Google’s new AI shopping assistant offers faster, personalized, and intent-driven Shopping experiences, setting a new bar for data expectations.

For brands, a pickier shopping algorithm is both a challenge and an opportunity.

And Google's just the beginning. The emergence of AI assistants means algorithms will prioritize data relevance and scrutinize discrepancies more than when they were simply matchmaking.

Incomplete, messy, and even boring product data creates barriers to your products that algorithms can't overcome.

If you're an ecommerce manager, take this as a sign to make product data completeness and feed optimization a core step of your process. In this blog post, I show you how to:

1. Optimize your product feeds for AI.
2. Align with conversational search trends.
3. Expand across multiple sales channels for broader reach.

The Role of AI Sales Assistants in Shopping Decisions

AI sales assistants are reshaping how consumers discover products. Acting as intelligent filters, they streamline decision-making by delivering highly relevant, curated recommendations based on user intent.

For example, a shopper asking, “What’s the best running shoe under $150 with arch support?” will receive a tailored list that eliminates irrelevant options. This precision means brands with incomplete or poorly optimized product data risk being excluded from the results entirely.

The rise of AI assistants marks a shift from traditional browsing to intent-driven discovery, emphasizing the importance of optimized, AI-ready strategies for businesses to remain visible and competitive.

Key Factors Driving Product Visibility in an AI Ecosystem

AI assistants prioritize relevance, accuracy, and enriched product data when recommending products. Key drivers of visibility include:

Accurate Product Identifiers

GTINs, SKUs, and categories ensure the AI correctly identifies and surfaces your products.

Detailed Attributes

CPG Data Tip Sheet

Fields like size, material, and compatibility help match user intent with precision.

Conversational Keywords

Including phrases that align with natural language queries boosts discoverability (e.g., “lightweight waterproof jacket for hiking”).

Products lacking these elements are at a significant disadvantage, as AI algorithms filter out incomplete or irrelevant options.

Now that we understand how AI assistants influence product discovery, let’s explore actionable strategies to stay ahead in this evolving landscape.

Optimizing Product Feeds for AI

AI shopping assistants rely on detailed, accurate product feeds to deliver relevant recommendations. An optimized feed is your gateway to visibility in this AI-first ecosystem.

Focus on these core areas:

Populate Essential Attributes

Include GTINs, product categories, and custom labels to ensure products are correctly identified.

A complete set of identifiers sets up your products for AI discovery. Start with the basics:

• GTINs (UPC, EAN, ISBN codes) that uniquely identify each product
• Accurate product category paths that match marketplace taxonomies
• Custom labels for internal organization and campaign targeting
• Brand names and manufacturer part numbers
• Product type identifiers that align with standard classifications

These core identifiers work together to help AI assistants properly classify, filter, and recommend your products. Missing or incorrect identifiers can exclude your products from relevant search results and recommendations.

AI shopping assistants use these identifiers to validate product authenticity and ensure they're showing shoppers the exact items they're looking for. Proper identification also helps prevent your products from being miscategorized or confused with similar items.

Enrich Titles and Descriptions

Highlight key benefits and features, like material, size, or unique selling points.

A product attribute becomes enriched when you add detailed, accurate information beyond basic specs. This includes specific features, benefits, materials, dimensions, and use cases that help shoppers make informed buying decisions.

• Example 1: 
  • Basic attribute: "Color: Blue" 
  • Enriched attribute: "Color: Navy Blue with White Contrast Stitching"
• Example 2:
  • Basic attribute: "Material: Cotton" 
  • Enriched attribute: "Material: 100% Organic Egyptian Cotton, Pre-Washed for Softness"

Use High-Quality Images

High-quality product images serve a critical function in AI-driven shopping. Your product images help AI understand your products. 

AI tools like Google's shopping assistant can analyze and interpret images, using visual data to match products with customer queries and intent. This capability, known as visual search or image recognition, has become a powerful feature in ecommerce and online shopping experiences.

Using multiple high-quality images showing your product from different angles, in use, and in lifestyle settings helps AI better understand:

• Product features and attributes
• Real-world applications
• Size and scale context
• Color variations
• Material details
• Quality indicators

Poor images not only hurt conversion rates but can also limit your visibility in AI-powered searches and recommendations. Clear, detailed visuals that meet platform standards help ensure your products appear in relevant AI-assisted shopping queries.

Update Availability Data

Accurate inventory and pricing data directly impact your product visibility. When stock levels or prices are wrong, marketplaces may disapprove listings or, worse, suspend your account. Common issues include:

• Outdated inventory counts leading to overselling
• Price mismatches between your store and marketplaces
• Missing or incorrect shipping costs
• Promotional pricing is not properly reflected
• Variant availability not accurately tracked

Regular feed monitoring helps catch these issues before they affect sales. Automated feed management tools can:

• Check for data accuracy across channels
• Fill gaps by pulling information from related attributes
• Sync external data sources without changing your source catalog
• Monitor listing status and alert you to problems
• Validate pricing and inventory updates in real-time

While manual audits work, automated validation saves time and reduces errors. The right tools can maintain data accuracy across all your sales channels while preserving your source catalog's integrity.

