mixlift 0.1.0

Marketing Mix Modeling MCP server — CSV in, budget recommendations out.

MixLift

Marketing Mix Modeling as an MCP server. CSV in, budget recommendations out.

MixLift runs Bayesian Media Mix Models (MMM) using PyMC-Marketing and exposes results through the Model Context Protocol so AI assistants like Claude can analyze your marketing data.

What you get

• Channel ROAS — Return on ad spend per channel with 90% credible intervals
• Marginal ROAS — Diminishing returns at current spend levels
• Budget optimization — Optimal allocation across channels with expected lift
• Saturation curves — Spend vs. response curves showing diminishing returns
• Convergence diagnostics — MCMC health checks (R-hat, divergences)
• Actionable recommendations — Ranked list of budget reallocation moves

Install

pip install mixlift

Requires Python 3.11+. PyMC and its dependencies (PyTensor, NumPy, etc.) are installed automatically.

Setup with Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "mixlift": {
      "command": "mixlift-mcp"
    }
  }
}

Restart Claude Desktop. You now have the mixlift_analyze tool available.

Setup with Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "mixlift": {
      "command": "mixlift-mcp"
    }
  }
}

Usage

Ask Claude to analyze your marketing data:

"Run a marketing mix model on my data at ~/marketing/spend_data.csv"

Or try the bundled demo dataset:

"Run mixlift_analyze with no arguments to use the demo data"

CSV format

MixLift auto-detects your CSV format. The simplest is wide format:

date	google_search_spend	meta_spend	tiktok_spend	revenue
2024-03-11	5243.77	5658.76	1991.60	61475.19
2024-03-18	4237.89	7190.88	1825.93	59907.05

Requirements:

• date column (weekly granularity works best)
• One or more *_spend columns
• A revenue, target, or sales column

Platform exports from Meta Ads, Google Ads, and TikTok Ads are also supported.

Pricing

• Free: 3 channels, 1,000 rows
• Pro ($199/mo): Unlimited channels and rows — mixlift.io/pricing

How it works

1. Your CSV is loaded and auto-detected (wide format or platform export)
2. A Bayesian MMM is fit using MCMC sampling (~30-60 seconds)
3. Channel contributions, ROAS, and saturation parameters are extracted
4. Budget is optimized using gradient-based reallocation
5. Results are returned as structured JSON to the AI assistant

License

MIT