How to Extract Key Attributes From Product Names in Batch in Excel
Catalog spreadsheets often contain long product-name strings that hide the fields you actually need for filtering and merchandising. BatchGPT helps you pull those attributes out with one repeatable prompt across your selected rows.
Why Product-Title Parsing Is a Good BatchGPT Workflow
The logic repeats for every row: find the brand, identify the model, capture the color or size, and keep missing values explicit. That makes attribute extraction a strong fit for a controlled spreadsheet workflow.
How to Use the BatchGPT Excel Add-in for This Workflow
- Write the prompt that tells the add-in what to do with each selected cell value.
- Select the Excel cells or range you want to process. For larger datasets, work in clean batches of rows.
- Choose the output column and adjust optional settings such as reasoning effort or web search when the task really needs them.
- Click Generate so the add-in processes each selected cell separately and writes the result to the output column you chose.
- Review the results in Excel, refine the prompt if needed, and rerun only the rows that need another pass.
Prompt Example for Product Attribute Extraction
Specify the fields and the null behavior so the output stays consistent even when titles are messy.
Extract product attributes from this product title:
Return labeled output with:
- brand
- product_type
- model
- color
- size
- quantity_pack
Rules:
- Use null when a value is missing
- Do not invent details
- Keep values concise and standardizedSample input row:
A2: Nike Air Zoom Pegasus 40 Mens Running Shoes Blue Size 11Sample output row:
brand: Nike
product_type: running shoes
model: Air Zoom Pegasus 40
color: blue
size: 11
quantity_pack: nullHow to Improve Catalog Quality With Structured Attribute Output
Attribute extraction is most valuable when the output fields match the way your catalog is actually used.
- Use standardized field names and casing so the results are easier to reuse in filters and feed preparation.
- Keep the extracted attributes together in one output block if you want a quick review surface inside Excel.
- Sample rows from different brands or title formats before scaling to a wider selection.
- Treat borderline cases as review rows instead of letting the prompt guess on weak evidence.
FAQ
Can I extract multiple attributes in one run?
Yes. Ask for all required fields in one output block and BatchGPT will return them together for each selected row.
What if product names are inconsistent?
Use null rules, standardized field names, and sample testing across your messiest titles before scaling up.
Can this help marketplace feed quality?
Yes. Clean attributes can improve feed consistency, filtering, internal search, and merchandising workflows.
Should I let the model infer missing attributes?
Usually no. It is better to return null or review_flag for missing values than to invent details that are not present in the row.
Normalize Catalog Titles Without Leaving Excel
If your catalog lives in spreadsheets and each title needs the same parsing logic, BatchGPT is a practical fit. Define the fields once, process the selected titles, and review the extracted attributes beside the original row data.
Get started!