Marketing Intelligence & Optimization

Marketing Intelligence & Optimization

Turns ad spend, sales, order baskets, A/B test counts and adoption data into ranked STOP / SCALE / SHIP / WAIT decisions with money impact and confidence. Covers marketing mix modelling (channel ROI, saturation and budget reallocation), market-basket bundles, A/B significance with power and peeking checks, Bass diffusion for new-product ramps, and CAC / LTV / payback by channel. Each part runs only when its data is supplied; MMM results are directional, not causal.

Registry name	ai.saifs/marketing-intelligence
Endpoint	https://marketing-analysis-mcp.saifs.ai/mcp
Transport	Streamable HTTP (remote, nothing to install)
Auth	OAuth 2.1 — PKCE (S256) and dynamic client registration; your client opens a sign-in page on first use
Publisher	SAIFS AI

Install

VS Code / GitHub Copilot

Add to .vscode/mcp.json (workspace) or your user mcp.json:

{
  "servers": {
    "saifs-marketing-intelligence": {
      "type": "http",
      "url": "https://marketing-analysis-mcp.saifs.ai/mcp"
    }
  }
}

Then start the server from the MCP view and sign in when prompted.

Claude (claude.ai, Desktop)

Settings → Connectors → Add custom connector, and paste:

https://marketing-analysis-mcp.saifs.ai/mcp

Claude Code

claude mcp add --transport http saifs-marketing-intelligence https://marketing-analysis-mcp.saifs.ai/mcp

Any other MCP client

Point it at https://marketing-analysis-mcp.saifs.ai/mcp using the Streamable HTTP transport. The server returns 401 with a WWW-Authenticate: Bearer … resource_metadata=… challenge, so OAuth-aware clients sign in automatically.

Tools

Tool	What it does
marketing_intelligence	Analyses payloads of up to 200 rows per data array — spend, sales, baskets, A/B tests, adoption, acquisitions, customer revenue — and returns ranked decisions plus channel ROI, budget moves, bundles, test verdicts and unit economics.
marketing_intelligence_get_engine	For larger exports (Meta/Google spend, Shopify orders): returns a numpy-only Python script that runs the same analysis locally, so no marketing data reaches the server.

Example prompts

• “Here's our weekly Meta, Google and email spend with sales. Which channel should we scale or cut?”
• “Is our landing-page A/B test win real, or is it under-powered?”
• “Which products should we bundle, based on these order baskets?”

Support

• Website: https://saifs.ai
• Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
• Privacy: https://saifs.ai/privacy_policy