How to Audit Your Shopify Product Catalog for Missing Textile Data

Last updated: October 20, 2026

The fastest way to audit a Shopify apparel catalog is to export all products with their metafields, define the fields every product must have, and flag every row that is blank, inconsistent or uses a non-compliant term. A catalog of thousands of variants can be checked in an afternoon with a spreadsheet or a short script.

Step 1: Define the fields every product needs

Step 2: Export your catalog

Export products from the Shopify admin as a CSV. Depending on your plan and tools, the native export may not include every metafield. If columns are missing, use an export app or the Admin API. Keep one row per product for composition and origin checks, and one row per variant where data differs by variant.

Step 3: Run the checks

1. Blanks. Filter each required field for empty cells.
2. Percentages. Confirm fiber percentages add up to 100.
3. Names. Search for non-FTC fiber names: viscose (use rayon), elastane (use spandex), polyamide (use nylon).
4. Order. Check that fibers are listed from highest to lowest percentage.
5. Consistency. Compare composition and origin between the title, description and metafield. Flag differences.
6. Claims. Search descriptions, tags and titles for "eco", "sustainable", "organic", "recycled", "recyclable" and "Made in USA", then check each against evidence.
7. Sample the physical label. Compare 5–10% of styles against actual garments or label artwork.

Step 3b: Automate it with a script

This Python example reads an export and flags common problems. Adjust the column names to match your file.

import pandas as pd

df = pd.read_csv("products_export.csv")

REQUIRED = ["Material composition", "Country of origin (display)", "Care instructions"]
BAD_TERMS = ["viscose", "elastane", "polyamide", "cotton blend"]

issues = []
for _, row in df.iterrows():
    handle = row.get("Handle")
    for col in REQUIRED:
        if col not in df.columns or pd.isna(row.get(col)) or str(row.get(col)).strip() == "":
            issues.append((handle, f"Missing: {col}"))
    comp = str(row.get("Material composition", "")).lower()
    for term in BAD_TERMS:
        if term in comp:
            issues.append((handle, f"Check term: {term}"))

pd.DataFrame(issues, columns=["Handle", "Issue"]).to_csv("audit_issues.csv", index=False)
print(f"{len(issues)} issues found")

Step 4: Prioritize fixes

Rank products by revenue and by sales into California, and fix those first. Group issues by supplier, because one supplier's missing data often affects many products.

Step 5: Fix at the source

• Ask suppliers for fiber test reports and label artwork
• Update metafields in bulk through import or the bulk editor
• Update physical labels in the next production run if they are wrong, not just the web page
• Record each decision so that you can explain it later

Step 6: Prevent new gaps

• [ ] Make the required fields mandatory in your product-onboarding checklist
• [ ] Add the checks above to your pre-launch review
• [ ] Rerun the audit twice a year
• [ ] Assign one owner for textile data

Frequently asked questions

How long does an audit take? For most catalogs, a first pass with a spreadsheet takes hours, not weeks. Fixing supplier data takes longer.

Do I need a developer? Not for a spreadsheet audit. A script helps with large catalogs.

What should I fix first? Missing fiber content and country of origin, then care instructions and claims.

Related guides

• Why Product Data Hygiene Matters for Apparel Brands Selling to California
• US Clothing Label Requirements: A Complete Checklist
• What Is California SB 707?

This article is general information, not legal advice.

Sources

• AAFA: U.S. labeling resources
• SPS Commerce: FTC labeling requirements, including recordkeeping
• PPAI: SB 707 FAQ