Adapting to Conversational Search Trends

AI assistants interpret natural language queries, making conversational keywords essential for product discovery. For example, instead of “Men’s Jacket,” consumers may search for a “lightweight waterproof jacket for camping.”

Adjust your approach by:

Adding Benefit-Driven Keywords

Include details like price points (“under $50”) or product features (“machine washable”).

‍

Using Long-Tail Keywords

Reflect common conversational phrases in your product titles and descriptions.

[GoDataFeed App screenshot of: Adding use-case information into Description field]

Refining Product Descriptions

Answer questions shoppers may ask, like “Who is this product for?” or “What problem does it solve?”

Example: Change “Wireless Headphones” to “Noise-Canceling Wireless Headphones with 30-Hour Battery Life.”

Expanding Multichannel Presence

Diversifying your sales channels reduces reliance on any single platform, increasing visibility and conversions.

Here’s how to do it:

Sync Product Feeds Across Platforms

Optimize feeds specifically for each channel or marketplace. Amazon, Facebook, Instagram, Walmart, and Google Shopping should all use different templates and tactics. Then, ensure syncing is stable and continuous.

Leverage Performance Max Campaigns

Extend reach across Google’s ecosystem, from YouTube to Shopping Ads.

Use Data-Driven Insights

Analyze performance metrics to refine listings and reallocate budgets for maximum ROI.

Pro Tip: Segment products by performance tiers and tailor your strategy to focus on high-ROAS products first.

With these strategies in place, the next step is to implement them effectively. Let’s look at actionable steps to bring your AI-focused ecommerce strategy to life.

Audit and Improve Your Product Feeds

A well-structured product feed is essential for AI compatibility. Here’s how to get yours in shape:

1. Check Required Fields

Ensure GTINs, SKUs, and product categories are accurate and complete.

2. Remove Duplicate or Outdated Listings

Avoid penalization or confusion in AI-driven platforms.

3. Enrich Descriptions

Highlight benefits, features, and keywords that align with conversational search trends.

4. Optimize Images

Use high-resolution, platform-compliant images that showcase your product effectively.

Tools to Use:

• Feed management platform to automate enrichment and error resolution.
• Google Merchant Center reports to identify and resolve feed issues.

Adapt Content for AI-Driven Search Trends

AI-first ecommerce requires content that matches the conversational, benefit-driven nature of modern search.

Key tactics include:

Use Keyword Research Tools

 Identify conversational phrases relevant to your niche (e.g., “best gifts under $30” or “eco-friendly office supplies”).

Reformat Product Titles

Lead with benefits or key features, such as “Comfortable Memory Foam Pillow for Neck Pain.”

Answer Shopper Questions

Use your descriptions to address common queries like “What makes this product unique?” or “Who is this ideal for?”

Example: Replace generic titles like “Yoga Mat” with “Non-Slip Yoga Mat with Carrying Strap for Hot Yoga.”

Strengthen Multichannel Advertising Strategies

Broaden your reach and reduce risk by leveraging multiple platforms.

Steps to take:

Step 1

Synchronize Feeds Across Platforms

Automate feed updates for Amazon, Walmart, and Google Shopping to ensure consistency. Use a multichannel integration platform that can handle all of your channels to avoid having to deal with different automation protocols and formatting templates.

Step 2

Experiment with Ad Formats

Use Local Inventory Ads to attract nearby shoppers or Sponsored Products on marketplaces.

Step 3

Analyze Channel Performance

Using your product feed optimization tool, tag your product URLs with parameters like "source," "medium," "campaign," "term," and "content". Then, use Google Analytics to identify high-performing products, segment audiences, and reallocate budgets effectively.

Example: You run Performance Max campaigns on Google while running retargeting ads on AdRoll. How do you determine which ad closed the deal if both ads were seen or clicked? By tagging URLs with these parameters you give Analytics a clearer picture of your shoppers:

• Original product URL: 
  • https://www.yourstore.com/product/summer-dress
• With UTM parameters: 
  • https://www.yourstore.com/product/summer-dress?utm_source=google+shopping&utm_medium=organic+search&utm_campaign=pmax
  • https://www.yourstore.com/product/summer-dress?utm_source=adroll&utm_medium=retargeting&utm_campaign=summer_sale

Be sure to tag URLs and monitor performance data in Google Analytics before adjusting bid and budget strategies.

Making Your Products AI-Ready

Artificial intelligence is changing the way we do most things online and shopping is no exception. Sellers who prioritize AI-focused optimization will have the advantage. 

Quality product data drives sales. When you combine detailed listings, natural language optimization, and strategic channel diversity, you build a foundation that works today and scales for tomorrow.

The time to optimize is now. Your customers are already shopping with AI.