How to Infer Gender from Names in Google Sheets (For Marketing and Demographics)

You've got a spreadsheet with 5,000 customer names. Marketing wants to personalize the next email campaign—different subject lines for different audiences. Or maybe you're doing demographic analysis for a report. Either way, you need to figure out who's who.

Going through names one by one? That's not happening. And those gender API services that charge per lookup? At $0.002 per name, you're looking at $10 just for one list. Not to mention the hassle of setting up API calls, handling the data transfer, and dealing with rate limits.

There's a simpler way to do this, and it works right inside your spreadsheet.

Why This Even Matters

Let me back up for a second. Why would you need gender data from names in the first place?

Personalization, mostly. Email campaigns that use "Hi [Name]" perform better than generic ones, but campaigns that reference "products other women love" or "top picks for men" can perform even better—when done appropriately. Same goes for product recommendations, ad targeting, and customer segmentation.

There's also demographic analysis. If you're a researcher, HR professional, or analyst trying to understand the composition of a dataset, gender distribution is often one of the first things you look at.

The Old Way: APIs and Manual Lookups

Traditionally, if you wanted to infer gender from names at scale, you had a few options:

• Gender API services: Services like Gender-API or Genderize.io. They work, but you're paying per lookup and dealing with API integration.
• Lookup tables: Build a spreadsheet with known name-gender mappings, then VLOOKUP against it. Breaks constantly on names that aren't in your table.
• Manual review: Just... look at names and guess. Surprisingly common in smaller organizations. Not scalable.

All of these have problems. APIs cost money and require technical setup. Lookup tables miss edge cases. Manual work takes forever and still gets things wrong.

Using AI Instead

Modern AI can infer gender from names with pretty solid accuracy—and it handles edge cases intelligently. Unlike a lookup table that fails on "Saoirse" or "Priya," AI has been trained on millions of names from diverse cultural backgrounds.

Here's what makes AI different:

• Cultural context: It knows that "Andrea" is typically female in English-speaking countries but male in Italy.
• Confidence handling: For ambiguous names, it can output "Unknown" rather than guessing wrong.
• No maintenance: You don't have to keep updating a lookup table every time you encounter a new name.

How to Do This in Google Sheets

I built Categorize AI to handle exactly this kind of task. Here's how it works for gender categorization:

Processing 2,000 names takes a few seconds. No API keys, no per-lookup fees, no technical setup.

What the Output Looks Like

Here's an example of what you'd get:

Notice how "Jordan" and "Yuki" get marked as "Unknown"—these are genuinely ambiguous names that could go either way. That's better than guessing wrong 50% of the time.

Common Use Cases

Handling the Edge Cases

Let's be realistic about limitations. Gender inference from names isn't—and shouldn't be treated as—definitive.

Some practical tips:

• Use "Unknown" liberally. It's better to flag 15% of names as unknown and manually review them than to get 15% wrong.
• Consider your use case. For internal analytics, inferred data is usually fine. For customer-facing personalization, consider giving people the option to self-identify.
• Be culturally aware. Name-gender mappings vary significantly across cultures. A name that's clearly male in one country might be female in another.
• Don't over-rely on this data. Use it as one signal among many, not the sole basis for decisions.

Privacy Considerations

One thing worth noting: Categorize AI only processes data from the sheet you're actively working on. Your data doesn't get stored, shared, or used for training. The categorization happens, results go into your spreadsheet, and that's it.

If you're working with customer data, this matters. You're not sending names to some external database that you don't control.

Getting Started

If you want to try this with your own data:

1. Install Categorize AI from the Google Workspace Marketplace
2. Open your spreadsheet with the name column
3. Set your categories: "Male," "Female," "Unknown"
4. Select your data and run

There's a free trial, so you can test it on your actual data before committing.

Beyond Gender: Other Name-Based Categorization

Once you're set up, you might find other uses for this kind of categorization. The same tool can infer:

• Likely region of origin: For international customer segmentation
• Language preference: Based on name patterns, helpful for localization
• Generational cohort: Some names are strongly associated with particular decades

The underlying approach is the same: take unstructured name data and turn it into structured categories you can filter, sort, and analyze.

Wrapping Up

Inferring gender from names isn't a new problem, but the old solutions—APIs, lookup tables, manual work—all have significant drawbacks. AI makes this faster, cheaper, and more accurate, especially for edge cases and non-Western names.

Just remember: this is inference, not ground truth. Use it for what it's good at (marketing segmentation, demographic analysis, personalization at scale) and supplement with other data sources when precision matters.

The next time marketing asks you to "segment the list by gender," you won't need to spend a day on it. Or pay $50 to an API service. A few clicks and a few seconds will do.