What is Simba and how does it simplify marketing analytics?

Simba connects AI assistants (like Claude or Cursor) to marketing mix models, allowing you to build models, optimize budgets, and analyze data using natural language, eliminating complex manual processes.

Which AI assistants are compatible with Simba?

Simba is designed to work with AI assistants that support the Model Context Protocol (MCP), such as Claude and Cursor.

How does Simba ensure data privacy and accuracy?

Simba keeps your data local during all statistical calculations, ensuring privacy. It utilizes Bayesian hierarchical modeling to accurately calculate media performance and minimize errors.

Can I optimize my marketing budget with Simba?

Yes, Simba enables comprehensive marketing budget optimization and future scenario testing. You can ask the AI for recommendations based on specific budget caps or target revenue goals.

What kind of data do I need to use Simba?

You need to provide your marketing spend and outcome data (e.g., total revenue or conversions) in a CSV file. Ensure it includes a 'Date' column formatted as YYYY-MM-DD.

Simba

Name: Simba
Author: fritsferdiansyah38-EOS

byfritsferdiansyah38-EOS

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分析と監視

マーケティングオートメーション

データサイエンスとML

Connects AI assistants to marketing mix models for building models, analyzing data, and running budget optimizations using natural language.

Simba-mcp revolutionizes marketing analytics by enabling AI assistants like Claude and Cursor to interact directly with complex marketing mix models. It empowers marketers to leverage natural language for tasks such as uploading data, building Bayesian performance models, optimizing marketing budgets, and testing future scenarios. By bridging the gap between statistical analysis and daily operations, it allows AI to handle the heavy lifting, providing insights into ROI and budget allocation based on unique data, all while ensuring data privacy by processing calculations locally.

主な機能

01Enables natural language interaction for data upload and model creation

02Performs marketing budget optimization and future scenario testing

03Utilizes Bayesian hierarchical modeling for media performance calculation

04Connects AI assistants (e.g., Claude, Cursor) to marketing mix models

05Keeps user data local during statistical calculations for privacy

060 GitHub stars

ユースケース

01Optimizing marketing budgets to achieve specific revenue goals or caps

02Building marketing performance models using natural language with AI assistance

03Analyzing media attribution and ROI measurement across channels