marketing-attribution-modeler-mcp
Exposes MCP endpoints for building and applying marketing attribution models to assign credit to touchpoints like ads, emails, and social media in customer journeys. Data analysts and marketing teams use it to quantify channel ROI from conversion data. Applies models such as last-click, linear, or Markov chains via API calls.
Overview
The marketing-attribution-modeler-mcp server provides Model Context Protocol (MCP) access to functions for constructing and executing marketing attribution models. It processes customer journey data to distribute conversion credit across marketing channels, supporting data-driven budget allocation.
Key Capabilities
- Model Fitting: Ingests event data (clicks, views, purchases) to train attribution models including first-touch, last-touch, linear, time-decay, and position-based.
- Prediction and Attribution: Runs models on new datasets to output channel contributions as percentages or values.
- Reporting: Generates visualizations and summaries of attribution results, such as ROI per channel.
Specific tools are not enumerated, but the server handles data ingestion, model computation, and output export in standard formats like CSV or JSON.
Use Cases
- Campaign Analysis: Feed clickstream data from Google Analytics into the model fitter to attribute e-commerce sales across paid search, display ads, and organic traffic.
- Budget Optimization: Apply time-decay model predictions to historical sales data, identifying underperforming channels for reallocation.
- Cross-Channel ROI: Use linear attribution on CRM journey data to quantify email nurture vs. social retargeting impact on leads.
- A/B Testing: Compare attribution outputs between control and test campaigns to measure uplift.
Who This Is For
Marketing analysts, data scientists, and growth teams analyzing multi-touch customer journeys. Developers integrating attribution into dashboards or automation pipelines benefit from the MCP API.