Fixing Missing Product Attributes with ChatGPT

You upload your product feed, hit submit, and then—error messages. Google Merchant Center, Amazon, or Meta flags missing GTIN, MPN, brand, color, or size and suddenly, your products aren’t showing up where they should.

How Missing Attributes Weaken Listings

If your products are being flagged by seller platforms, it means the missing attributes aren’t just optional details; they’re essential for getting your listings approved, improving search visibility, and ensuring accurate product matching. 

Channel algorithms rely on structured data to sort and index products and match them to what shoppers are looking for. Incomplete feeds interrupt this process, and it’s one of the most common reasons for disapprovals, low rankings, or even suspensions.

In this post, we show you how to find missing attributes in your product feeds, how to fix them using AI tools like ChatGPT, and prevent incomplete feeds from slowing down your ecommerce sales in the future. Let’s get started.

Before Getting Started: Identify What’s Missing

Before fixing anything, figure out where the gaps are.

Check platform diagnostics – Google Merchant Center, Amazon Seller Central, and Meta Commerce Manager all provide error reports.

Export your feed – Open it in a spreadsheet and scan for blank fields in required columns.

Use AI tools (like ChatGPT) – Ask AI to analyze a sample of your data and highlight missing or inconsistent fields.

Using AI to Identify Missing or Inconsistent Product Attributes

Manually reviewing product feeds for missing GTINs, MPNs, brands, colors, or sizes can be tedious—especially when dealing with hundreds or thousands of SKUs. AI tools, like ChatGPT, can assist by quickly analyzing your data, identifying gaps, and even suggesting fixes. Here’s how to leverage AI to streamline the process.

Step 1: Extract and Prepare Your Data

Before using AI, you need a structured dataset.

Export your product catalog as a CSV or spreadsheet from Shopify, WooCommerce, BigCommerce, or your feed management tool.

Focus on relevant fields: Product ID, Title, GTIN, MPN, Brand, Color, Size.

Save it as a clean, structured table (Google Sheets, Excel, or plain text format).

Step 2: Feed the Data into ChatGPT (or Another AI Tool)

Since ChatGPT can’t process entire spreadsheets directly, use a formatted text approach:

1. Copy a small sample (e.g., 10–20 rows) from your dataset.

2. Paste it into ChatGPT in tab-separated or JSON format for clarity. 

Example:

3. Prompt the AI to perform the task. The more detailed you can be about your needs, the better.

Some examples:

“Analyze this product data and identify missing or inconsistent attributes required by Commerce Manager. Also point out any missing attributes that are highly recommended for optimal visibility and engagement.”

“Suggest possible GTINs, MPNs, or brand names for missing values, ensuring perfect taxonomy alignment with Google’s product classification system.”

“Suggest replacement color options for non-standard colors. Reformat sizing values according to Amazon Seller Central’s best practices.”

Step 3: Review AI Findings

✅ If ChatGPT identifies missing GTINs & MPNs 

AI might suggest looking up GTINs based on brand and category. If AI can’t provide a direct match, it will recommend where to source this information (e.g., manufacturer websites, GS1 databases).

✅ If ChatGPT flags inconsistencies 

AI can detect inconsistencies in your dataset, such as product variations missing attributes while others include them. Standardizing these fields prevents listing errors and improves filtering on marketplaces.

✅ If ChatGPT recommends formatting fixes 

Platforms have strict attribute formatting rules, and improper values can cause feed errors or rejections. AI can suggest formatting corrections like expanding abbreviations (e.g., “blk” to “Black”) or adjusting size conventions (e.g., “SM” to “Small”).

Step 4: Apply Fixes & Validate

Unfortunately, even ChatGPT has its limits. AI can highlight missing fields, flag inconsistencies, and even suggest standardized formats, but it can’t do the work for you. Not without a data feed platform to automate the updates and ensure the changes made are reflected properly on the channel side. 

To apply the fixes on your feed, follow these steps:

Warning: Use AI’s recommendations as a guide, but make sure to personally/manually vet all changes before committing to them. Trust me, you don’t want to inject a bunch of hallucinated product data into your feeds.

Fill Informational Gaps

1. For GTINs and MPNs, cross-reference with manufacturer databases, barcode lookup tools, or supplier documentation.
2. For color and size inconsistencies, apply standard naming conventions across all SKUs (e.g., “Dark Blue” vs. “Navy”).
3. Use platform-specific formatting to ensure compliance with Google, Amazon, Meta or other channel requirements. Tip: Using a data feed platform would automate the compliance process.
4. Standardize your brand names, product types, and size formats across all listings to avoid rejections.

Enrich Data

1. For GTINs/MPNs, cross-reference manufacturer databases—AI can’t generate these but can help direct your search. 
2. If GTINs are unavailable, apply for exemptions in Google Merchant Center or Amazon’s Brand Registry.
3. Use AI to suggest category mappings, but confirm selections against each platform’s taxonomy (e.g., Google’s Product Categories).

Submit New Feed

1. Upload the revised feed to Google Merchant Center, Amazon, or Meta.
2. Run a test submission to catch any lingering formatting issues before the feed goes live.
3. Review platform diagnostics post-submission to ensure all required attributes are properly mapped.

Step 5: Do It Again Next Week

AI helps speed up the error-checking process, but final decisions require human oversight. Product data isn’t static—inventory changes, new SKUs get added, and platform requirements evolve. What’s correct today might be outdated next week. 

A well-structured feed ensures:

• Product approval – Fewer disapprovals, smoother approvals.
• Better ad performance – Structured data improves ad targeting and bidding efficiency.
• Higher search visibility – More accurate product categorization leads to better rankings.

Ongoing Feed Health Monitoring 

Fixing errors once isn’t enough—feeds need regular maintenance to stay accurate and compliant. Scheduling routine audits keeps your data optimized and prevents recurring issues before they impact performance.

Here are a few steps you’ll want to add to your product feed management routine:

• Schedule weekly audits to catch new missing attributes as your catalog updates.
• Compare feed performance over time
  • Document pre-optimization benchmarks to establish a baseline:
    • Past 7 days – I prefer to extend this to the past 4 weeks as separate reports to track natural week-to-week fluctuations.
    • Past 30 days – I also compare this against the same period from the previous year to account for seasonal trends.
    • Same period last year – This comparison helps separate true growth or decline from seasonal fluctuations. If sales are up, is it due to optimizations, or is it just a naturally stronger time of year? If sales are down, is it a real issue or just part of an annual cycle?
    • Year to date (YTD) – Gives a broader view of overall performance trends.
    • Past 12 months (or rolling 12 months) – Helps identify long-term patterns and anomalies.
  • Are attribute improvements leading to more impressions?
  • Have engagement KPI like CTR and engagement rate gone up? 
  • Are conversions picking up?

• Save AI prompts tand continue to modify them as you become more familiar with the process. 
• Refine product listings based on marketplace trends and updates.

Correcting Errors Is One Thing, Preventing Them Is Another

If you’re a small merchant or just getting started, using AI to fix feeds can work—for now. But as you scale, spending hours every week fixing Missing Product Attributes (even with ChatGPT) isn’t efficient.

That’s why ecom managers who have done this for some time—including yours truly—opt for a data feed managing app. I use GoDataFeed because it’s pretty flexible and allows me to manage small clients as well as very large catalog clients, but there are many options, each with their own benefits and limitations. I highly suggest you test different options.