution-Models is a Python class that helps startup founders accurately attribute the impact of their digital marketing efforts on customer interactions and conversions.

When to use it

•You're spending a significant budget on digital marketing and need to understand which channels are driving the most value.
•You're using multiple marketing channels and want to optimize your budget allocation based on data-driven insights.
•You need to measure the return on investment (ROI) of specific marketing campaigns or tactics.

Ready-to-paste prompt

from marketing_attribution import MarketingAttribution; attribution = MarketingAttribution(df); attribution.run_attribution()

Heads up: Make sure you have Python 3.8 or later installed, as well as the required libraries listed in the `requirements.txt` file, including Pandas and NumPy.

Saves to your device

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/marketing-attribution-models && curl -fsSL https://workflowstacks.com/api/skills/marketing-attribution-models/claude-skill -o ~/.claude/skills/marketing-attribution-models/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Marketing-Attribution-Models: Accurate Attribution works

Codeflow

Free to inspect

Marketing-Attribution-Models: Accurate Attribution is a medium Python project (~5.9k lines across 19 code files, plus 9 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, Apache-2.0 license, has a test suite.

Size

Medium codebase

~5.9k lines · 19 code files · 49 min skim

Setup

One-command install

Installs like a normal app. Reading the code is optional.

Runs on

Python

No API keys detected

Python 97%CSS 2%JavaScript 1%

Where to start reading

1
README.md
Start here — what it does and how to install it
2
pyproject.toml
Dependencies and the commands it exposes
3
examples/generate_example.py
A worked example — copy this to get going

What's in each folder

examples/Examples you can copy4 files

mam/Folder11 files

codigo_legado/Folder4 files

visual_ref/Folder3 files

manifesto/Folder1 files

plano/Folder1 files

tests/Tests — proof it works9 files

.github/CI / automation (GitHub Actions)7 files

READMEHas testsDocumentedCI checksExamples includedApache-2.0 licenseUpdated this month