CTR Suppression Intelligence Engine – Baseline-Adjusted AI Search Impact Modeling Platform_

Project Overview

AI-driven search interfaces are fundamentally changing organic traffic behavior.

AI Overviews, enriched SERP features, and zero-click experiences reduce click-through rates — even when rankings remain stable.

Traditional SEO tools track:

• Position changes
• Keyword visibility
• Traffic fluctuations

They do not:

• Isolate interface-driven suppression from ranking volatility
• Model expected CTR behavior by ranking position
• Quantify revenue exposure caused by AI-induced click loss

The CTR Suppression Intelligence Engine was built to solve that gap.

It ingests exported Search Console data, constructs historical CTR baselines, applies statistical validation, models volatility-adjusted risk scores, and quantifies revenue exposure — all through deterministic backend modeling.

This is not a traffic comparison dashboard. It is a suppression modeling intelligence engine.

What It Does

The system ingests:

• Google Search Console query-level CSV exports
• Query, date, CTR, impressions, and average position data
• Configurable AI rollout cutoff date

Then computes:

• Position-Normalized CTR Baseline
• Expected CTR vs Actual CTR Deviation
• Ranking Stability Filtering
• Effective CTR Suppression Score
• Statistical Significance Validation (t-test)
• Revenue Loss Estimation
• 6-Month Revenue Exposure Projection
• Intent-Level Suppression Analysis
• Volatility-Adjusted Risk Score
• High-Risk Query Detection

Every metric is derived from deterministic backend modeling — the frontend only renders computed results.

No synthetic demo metrics. No client-side scoring logic.

Core Capabilities

• Baseline-Adjusted CTR Modeling

  Builds historical expected CTR curves by ranking position and applies them to post-cutoff data to isolate suppression independent of ranking movement.

• Ranking Stability Filtering

  Removes queries with significant ranking shifts to prevent false suppression attribution.

• Statistical Validation Engine

  Uses two-sample statistical testing to determine whether CTR suppression is significant rather than noise.

• Revenue Impact Modeling

  Estimates revenue loss using lost clicks, conversion rate, and average order value. Provides query-level revenue exposure.

• Volatility Scoring

  Applies rolling suppression modeling to detect CTR instability and integrate volatility into risk scoring.

• Intent Classification Engine

  Classifies queries into informational, commercial, transactional, and navigational categories, then measures suppression intensity across intent types.

• Deterministic Risk Scoring

  Combines effective CTR loss, revenue exposure, statistical significance, and volatility factor to compute a final risk score and risk level classification.

• Executive Intelligence Dashboard

  Visualizes revenue at risk KPIs, suppression heatmap, intent-level impact distribution, volatility trend modeling, high-risk query table, and 6-month revenue projection.

• Local-First Architecture

  Runs entirely on FastAPI + React with no external AI APIs or third-party black-box scoring services. All modeling logic is deterministic and server-side.

The Challenge

Modern search environments introduce interface-driven suppression that cannot be detected by:

• Rank tracking tools
• Visibility indexes
• Traditional SEO reporting
• Keyword position monitoring

Organic traffic declines may not correlate with ranking drops.

This creates analytical blind spots:

• Was the loss caused by ranking?
• Was it seasonal?
• Or was it interface-driven suppression?

There was no lightweight, self-hosted system capable of:

• Modeling expected CTR behavior
• Isolating suppression from rank volatility
• Quantifying revenue impact deterministically
• Integrating volatility into risk scoring

The Solution

Built a full-stack suppression intelligence engine composed of:

Backend:

• FastAPI modeling API
• Historical CTR baseline modeling
• Position-normalized expected CTR engine
• Ranking stability filter
• Statistical significance testing layer
• Volatility computation engine
• Revenue loss modeling logic
• Deterministic risk scoring system
• Structured JSON intelligence output

Frontend:

• React dashboard interface
• TypeScript strict typing
• Dark-mode executive UI
• Suppression heatmap visualization
• Intent impact bar modeling
• Volatility trend chart
• Revenue projection modeling
• High-risk query detection table

The system enforces strict backend authority — risk scores and suppression metrics cannot be manipulated client-side.

Why It Matters

As AI interfaces reshape search behavior, businesses must understand:

• Which queries are structurally suppressed
• Where revenue exposure exists
• How volatility evolves post-AI rollout
• Which intent categories are most vulnerable
• Which queries represent strategic risk

This engine provides a measurable, deterministic framework for quantifying AI-driven CTR suppression.

It shifts suppression analysis from observational reporting to structured baseline modeling and statistical validation.

Future Expansion

• Direct Google Search Console API integration
• AI Overview detection simulation layer
• SERP feature exposure modeling
• Multi-domain suppression comparison
• Historical suppression tracking
• Multi-cutoff event modeling
• Automated narrative generation layer
• SaaS-ready multi-tenant architecture
• Persistent database storage
• Trend comparison between analysis runs