Cleaning Excel Export Data: Fixing SKU and Currency Bugs

Importing catalog data into spreadsheet software often introduces formatting issues. Excel may convert long numerical SKUs into scientific notation (like `1.2E+11`) or merge currency symbols with numeric values, which can break your import files. Learning how to clean Shopify Excel data keeps your databases accurate.

Common Spreadsheet Formatting Bugs

• Scientific SKU Notation: Occurs when Excel formats long numeric identifiers as numbers instead of plain text.
• Currency Notations: Symbols (like `$`) in price columns can prevent your platform from parsing numeric values correctly.
• Broken Handles: Spaces or special characters in handle columns can break your page URLs.

Preventing Spreadsheet Errors

To avoid these issues, format your sheet columns as text before pasting data. If you use browser extensions to export your files, ensure you download clean, formatted spreadsheets that protect SKU fields and numeric pricing values automatically.

Data Cleanup Workflow

Cleaning Excel Export Data: Fixing SKU and Currency Bugs is most useful when the export is clean enough to filter, import, or hand to another team without extra explanation. Shopify catalog data can include nested variants, HTML descriptions, image arrays, tags, vendors, product types, SKUs, barcodes, weights, and sale pricing. A structured cleanup pass turns that raw data into something you can trust for migration, reporting, or product research.

Columns to Validate

• Identifiers: Handles, SKUs, barcodes, and variant IDs should remain stable and should not be reformatted by spreadsheet software.
• Variant fields: Option names and option values should stay aligned so size, color, material, and other choices do not shift columns.
• Content fields: Product descriptions, body HTML, SEO titles, and SEO descriptions should preserve formatting where the destination workflow needs it.
• Media fields: Image URLs should be checked for broken links, duplicate rows, and variant-specific image relationships.

Recommended Process

1. Export the catalog and save a raw backup before making edits.
2. Open a working copy in Excel, Google Sheets, or your preferred data tool.
3. Freeze the header row and confirm each column has a clear purpose.
4. Normalize blank values, duplicate handles, currency formatting, and unexpected HTML fragments.
5. Run filters for missing SKUs, missing prices, empty images, and inconsistent option names.

Quality Checks Before Using the File

Before importing or analyzing the file, sample products from different parts of the catalog: a simple product, a multi-variant product, a discounted product, and a product with several images. Check that each row still belongs to the right parent product and that spreadsheet auto-formatting has not damaged SKUs, barcodes, or long numeric IDs.

Common Cleanup Mistakes

• Letting spreadsheet software convert barcode or SKU values into numbers.
• Deleting blank-looking columns that are required by an import template.
• Sorting only part of the sheet and breaking the relationship between products and variants.
• Editing HTML descriptions without checking how they render on the destination platform.

Example Cleanup Checklist

For Cleaning Excel Export Data: Fixing SKU and Currency Bugs, add a QA tab with checks for missing titles, duplicate handles, blank prices, broken image URLs, empty option values, and SKUs that changed format. Mark each issue as fixed, accepted, or needs review. This keeps cleanup decisions visible instead of hiding them inside ad hoc spreadsheet edits.

FAQ

Should I edit the raw export? Keep the raw export untouched and work from a copy. That gives you a clean fallback if formulas, sorting, or imports go wrong.

What causes most CSV problems? Broken variant relationships, spreadsheet auto-formatting, missing required columns, and HTML content pasted into the wrong field are the usual causes.

Next Steps

To put Cleaning Excel Export Data: Fixing SKU and Currency Bugs into practice, start with one focused Shopify store and one clearly defined question. For example, decide whether you are checking prices, validating product ideas, preparing a migration, or cleaning catalog data. Export the catalog, review the fields that matter to that question, and write down the decision you will make from the result.

After the first pass, repeat the same workflow on a second store or a later snapshot. Comparing two clean exports is usually more useful than collecting a large amount of messy data. Keep your spreadsheet simple, document the filters you used, and save the raw export separately so your analysis can be checked or repeated later.