Google adds cross-campaign testing with new Mix Experiments beta

Google is rolling out Campaign Mix Experiments (beta), a new testing framework that lets advertisers experiment across multiple campaign types, budgets, and settings within a single, unified experiment.

How it works:

• Advertisers can create up to five experiment arms, each containing a different mix of campaigns.
• Campaigns can appear in multiple arms, with traffic split between them.
• Experiments support Search, Performance Max, Shopping, Demand Gen, Video, and App campaigns (excluding Hotels).
• Traffic splits can be customized (minimum 1%), with results normalized to the lowest split for fair comparison.

What you can test:

• Budget allocation across campaign types
• Account structure, including consolidation vs. fragmentation
• Bidding strategies, targeting, and feature adoption
• Cross-channel performance interactions, not just single-campaign lift

Why we care. Instead of testing Search, Performance Max, Demand Gen, or Video campaigns in isolation, advertisers can now see how different campaign types work together — and which mix actually drives the best business results.

Reporting details. Results appear in the Experiment summary and campaign-level reporting, with advertisers able to choose confidence intervals (95%, 80%, or 70%) and primary success metrics like ROAS, CPA, conversions, or conversion value.

Best practices:

• Keep experiment arms similar, changing only one variable at a time.
• Align total budgets across arms unless budget is the test itself.
• Avoid shared budgets and major in-flight changes.
• Run experiments for at least six to eight weeks to reach statistical reliability.

Between the lines. This is Google acknowledging that modern performance isn’t about winning one campaign — it’s about finding the right mix, especially as automation blurs the lines between channels.

Bottom line. Campaign mix experiments give advertisers a clearer, more realistic way to test how different campaign types and budgets work together — and make smarter decisions about where spend actually delivers incremental value.

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