Genderizer
The likely gender of a person, derived from their name.
More than a lookup table
The Genderizer assigns a probability to each gender based on the given name — trained on a large multilingual corpus of personal names with known cultural and regional context.
Names that are clearly female (Maria) or male (Hans) get high confidence. Unisex names (Alex, Jordan) return balanced probabilities. The Genderizer never forces a binary guess when the data does not support one.
What it does
Probability scores
Returns separate male/female probabilities — not just a single label — so you can pick a confidence threshold per use case.
Multilingual coverage
Trained across European, Latin American, Slavic, Arabic, Indian and East Asian naming traditions.
Unisex aware
Names like Alex or Sasha return ambiguous results — consumers decide how to handle them.
Cultural context
A given name's gender can flip across cultures. Country and language hints sharpen the answer when supplied.
Salutation derivation
Map the predicted gender to localised salutations (Mr/Ms, Herr/Frau, M./Mme) for transactional output.
Batch friendly
Inexpensive enough to score entire customer databases — useful for bulk personalisation backfills.
Where Genderizer fits
E-mail & marketing personalisation
Produce salutation lines that match each recipient — even when no gender field was ever captured.
Transactional letters
Avoid awkward generic greetings on contracts, invoices and onboarding letters.
Demographic analytics
Derive aggregate gender splits across customer segments without forcing collection of self-reported gender.
Data enrichment
Backfill missing gender on legacy records — with explicit confidence so downstream systems can decide whether to rely on it.
Try it on your own names
Happy to benchmark prediction quality on a sample of your customer data, in any language.