Building an AI-Search Visibility Dashboard Using GA4, GSC, BigQuery & Looker_

Organic search reporting is broken.

Not because the data is wrong—but because the questions have changed.

This article explains how to build an AI-Search Visibility Dashboard using:

• Google Analytics 4
• Google Search Console
• BigQuery
• Google Looker Studio

The goal is not more charts. The goal is executive-ready AI impact monitoring.

Why Traditional SEO Dashboards No Longer Work

Classic dashboards assume this flow:

Rankings → CTR → Traffic → Conversions

AI search breaks the chain between rankings and clicks.

The missing variable? AI answers intercepting demand.

If your dashboard doesn't explicitly measure AI impact, leadership is flying blind.

What an AI-Search Visibility Dashboard Must Answer

An executive does not care about:

• Average position
• Keyword count
• Indexed pages

They care about:

• Where are we losing visibility to AI answers?
• Which queries are becoming zero-click by design?
• Which content still influences AI outputs?
• What is the revenue risk trajectory?

Your dashboard must translate AI behavior into business language.

The Core Architecture

At a high level, the pipeline looks like this:

Each tool does what it's best at.

Step 1: Query Class Segmentation (The Foundation)

Not all queries are equally affected by AI.

Before building metrics, you must classify queries.

AI answers disproportionately suppress informational and research queries.

If you don't segment queries, AI impact gets averaged away.

Query Classification Logic (Conceptual)

In BigQuery, classify queries using pattern matching:

CASE
  WHEN query LIKE '%what%' OR query LIKE '%how%' THEN 'informational'
  WHEN query LIKE '%best%' OR query LIKE '%vs%' THEN 'commercial'
  WHEN query LIKE '%price%' OR query LIKE '%buy%' THEN 'transactional'
  ELSE 'other'
END AS query_class

This is not perfect—but it's directionally powerful.

Step 2: Detecting AI-Answer Overlap

The most important new metric is AI-answer overlap.

AI-answer overlap asks: "Is this query likely being answered directly by an AI overview?"

Signals include:

• Sudden CTR drop without ranking change
• Stable impressions with falling clicks
• SERP features expanding above organic results

You may not have explicit AI flags—but behavior tells the story.

AI-Answer Overlap Heuristic

In BigQuery:

CASE
  WHEN ctr_after < ctr_before
   AND ABS(avg_position_after - avg_position_before) < 0.5
  THEN 1
  ELSE 0
END AS ai_overlap_flag

This flags queries where AI interception is likely.

Step 3: Pre/Post CTR Suppression Modeling

Now quantify loss.

SELECT
  query,
  query_class,
  AVG(ctr_before) - AVG(ctr_after) AS ctr_loss
FROM search_console_data
GROUP BY query, query_class;

This turns AI impact into:

• Measurable deltas
• Comparable across query classes
• Trackable over time

Executives understand loss.

Step 4: GA4 Event Stitching (Turning Loss Into Value)

CTR loss alone is not a business metric.

You must connect:

Query → Landing page → Session → Conversion → Revenue

This is where GA4 comes in.

Now you can answer: "What is the estimated revenue impact of AI-driven visibility loss?"

That is CFO-level insight.

Step 5: Defining AI-Search Visibility Metrics

Your dashboard should focus on four core metrics.

1. AI Visibility Index

A composite score based on:

• Query volume
• CTR retention
• AI overlap probability

This shows overall exposure health, not traffic.

2. AI Suppression Rate

Percentage of queries experiencing CTR suppression without ranking loss.

This isolates AI effects from SEO issues.

3. Influence vs Traffic Ratio

Measures:

• Pages still cited or influencing AI answers
• Even when traffic declines

This reframes SEO success away from clicks alone.

4. Revenue at Risk

Estimated revenue tied to:

• AI-suppressed queries
• Informational demand loss
• Early-funnel interception

This metric gets budgets approved.

Step 6: BigQuery as the Intelligence Layer

BigQuery is not optional at this stage.

You need it to:

• Handle time-series analysis
• Run cohort comparisons
• Normalize data sources
• Create repeatable pipelines

Without BigQuery, AI impact analysis collapses into spreadsheets.

Example: Weekly AI Impact Snapshot

SELECT
  DATE_TRUNC(date, WEEK) AS week,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks,
  AVG(ctr) AS avg_ctr,
  SUM(ai_overlap_flag) AS ai_impacted_queries
FROM ai_search_visibility_table
GROUP BY week;

This produces trend-ready data.

Step 7: Looker Studio — Executive-Ready Visualization

Looker is where complexity must disappear.

Executives should see:

• Fewer charts
• Clear narratives
• Directional signals

No keyword tables. No vanity charts.

Step 8: Looker Calculated Fields

Looker lets you abstract complexity using calculated fields.

Example:

• AI_Suppression_Score
• Visibility_Risk_Level
• AI_Impact_Trend

Why This Dashboard Changes SEO's Role Internally

This elevates SEO into:

• Strategic planning
• Forecasting
• Risk mitigation

Teams that adopt this dashboard:

• Stop reacting late
• Detect AI shifts early
• Protect revenue proactively

Common Mistakes to Avoid

• Reporting rankings without CTR context
• Aggregating all queries together
• Ignoring informational queries
• Treating AI impact as "unmeasurable"
• Waiting for official AI metrics

AI doesn't wait. Neither should your reporting.

The Strategic Shift: From SEO Dashboards to AI Visibility Systems

That is a fundamentally different role.

And it's the role SEO teams must step into.

Final Thoughts: If You Can't See AI Impact, You Can't Manage It

AI search is not killing organic search.

It's changing how visibility works.

The teams that win are not those who:

• Chase rankings harder
• Publish more content
• Argue about attribution

They are the teams who:

• Measure AI interception
• Quantify visibility loss
• Communicate impact in business terms
• Act before traffic collapses

If your dashboard can't explain AI's impact on your business, it's already outdated.