Add gender to Shopify customers
Shopify has no gender field on a customer, but tags work everywhere: in customer segments, in Shopify Email and in most marketing apps. Let a Zap tag each new customer with gender-female or gender-male when NameGender is confident, and leave the rest untagged.
100 free credits every day. No card.
Why a tag, and only when it is confident
Tags are the one customer field every Shopify tool can filter on. A segment such as “customer_tags CONTAINS 'gender-female'” can then drive an email, a discount audience or a collection page without any app reading the API.
Tag a customer only when the probability is high, for example 90 or above. Customers without a tag get your neutral wording. That is better than a tag on every customer, where a 55% guess and a 99% answer would look the same in every segment.
Use it for wording and product suggestions. Prices, discounts that only one gender can get and anything else that treats one customer differently from another should not depend on a guess from a name.
Zapier: New Customer → Find Gender → Add Tag to Customer
Add NameGender to your Zapier account with the invitation link on this page. Trigger: Shopify, New Customer. Action: NameGender, Find Gender, with the customer’s First Name, or the Email with the type of value set to Email when first names are often missing; map the country from the default address if you want it, as a code or a name. Then add a Filter by Zapier step that only continues when Probability is at least 90, and finish with Shopify, Add Tag to Customer, using the customer ID from the trigger and a tag made of gender- followed by the Gender field from the NameGender step.
Zapier’s Shopify app cannot write customer metafields, which is why this uses a tag. If you also want the probability in Shopify, put it in the customer note with Update Customer.
In Make, Shopify’s app has no new-customer trigger. Start from Watch orders instead, which catches every customer who buys, then call NameGender’s Get Gender from a Name and write the tag with Update a customer.
Existing customers: one export, one import
Export your customers from Shopify Admin → Customers → Export as CSV. Upload the file in the NameGender dashboard, pick the First Name column (or Email when first names are sparse) and check the estimated cost before starting. Repeated first names are looked up once.
Add gender-female or gender-male to the Tags column of the rows you want tagged, keeping the tags that are already there, and import the file with “Overwrite existing customers that have the same email or phone” switched on. An import replaces the tags of a matched customer, so a row whose existing tags were dropped loses them.
Questions
Can I store gender in a customer metafield?
Not with Zapier, whose Shopify app cannot write metafields. With your own app or Shopify Flow you can; a tag needs no setup and works in segments.
What about customers without a first name?
Send the email address instead. Only the part before the @ is read, so jane.doe@example.com is looked up as Jane, and role addresses such as info@ come back unknown.
Does an unknown name cost a credit?
Yes. Every lookup costs one credit, including names that come back unknown.
Is inferred gender personal data?
Treat it as personal data: mention the enrichment in your privacy notice and use it for wording and suggestions, not for decisions about a customer.
Related pages
Add gender and probability to Stripe customer metadata from the customer name or email, with a webhook handler or a Zapier flow into your CRM or sheet.
Write a gender custom property to new Klaviyo profiles from the first name or email, and build segments that only use confident answers.
Fill a GENDER audience field for new Mailchimp subscribers from the first name or email, then segment and personalise only where the answer is confident.
Reading the gender field alone discards everything that says whether to believe it. What each response field means and the thresholds behind confidence.
Check it against your own list
Every number on this page is reproducible with a free key. If your data breaks it, that is the more interesting result.