dataclean.to

Clean Ecommerce Product Data from Excel Files

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12

Excel is where most product data lives before it reaches an ecommerce platform. Supplier price lists, internal inventory sheets, and catalog drafts all start as .xlsx files. But Excel introduces its own data quality issues: date serial numbers, scientific notation on long numbers, hidden characters from copy-paste, and cell formatting that masks the actual stored value. dataclean.to reads your Excel files and fixes these Excel-specific problems alongside standard product data issues.

The Problem

Excel silently converts UPC codes like 012345678912 to the number 12345678912, stripping the leading zero. Long SKUs display as scientific notation (1.23E+15). Dates pasted from different locales store as different serial numbers. Currency formatting on price cells hides the actual decimal precision. Hidden rows and filtered-out data may or may not appear in exports. Merged cells create blank values in exported rows. Microsoft Excel CSV import/export guide

How to Fix It

1
Upload your Excel product file
Import your .xlsx or .xls file directly. dataclean.to reads all sheets and lets you select which sheet contains your product data. No need to convert to CSV first.
2
Fix Excel data corruption
Restore leading zeros stripped from UPC, EAN, and ZIP codes. Convert scientific notation back to full numbers. Fix date serial numbers that display as integers instead of dates.
3
Resolve formatting vs. actual values
Identify cells where Excel formatting masks the real value: prices that show '$19.99' but store 19.989999. Ensure what you see matches what the data actually contains.
4
Standardize product data
Normalize column headers. Fill in values from merged cells. Remove hidden rows and blank rows. Standardize price formats, weight units, and category names across the sheet.
5
Export clean product data
Download as a clean CSV or Excel file with accurate values, no formatting artifacts, and properly typed columns. The file is ready for import into any ecommerce platform.

Frequently Asked Questions

Why does Excel remove leading zeros from UPC codes?
Excel treats cells as numbers by default. The UPC code 012345678912 becomes 12345678912 because Excel strips leading zeros from numeric values. dataclean.to detects these truncated identifiers and restores the leading zeros.
Can dataclean.to read multi-sheet Excel files?
Yes. The platform reads all sheets in an Excel workbook and lets you select which sheet to clean. You can process multiple sheets individually if your product data spans several tabs.
How are merged cells handled?
Merged cells export as one value followed by blank cells. dataclean.to detects merge patterns and fills the blank cells with the merged value so every row has complete data.

Example: Input → Output

nameskupricebrandrating
Wireless Headphones ProWHP-001$89.99SoundTech4.5
wireless headphones proWHP00189.99soundtech4.5/5

Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Wireless Headphones Pro",
  "sku": "WHP-001",
  "offers": {"@type": "Offer", "price": "89.99", "priceCurrency": "USD"},
  "brand": {"@type": "Brand", "name": "SoundTech"},
  "aggregateRating": {"@type": "AggregateRating", "ratingValue": "4.5"}
}
💡 How it works: Product schema enables rich results with price, availability, and star ratings in Google Shopping.

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