Why ROAS Works Differently in the AI Era of Google Ads (Post-Andromeda Evolution)

Introduction: ROAS Isn’t What It Used to Be

For years, Return on Ad Spend (ROAS) has been the holy-grail metric for performance marketers a simple equation that tells you how efficiently every rupee or dollar in ad spend converts into revenue.

But in 2026, that equation has quietly changed.

With Google’s ad ecosystem now powered by AI automation, cross-channel attribution, and real-time learning models (the so-called “Andromeda evolution”), traditional ROAS metrics no longer tell the full story.

The old question “How much revenue did I get for my ad spend?” is being replaced by a smarter one:

“How much predictive value did my ad spend create across the customer journey?”

Let’s unpack why ROAS behaves differently today, how Google’s AI is re-defining attribution, and what modern brands must do to measure profit accurately in this new era.

1. What Traditional ROAS Really Measures

Classic ROAS was simple:

ROAS = Revenue from Ads ÷ Ad Spend

If you spent ₹100 and earned ₹400 in attributed sales, your ROAS = 4.0 (400%).

This model worked when:

• Campaigns were channel-specific (Search vs Display vs YouTube).
  
• Conversions were mostly single-device, single-session.
  
• Attribution was “last click.”
  
  

The problem? None of that reflects how users behave in 2026.
They research on mobile, watch videos on YouTube, read reviews on desktop, and convert later through a remarketing ad  sometimes days or weeks after the first touch.

2. The “Andromeda” Shift: AI Unifies Everything

Google’s current advertising backbone (Performance Max, Smart Bidding, and Gemini AI) represents a unified, AI-driven architecture often referred to by marketers as the Andromeda evolution.

This system:

• Blends Search, YouTube, Display, Shopping, Discover, and Maps into one intelligence layer.
  
• Uses machine learning to predict user intent before the click.
  
• Allocates budget dynamically across channels.
  
• Attributes conversions using multi-touch and predictive models, not just direct response.
  
  

Impact on ROAS

ROAS is no longer static or channel-bound.
It now reflects probabilistic contribution what each impression, view, or click likely adds to revenue over time.

So when you see a 3.8× ROAS in your dashboard, that figure already includes:

• Assisted conversions from YouTube views.
  
• Post-view revenue predicted from remarketing audiences.
  
• Offline or app-driven actions synced via Conversion API.
  
  

Spinta Insight:

ROAS has evolved from a snapshot metric to a forecasting metric.

3. The Rise of Predictive and Modeled Conversions

Since browser privacy updates and iOS 14.5, first-party data has become the backbone of ad optimization.
To fill tracking gaps, Google Ads now uses modeled conversions AI-estimated actions based on partial data.

Why It Matters for ROAS

• Your ROAS today may include conversions Google’s AI predicts will occur later.
  
• This makes ROAS more accurate over time but less instantaneously precise.
  
• Comparing ROAS week-to-week without context can mislead decisions.
  
  

What You Should Do

• Track Conversion Value Lag how long it takes for assisted conversions to finalize.
  
• Use data-driven attribution (DDA) instead of last-click.
  
• Layer first-party CRM revenue into Google Ads to validate modeled results.
  
  

4. Multi-Touch Attribution Redefines “Value per Click”

In the pre-AI era, one click = one opportunity.
Now, one click can trigger a dozen signals: engagement depth, content interest, device pattern, lifetime value prediction, and more.

Andromeda-style attribution assigns fractional credit to every meaningful interaction.

For example:

Traditional ROAS would have counted only the Search Ad Click.
AI-attributed ROAS now factors in the entire journey giving a truer sense of efficiency.