Optimizing Your Google Feed with AI

Your product feed is the foundation of every Google Shopping campaign, but what happens when product information is missing or inconsistent? At Channable, we’ve been exploring the ways it can help to optimize your Google Feed with AI and the results are in.

In this blog, we’ll show you how to use AI to optimize your Google Feed. Using a real-life example, we’ll demonstrate how you can streamline your product data and create a continuous performance feedback loop that drives measurable results. We’ll show you how we’re already making it work for a European eCommerce leader.

The article is based on a presentation delivered by Ruben Runneboom, a Google Ads Expert at eC-Commerce experts Taskforce for one of our IMPACT cube events.

You’ll learn how to leverage AI for an optimized Google Feed. Discover techniques for creating AI-friendly content to increase your search visibility, and drive more traffic.

Key takeaways

• Automate feed optimization with AI-driven insights

• Build a feedback loop between data, performance, and product titles

• Use real search data to create high-performing, AI-optimized product feeds

• Learn how to test, refine, and scale your results
  

Why Your Google Feed Matters More Than Ever

A product feed isn’t just a list of SKUs, it’s a guide to Google about when, where, and how to show your products to customers. Experience has shown us that Google’s AI-powered Shopping system rewards accuracy, context, and consistency.

How’s yours?

The truth is that most product feeds are full of problems. They’re often static, inconsistent, and out of sync with how people actually search.

Traditional feed management relies on manual rules: “If X, then Y.” AI can follow the rules, but its real power is in its potential to learn from data. Over time, the system can build up detailed insights into customer behaviour that can be used to optimize ads.

The data team at Channable wanted to answer a simple question: Can we build a self-optimizing feed that improves automatically based on real-world performance?

The team set out to test if they automatically added unused product attributes to titles using real search data and a continuous feedback loop, could they boost ROAS and CTR by at least 10%?

To test it, the team used a real account from European medical supply company Medisave.

Here’s how it went…

How AI Feed Optimization Works

Here’s a step-by-step guide on how our team used AI to optimize a product feed for Medisave.

1. Combining and enriching data

The team started by merging data from multiple sources, including:

• Channable export: SKU, title, description, category, brand

• Markifact search terms: Organic queries per SKU

• Google Ads data: Performance metrics per SKU

• Master product spreadsheet: Core feed data

This foundational data enabled the AI system to understand the relationship between product attributes and search intent.

2. Generating optimized alternatives with OpenAI

Using N8N and OpenAI, the system generated two alternative titles per SKU. These were based on Google Shopping title best practices, highlighting key product attributes, and using the most effective keywords and conversion patterns.

3. Test, measure, and select winners

Each SKU was duplicated into three variants to enable the team to test the impact. The variants included:

• Variant 1: Optimized title 1

• Variant 2: Optimized title 2

• Variant 3: New image variant

The system tested these versions in Google Ads through a dedicated test Merchant Center account.

The best-performing titles and images were then automatically applied to the main account to maximise impact.

The results: AI vs Human Optimization

To help us quantify impact, we set a standard rule: Products with a minimum £5 spend and at least a 10% improvement in ROAS and conversion value were classified as winners.

The team ran the test across 80 SKUs across the UK, USA and Europe.

• The system generated 21 winning variations

• Unadjusted products averaged a 7.4 ROAS and 1% CTR

• AI-optimized products averaged a 10.1 ROAS and 1.7% CTR

The test proved that an automated feedback loop can outperform manual optimization. Even better, the impact was delivered quickly.

While it’s only one test, it demonstrates the impact AI can have. The system created a Shopping feed that can learn, adapt, and refine to improve ROAS and CTRs.

We proved that our initial hypothesis was right. If it works for Medisave, it can work for you.

Building a Continuous Feedback Loop

AI is a learning system, and the more information you feed it, the better it can become.

Once you have a working feed optimization process, the next step is connecting it to a continuous feedback loop that can continue to learn and improve.

Here’s how you can create a continuous feedback loop:

1. Feed performance data back into your model (CTR, ROAS, ROI, conversions)

2. Use AI to detect underperforming titles and automatically generate replacements

3. Run controlled tests before pushing changes live

4. Integrate search term data from DSA campaigns to refine targeting

This cycle ensures that the evolution and optimization of your feed is based on real data, not assumptions.

You can learn more about how AI is transforming feed optimization and the impact on digital marketing in Channable’s Google Ads Growth Strategy.

Conclusion

We set out to test whether AI can give you an advantage, and we were proved right. We established that optimizing your Google Feed with AI can boost ROAS and CTR by at least 10%.

We know AI holds a lot of promises, but we’re not talking about the future here. AI-powered feed management can help you scale performance, reduce waste, and stay ahead of the competition today.

If you want a full walkthrough of advanced feed and ad automation, read Channable’s Google Ads Growth Strategy.