How to Clean Up Products, Customers, and Other Data in Shopify

Shopify data cleanup means finding and fixing the errors that build up in your store over time. These include duplicate products, inconsistent vendors and tags, missing SKUs or images, outdated listings, duplicate customer profiles and fields left behind by old apps.

The safest way to do it is to back up first, audit your data in a spreadsheet, fix problems in batches and archive rather than delete when you’re unsure. Messy data causes practical problems. Automated collections pick up the wrong products, filters show near-identical options, and imports overwrite good values. This guide focuses on product data first, because that’s where most clutter lives, and then covers customers and metafields.

How to Clean Up Your Shopify Product Data

Product cleanup works best as a cycle: export everything, review it where you can see the whole catalog at once, then push corrections back in controlled batches. Working from a spreadsheet makes patterns visible that are easy to miss when you click through products one by one.

Step 1: Back up your store data

Start with a full export so you can restore any value you change by mistake. In your Shopify admin, go to Products, click Export, choose All products and select CSV for Excel, Numbers, or another spreadsheet program. Shopify emails the file to you, and to the store owner if that’s someone else. The CSV holds your product details and image links, not the image files themselves.

If your catalog has thousands of variants, the export can time out. In that case, filter the product list, for example by vendor or product type, and export in smaller batches using the option for products that match your search and filters. Export your customer list from the Customers page at the same time. Keep both files untouched as your backup, and do all cleanup work on copies.

Step 2: Audit your catalog for common problems

Open a copy of the product export and look for the issues below. Most of them come from different people, apps and supplier files adding data over the years without shared rules.

Use filters and formulas to find problems rather than sorting the sheet. Shopify warns that sorting a product CSV can separate variants from their image URLs, and importing a sorted file can overwrite products with bad data. If you need a sorted view to spot duplicates, make it on a separate copy that you’ll never import.

Not every blank cell is an error. For example, SEO title and SEO description export as blank unless someone customized them, and Shopify fills them automatically from the product title and description. Only add them where you want different text in search results.

As you go, write down the standard you’re cleaning toward, such as an approved vendor list, a fixed set of product types and tag naming rules like lowercase with hyphens. That list makes the fixes consistent and becomes the rulebook for future imports.

Step 3: Choose where to make each fix

Shopify gives you three ways to correct data in bulk, and each suits a different kind of change. Picking the right one keeps small fixes quick and large ones safe.

To use the bulk editor, select products on the Products page and click Bulk edit. Click Columns to add the fields you want to fix, including metafield columns. Filtering the list first helps here, because you can open the bulk editor on exactly the products with a given problem, such as every product from one vendor.

If saving fails with “Update the invalid values, then save again,” a variant is usually missing a required SKU or has a metafield validation error. Add the SKU and metafield columns to find the problem cell, fix it and save again.

Step 4: Re-import corrected data safely

For larger fixes, edit your working copy of the export and import it from Products > Import with Overwrite products with matching handles selected. The overwrite option is what makes a cleanup file update existing products instead of skipping them. Overwriting follows strict rules, so build the file deliberately. A column included but left blank clears that value on the product, while a column removed from the file keeps its current value.

Therefore, the safest cleanup file contains URL handle, Title and only the columns you’re correcting. For example, to standardize vendor names, keep URL handle, Title and Vendor, fix the vendor values and delete every other column. If you change variant-level fields such as SKU or price, keep the option name and value columns too. Shopify uses them to match variants, and leaving them out can replace your variants with a single default one. Avoid editing option values during a cleanup unless you have to.

Changing a value such as “Small” to “S” creates new variant IDs, which can break apps and integrations linked to the old ones. Also save the file as UTF-8, test it on a few products first and remember that an import can’t be canceled once it starts. If you run this export, fix and re-import cycle often, Bulkflow can make it easier to repeat. It supports bulk export and import for Shopify with field mapping, so you can update existing records from a spreadsheet without rebuilding Shopify’s template each time.

Step 5: Archive or delete outdated products

Archive products you no longer sell but might want later, and delete only what you’re sure you’ll never need. Archiving stops a product from showing in your online store without removing it, and archived products move to the Archived tab on the Products page.

If you unarchive one, it returns as a draft. Deleting removes a product from Shopify permanently, and deleted products can’t be restored. That makes deletion a good fit for clear mistakes, such as an exact duplicate created by a bad import.

For discontinued or seasonal items, archiving is the safer default. You can archive or delete several products at once by selecting them on the Products page and using the bulk actions menu. Note that a CSV import can’t bulk-delete products, so removals happen in the admin rather than through your cleanup file.

Merge Duplicate Customer Profiles

Duplicate customer profiles appear when the same person checks out with a different email address or phone number, or when records come in from another system. Shopify lets you merge two profiles into one, which combines their orders, addresses, notes, tags and timeline into a single record.

Shopify doesn’t detect duplicates automatically, so the customer export from Step 1 is useful here. Look for rows with matching names and phone numbers or shipping addresses, then confirm each pair in the admin before merging.

To merge, select the two customers on the Customers page, open the menu next to Edit customers, choose Merge customers, review the merged result and confirm. Check these limits before you start, because a merge can’t be undone:

• Two at a time: Each merge combines two profiles, so repeat it for any additional duplicates.
• Restricted profiles: Profiles with a subscription contract, an active B2B company link, a vaulted credit card, a store credit account or a multipass login can’t be merged.
• Combined limits: Merged notes can’t exceed 5,000 characters, and merged tags can’t exceed 250.
• Marketing consent: If either profile accepted email or SMS marketing, the merged profile accepts it too, so confirm that’s accurate.

Shopify doesn’t notify the customer about the merge. If the two profiles contain conflicting information, cancel the merge, correct each record and try again.

Remove Leftover Metafields Carefully

Metafields store extra product, collection and customer information, and apps often create them. Over time, a store can collect definitions that no theme section or app still uses. You can review and delete definitions under Settings > Metafields and metaobjects by selecting the area of your store, opening the definition and clicking Delete. Shopify then asks how much to remove.

Delete field only removes the definition but keeps its values, which you can later reconnect by creating a new definition with the same namespace and key. Delete field and its saved values removes everything, and it can’t be undone. Before deleting, check whether the metafield is connected to a block or section in your live theme, because removing it can cause display errors for shoppers. Some definitions can’t be deleted at all, such as search and discovery metafields, and category metafields tied to variant options must be disconnected first. You may also see metafields without a definition, often left by apps that stored data directly.

Be especially cautious with these. Apps can keep useful information, such as review data or Google Shopping categories, in metafields that have no definition, so only remove ones you can trace to an app you’ve already uninstalled.

Keep Your Store Data Clean After the Cleanup

A cleanup only lasts if new data follows the same standards. The naming rules you wrote down during the audit are the most useful tool here. Share the approved vendor list, product types and tag format with everyone who adds products, and check supplier spreadsheets against them before importing. It also helps to make cleanup a regular task rather than a rescue project.

A short review each quarter, or before a busy season, can catch new duplicates, untagged products and stale drafts while they’re still few. In addition, take a fresh export before every large import, so you always have a recent backup to return to.

Conclusion

Shopify data cleanup is less about one big fix and more about bringing your products, customers and metafields back to a consistent standard you can maintain. A clean catalog makes collections, filters and future imports more reliable, which saves time every time you update the store. We hope this guide helps you tackle your Shopify data cleanup with confidence. If you manage store data in spreadsheets regularly, you can explore Bulkflow.