AI Max for Shopping: What's New for Online Stores

AI Max for Shopping is an evolutionary step in Google Ads designed to optimize campaigns and better respond to complex, context-dependent search queries from modern users. This tool transforms static product data into dynamic, hyper-relevant ads, allowing online stores to compete more effectively for the attention of potential buyers. It promises not only to expand reach, but also to improve performance while maintaining the necessary level of control.

New AI Max capabilities for optimizing shopping campaigns

Google AI Max for Shopping is designed to bridge the gap between traditional ad formats and the way people search for products today, using more natural, conversational queries. The main goal is to make ads as relevant as possible for each specific search, resulting in higher user engagement and improved business outcomes.

Text customization: improving ad relevance

This feature allows Google AI to dynamically update the text elements of your ads, including product headlines, so they better match search queries and user intent. Using information from your product feed in Merchant Center, the system automatically generates headlines predicted to improve ad performance. This is especially useful for capturing long-tail, highly specific queries that may previously have been overlooked by standard campaigns.

• How it works: AI analyzes the user’s query and compares it with your product attributes. Based on this analysis, it creates the most relevant headline, which may differ from the original headline in your feed.
• Benefits: Increased CTR (Click-Through Rate) due to higher relevance, improved quality of search queries that trigger the ad, and reach for queries that may not have previously activated your ads.
• Practical example: If a user searches for “light waterproof jacket for running in the rain” and your product headline is “Sports Jacket,” AI Max can turn it into “Light Waterproof Jacket for Running in the Rain,” significantly increasing the chances of a click.

Final URL Expansion (FUE)

Final URL Expansion (FUE) takes your campaigns beyond standard shopping ads, allowing them to appear as text ads that link to the most relevant pages on your site, not just a specific product page. The system scans your website to find other high-value pages, such as category pages, new arrivals, or topical articles, that match the user’s search intent.

• Mechanics: FUE enables hybrid ad formats (text and shopping) as well as dynamic landing pages. This helps capture users at different stages of the sales funnel, including the research stage.
• Requirements: To activate FUE, text customization must be enabled. This ensures that the ad text matches the content of the landing page. This feature is also available only with the Target ROAS bidding strategy.
• Benefits: The ability to appear for broader or research-oriented queries, capture high-intent traffic that may previously have been missed by campaigns, and improve Conversion Rate (conversion rate) metrics through more relevant landing pages.
• When it makes sense: Ideal for products where users first explore categories or look for solutions rather than specific models. It is also useful for online stores with a large number of products, where it is difficult to manually optimize every page.
• Common mistakes and fixes:

  • 404 errors on dynamic pages: If you use tracking templates, they may conflict with dynamically generated URLs. It is important to check the compatibility of tracking templates with dynamic URLs before enabling FUE.
  • Loss of tracking parameters: Some parameters, such as {product_id}, may expand to an empty string when using FUE. If your conversion attribution system depends on this parameter, it can lead to inaccurate tracking.
  • Solution: It is recommended to use a “hybrid URL strategy” with tracking templates at the campaign level. Parameters such as {extensionid} and {adgroupid} should be appended to {lpurl} to preserve detailed product-level tracking and avoid issues with dynamic routing.

Optimal ad format selection

Final URL Expansion also unlocks automatic selection of the best ad format — whether text or shopping — depending on what is most relevant for a specific user search intent. This ensures maximum ad delivery efficiency across different scenarios.

• How it works: Google Ads analyzes the user’s query and likely behavior to determine which ad format (a text ad linking to a category or a shopping ad linking directly to the product) will deliver better engagement and conversion rates.
• Benefits: Optimized advertising costs (CPC, Cost Per Click), higher overall campaign conversion rate, and better use of budget.
• When it is useful: This feature is especially valuable for large online stores with broad assortments, where different types of queries may require different ad delivery approaches.

AI Max for Shopping vs Performance Max: what has changed

AI Max for Shopping is the next generation of shopping campaign optimization, differing from Performance Max (PMax) in its focus.

