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.
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").
Keeping Your Product Data Clean Isn’t a One-Time Fix
Fixing missing attributes with AI makes the process faster, but it’s not a one-time fix. As your catalog updates and platform requirements change, regular feed audits help prevent recurring issues. Using AI to flag gaps and suggest corrections keeps your data structured and listings compliant—ensuring your products stay visible and approved where it matters most.
Tired of fixing feeds manually? Let automation handle it for you. Get a free strategy session.
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