PDQ includes a gender segment in your analytics, rules, and A/B testing tools — all based on the shopper’s first name at checkout.
This is a lightweight and powerful way to personalize your shopper experience, with ~97% name coverage out of the box.
Once the name is picked up during checkout, here’s what happens next:
How we segment by gender
We take the shopper’s first name
This comes directly from the checkout field, no extra input needed.We clean the name before matching
Names are lower‑cased, stripped of accents, and transliterated (so “Élise” becomes “elise”).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.
As always, if you need help just reach out on chat or at
📩 support@prettydamnquick.com