Same Person, Different Email: Finding Duplicate Customers in Your Store's Export File
Your platform thinks they're two different people.
Same name. Same shipping address. Different email address — maybe a work email last year, a Gmail this time. Your customer count is off. Your email list has them twice. And if you suppress unsubscribes by email, you've got a compliance gap.
This is one of the most common data quality problems in e-commerce — and one of the hardest to catch with standard tools.
Why this happens
Every major e-commerce platform — Shopify, Squarespace, WooCommerce, BigCommerce — treats each email address as a separate customer identity. There's no built-in check for "have I seen this person before under a different address?"
It happens when:
- A customer checks out as a guest, then creates an account later
- Someone uses their work email for one order and personal email for another
- A household shares an account but a family member creates their own
- A customer forgets they already have an account and registers again
The result: one real person, two (or more) records.
Why Excel won't catch it
Excel's Remove Duplicates works on exact matches only. If two rows have different email addresses, Excel keeps both — no matter how identical everything else is.
VLOOKUP has the same problem. You can look up a value across columns, but "Ann Russell" at "135 Nobility Court" won't match a row where the email is different. The tool doesn't know they're the same person.
What you need is matching that looks across multiple fields simultaneously — name, address, postcode — and scores how similar two records are, rather than checking whether they're identical.
How to find them
Step 1. Export your customer list.
- Shopify: Customers → Export → All customers (CSV)
- Squarespace: Contacts → Export
- WooCommerce: WooCommerce → Customers → Export
- BigCommerce: Customers → Export
Step 2. Upload the file to Clean by Similarity API.
Step 3. When prompted to select matching columns, choose:
- First Name
- Last Name
- Shipping Address 1
- Shipping City
- Shipping Zip / Postcode
Do not include email. Email is the field causing the problem — if you match on it, you'll only find records that are already identical, which defeats the purpose.
Step 4. Run. Clean compares every combination of rows using the fields you selected and groups likely matches into clusters.
Step 5. Review the clusters. Each group shows the records that likely belong to the same person, with a similarity score. You decide which to keep and which to merge or remove.
Step 6. Download your deduplicated list or the cluster view for manual review.
Total time: under 5 minutes for most files.
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Try it for free →What to do with the results
Once you have your clusters:
In your store: merge the accounts manually, keeping the one with the most order history. Most platforms have a merge or transfer function in the customer admin.
In your email platform: suppress both addresses or consolidate to one. If you're using segments or tags, apply them to the surviving record.
In your CRM: if you've already imported the duplicates, use the cluster list to identify and merge the records before your next send or campaign.
How often should you do this?
For most stores: quarterly is enough. Run it before any major campaign, before a list import into a new platform, or any time your customer count looks higher than expected relative to order volume.
Free for smaller lists
Files under 500 rows are completely free — no account needed. Larger files start at $4.99.
Find duplicate customers in your export →