• Focus on shopping placements: Unlike Performance Max, which distributed budget across all Google channels, AI Max for Shopping focuses exclusively on shopping surfaces: the Google Shopping tab, shopping ads in search results, and YouTube placements with shopping intent. This means your budget is directed where users have the highest shopping intent.
• Content generation from feeds: Performance Max used uploaded assets (images, texts), while AI Max for Shopping generates creatives directly from the product feed in Merchant Center. This makes your product feed a key element in creating effective ads.
• More detailed product-level ROAS optimization: AI Max for Shopping uses performance signals at the individual product level, which can improve overall ROAS, especially for accounts with diverse product categories.
• Simplified campaign structure: AI Max for Shopping reduces the need for manual setup, relying more on feed data, which simplifies management for advertisers.

Core elements preserved

It is important to note that AI Max for Shopping preserves many key elements that previous Google Ads solutions were built on:

• Smart Bidding: Automated bidding strategies remain the core mechanism.
• Merchant Center feed: This is still the foundation for creating ads.
• Audience signals: You can continue using audience signals to improve targeting.
• Conversion tracking: Conversion tracking setup remains unchanged.

Practical steps to prepare for and implement AI Max

To get the most out of AI Max for Shopping, it is recommended to take several steps:

1. Optimize the Merchant Center feed:

   • Ukrainian-language headlines: Make sure your product headlines include the key search terms used by Ukrainian users. If your main audience searches in Ukrainian, high-quality Ukrainian-language headlines are critical.
   • Detailed descriptions: Add full and informative product descriptions that include features, benefits, and possible use cases.
   • High-quality images: Use images that meet Google’s requirements, show the product from different angles, and attract attention.
   • Accurate GTINs: Check the correctness and completeness of Global Trade Item Numbers (GTINs), if they apply to your products.

2. Testing features:

   • Start with text customization: This is the lowest-risk and easiest feature to measure. Set aside a separate segment (for example, a product category or campaign) for testing and compare CTR and conversion lift against the baseline.
   • Limit FUE and format selection: Before enabling Final URL Expansion and Optimal format selection, clearly define exactly where users will be sent and which existing campaigns may lose traffic or conversions. If that is difficult to determine, you may not be ready to implement them yet.

3. Audit campaign overlap:

   • If you already use Performance Max, broad search campaigns, or dynamic search ads (DSA), ask yourself: where will AI Max for Shopping bring additional revenue, and where will it simply cannibalize traffic from other campaigns in the same account?

4. Migration planning:

   • Avoid peak seasons: Do not launch new campaigns or major changes during the busiest sales periods (for example, Black Friday or holiday sales), as campaigns need time to learn.
   • Conservative targets: Set more conservative ROAS targets (possibly 10–15% below your current targets) for the first 4–6 weeks after launch to give the system time to optimize.

5. Tracking results:

   • Document your current campaign results (ROAS, Conversion Rate, CPA) before implementing AI Max. This will help you objectively assess performance gains.
   • Separate goals that are not directly tied to sales. If Performance Max was used for brand reach or awareness, those goals should be handled in separate, specialized campaigns.

When AI Max is a fit — and when to wait

AI Max for Shopping is a powerful tool for online stores aiming to improve the performance of their shopping campaigns in today’s competitive environment and as user search behavior evolves. It is especially suitable for:

• Online stores with large assortments: Automation and feed-based content generation greatly simplify management.
• Businesses looking to increase ROAS: Focus on shopping surfaces and product-level optimization can deliver significant gains.
• Brands that want to better match complex, conversational queries: Text customization and FUE allow you to cover a wider range of search intents.

However, if your business is just starting out with online advertising, or if you already have very narrow, highly optimized campaigns, it may be better to thoroughly test AI Max on a limited segment first before fully switching to it. It is also important to have a clear understanding of your current campaign structure and potential overlap to avoid performance losses.

Google AI Max for Shopping represents a major step forward in shopping campaign optimization. Understanding how it works, preparing your data properly, and implementing it strategically will allow online stores to get the maximum benefit from this innovation, driving sales growth and improving performance metrics.