Standard Google Ads reporting works for AI Max, but Looker Studio (formerly Google Data Studio) enables richer visualizations, multi-source data blending, and custom dashboards that match AI Max's specific performance patterns. Building the right AI Max dashboard requires knowing which metrics are most meaningful for an AI-managed campaign type.
Connecting AI Max Data to Looker Studio
The data source connection is standard: Looker Studio > Create > Data source > Google Ads. Connect your Google Ads account (or MCC for multi-client), select the account, and use the Google Ads connector.
AI Max campaigns are accessible through the same Google Ads data connector as standard campaigns. Filter by Campaign Type = Performance Max (the API type for AI Max campaigns) or by campaign name (if you use a consistent naming convention).
Note: AI Max for Shopping data appears under Performance Max campaign type in Google Ads API / Looker Studio connectors. This labeling difference (the UI shows "AI Max" but the API reports "Performance Max") can cause confusion in dashboard filters.
Which Metrics to Prioritize in AI Max Dashboards
AI Max removes some metrics you'd track in keyword campaigns (keyword-level quality score, keyword bids, match type performance) and replaces them with different visibility points.
Primary AI Max metrics:
Conversion Value / Cost (ROAS): The headline metric. For AI Max with tROAS, show this vs. your target. Trending ROAS over time shows AI learning progress.
Cost / Conversion (CPA): For tCPA or lead gen campaigns. Show vs. target.
Budget Utilization Rate: Actual spend / daily budget * 100. Below 80% persistently indicates a bidding target issue, not a budget issue. This is a diagnostic metric standard dashboards don't show well.
Impression Share Lost to Budget: Available in Google Ads; shows what % of auctions were missed because budget ran out. High budget lost-to-budget with simultaneous underspend is contradictory and indicates a data quality issue worth investigating.
Search Impression Share: For AI Max for Search campaigns, this shows market coverage. Trending down means competitor pressure or budget constraints.
AI Max for Shopping additional metrics:
- Product-level revenue: Which SKUs are driving AI Max for Shopping revenue
- Shopping impression share: Coverage in Shopping auctions
- Channel split: Search vs. Shopping vs. Display vs. YouTube budget and conversion allocation (source: Insights panel export or GA4 data)
The June 2026 Deletion and Dashboard Date Ranges
The June 2026 data deletion (https://yositeup.com/blog/google-ads-reporting-data-deleted-june-2026) creates a visible discontinuity in any time-series chart covering that period. In Looker Studio, this appears as:
- A sharp drop-to-zero or near-zero in conversion counts for the deleted date range
- A corresponding ROAS anomaly (if conversions drop but cost data remains, ROAS tanks)
- A gap that makes YoY or MoM comparisons misleading
Dashboard configuration to handle this:
- Add a reference line annotation at the deletion dates: in Looker Studio chart settings, add a reference line at the deletion start date labeled "Data deletion" — users understand why there's a gap
- Create comparison date ranges that exclude the deletion period: Don't use "Compare to previous period" if the comparison period includes the deletion window. Use custom date ranges that skip the deleted weeks.
- Add a text box note on the dashboard explaining the June 2026 data gap and its cause for any readers who weren't aware of the event
AI Max Learning Phase Visualization
One of the most useful AI Max-specific charts: conversion rate or ROAS plotted week-by-week from campaign launch date. This shows the AI Max learning curve and helps stakeholders understand why the first 4-6 weeks look different from weeks 8+.
Build this chart: time series with week as dimension, conversion value/cost (ROAS) as metric, filter to your AI Max campaign. Use 7-day rolling average instead of raw weekly data to smooth noise.
Add a vertical reference line at "week 6" to mark where the learning phase typically ends — performance should stabilize after this point.
Blending GA4 and Google Ads Data in Looker Studio
AI Max data from Google Ads and GA4 data tell complementary stories. Google Ads conversion tracking shows what the AI is optimizing toward; GA4 attribution shows the full user journey.
In Looker Studio, blend these sources by joining on date:
- Left table: Google Ads AI Max campaign data (cost, clicks, conversions)
- Right table: GA4 data-driven attribution for the same date range
- Join key: date + campaign name (if consistent between sources)
This side-by-side view shows discrepancies between Google Ads last-click conversions and GA4 data-driven attribution — which is the most common conversation with clients who question AI Max's reported ROAS.
DSA Migration Tracking in Looker Studio
During the DSA-to-AI Max for Search migration (https://yositeup.com/blog/google-ads-dsa-ai-max-migration-february-2027), you're running both campaign types simultaneously. Build a side-by-side chart in Looker Studio:
- Left series: DSA campaign ROAS/CPA over time
- Right series: AI Max for Search ROAS/CPA over time
- Date range: from AI Max launch to DSA pause date
This gives stakeholders a visual performance handoff — when AI Max performance crosses above DSA performance, it's the evidence-based case for pausing DSA.
AI Max for Shopping Dashboard: Product Focus
For AI Max for Shopping (https://yositeup.com/blog/google-ai-max-shopping-replacing-performance-max-2026) dashboards, add a product performance table:
- Product title / ID from Merchant Center (join via the Merchant Center Looker Studio connector)
- Impressions, clicks, conversions, revenue per product
- Revenue contribution % (each product's revenue / total revenue)
This identifies your top 10% products that drive 80%+ of AI Max for Shopping revenue — the products where Merchant Center feed quality is most critical to maintain.
Reporting to Clients / Stakeholders About AI Max
Dashboards for non-expert stakeholders should suppress the technical detail and focus on business outcomes. Build a "client summary" page with:
- Total ad spend vs. budget (are we spending our budget efficiently?)
- Total conversions and conversion value (what are we getting?)
- ROAS vs. target (are we hitting our efficiency goal?)
- Week-over-week trend line (is performance improving?)
Avoid showing keyword-level data (doesn't exist in AI Max), quality score (irrelevant), or match type distribution (irrelevant) on client-facing dashboards. These metrics exist in standard campaigns; showing blank fields for them on AI Max dashboards causes confusion rather than clarity.
For clients managed under the July 2026 ToS (https://yositeup.com/blog/google-ads-tos-july-2026-ai-automation-what-changed): you can note in the dashboard footer that AI Max campaigns operate under the updated ToS provisions, documenting the framework under which the AI is operating.
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