Understanding gender by name segmentation

How we match shopper first names to gender, and how it helps personalize your PDQ setup

May 28, 2025

How we segment by gender

1. We take the shopper’s first name
   This comes directly from the checkout field, no extra input needed.

2. We clean the name before matching
   Names are lower‑cased, stripped of accents, and transliterated (so “Élise” becomes “elise”).

3. We match it to our reference table
   Names are checked against a public dataset of 200,000+ first names. If we find a confident match, we assign:

   • Male
   • Female
   • If no match → Unknown

A quick example

A shopper named “Noa” checks out.

→ We clean the name to noa → Match it to the dataset → Assign Female (based on majority labeling in the data)

What you can expect

• On average, 97–98% of names are recognized
• Gender-neutral names (e.g., “Alex”, “Jamie”) may be misclassified
• If a name is too rare (<10 matches in the dataset), it defaults to Unknown

Continuous improvements

Every night, we:

• Check for names that showed up as “Unknown”
• Flag high-frequency names
• Add them (with the correct gender) to our reference table. That means your data keeps getting more accurate, automatically.

How accurate is the gender data? In our tests, it’s ~95–98% accurate. Most mistakes are due to gender-neutral names.

That’s it! Your PDQ gender segmentation is working behind the scenes — helping you personalize more intelligently, with zero setup required.