MixLift brings full Bayesian Marketing Mix Modeling to your AI assistant. Know your true channel performance — not last-click fiction.
┌──────────────┬────────────┬─────────────────┐ │ Channel │ ROAS │ 90% CI │ ├──────────────┼────────────┼─────────────────┤ │ Meta Ads │ 3.2x │ [2.8x — 3.7x] │ │ Google Ads │ 2.1x │ [1.7x — 2.5x] │ │ TikTok Ads │ 4.1x │ [3.4x — 4.9x] │ └──────────────┴────────────┴─────────────────┘
Last-click attribution over-credits the final touchpoint and ignores everything that built demand. You're optimizing for the wrong channels.
Traditional Marketing Mix Modeling requires expensive consultants, months of setup, and enterprise contracts. Most teams can't justify the cost.
Linear regression in a spreadsheet ignores saturation effects, adstock carryover, and uncertainty. Your 'model' is a guess with extra steps.
MixLift runs a full Bayesian Marketing Mix Model locally on your machine. No dashboard. No login. No data upload. Just ask.
Your marketing data never leaves your machine. Period.
PyMC-powered inference with credible intervals — not a regression hack.
Auto-detects Meta, Google, and TikTok CSV exports. Drop in your data and ask.
Get return on ad spend for every channel with Bayesian credible intervals. Know not just the estimate, but how confident you should be.
Receive data-driven recommendations on how to reallocate spend across channels for maximum return.
See diminishing returns visualized. Know exactly when you're overspending on a channel before you waste another dollar.
Drop in CSV exports from Meta Ads, Google Ads, TikTok Ads, or any generic format. MixLift figures out the rest.
pip install mixlift-mcp
{
"mcpServers": {
"mixlift": {
"command": "mixlift-mcp"
}
}
}Ask your AI assistant: "Analyze my marketing data with MixLift" — that's it.
pip install mixlift-mcp
Add to your MCP config:
{
"mcpServers": {
"mixlift": {
"command": "mixlift-mcp"
}
}
}"Analyze my marketing data with MixLift"
Traditional attribution tools use last-click or rules-based models that systematically misattribute credit. MixLift uses Bayesian Marketing Mix Modeling — the same methodology used by Fortune 500 companies — to estimate true incremental impact of each channel, including saturation effects and carryover.
Yes. MixLift runs 100% locally on your machine via MCP. Your marketing data is never sent to any server, API, or cloud service. It stays on your computer.
The free tier supports up to 3 marketing channels. If you need more, the Pro plan at $199/month gives you unlimited channels and rows.
MixLift uses PyMC for full Bayesian inference, which means you get credible intervals — not just point estimates. The model accounts for saturation, adstock carryover, and control variables. Accuracy depends on your data quality and volume, but the confidence intervals tell you exactly how certain (or uncertain) the model is.
MixLift auto-detects CSV exports from Meta Ads, Google Ads, and TikTok Ads. You can also use any generic CSV with date, spend, and revenue columns. No special formatting required.