Should we use predicted gender in email greetings?

Neutral greetings are the safest default. If you use predictions, treat them as suggestions guests can confirm or change, and prefer stated preferences.

Why pass a country code?

Many names lean differently by country. A country hint helps produce a more relevant estimate for international guests, and it accepts ISO 3166-1 alpha-2 codes in any case.

What does an unknown result cost?

Unknown is a normal, successful result and is charged like any lookup, 1 credit for dataset lookups or 3 credits when AI is requested.

Gender prediction · Travel and hospitality

Gender insights for travel marketing and guest analytics

Travel marketers segment by destination, trip type and season, and sometimes want to understand the broad makeup of their audiences. Finowl gender predictions estimate how a name is typically used, which can support aggregate reporting, such as the estimated mix of subscribers to a solo travel newsletter or wellness retreat campaign. Adding a country hint improves estimates for international guests whose names lean differently across regions.

Keep any personalization light and optional. Predicted gender should never change room rates, fares, upgrade eligibility or which offers a guest can access. It is an estimate, not an identity, and it will be wrong for some guests. Let guests choose their own salutation or skip it entirely, and always prefer a stated preference over any prediction in profiles and emails.

Try it in the sandbox About gender

USE CASES

Where it fits in travel and hospitality

Campaign audience reports
Estimate the aggregate gender mix of guests engaging with campaigns for solo travel, family holidays or spa packages, helping marketers check that creative and channels reach the audience they intended.
Neutral-first greetings
Default to neutral greetings and use predictions only to suggest a salutation that guests can confirm or change in their profile, never to assume one silently.
International guest analysis
Use country hints when estimating aggregate audience composition for international markets, since names such as Andrea or Jean lean differently by country and a hint makes each regional report more meaningful.
Survey representativeness
Compare the estimated aggregate mix of guest survey respondents with the guests invited, to see whether satisfaction results may over-represent some groups and need cautious interpretation.

HOW TO INTEGRATE

The workflow

1
Export first names and country codes from guest marketing lists, kept separate from pricing and revenue management systems.
2
Send up to 50 items per request to POST /v1/gender/batch using opaque ids.
3
Filter by result_status and confidence, and report unknown results as a separate category.
4
Store aggregates for reporting, and store per-guest values only as editable suggestions that guests can override.

POST /v1/gender/name

curl https://api.finowl.co/v1/gender/name \
  -H "Authorization: Bearer $FINOWL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "name": "Andrea Rossi",
  "country": "IT"
}'

Estimates gender for a guest on an Italian marketing list, using a country hint because Andrea leans differently across countries. Used only for aggregate reporting, never for rates or offers.

GOOD PRACTICE

Before you ship

Never use predictions to set fares, room rates, upgrades, loyalty tiers or offer eligibility. They estimate name usage and can be wrong for individual guests.
Mention gender inference in your privacy notice and keep predicted values out of systems that drive pricing, revenue management or eligibility decisions.
Offer guests a way to set their own salutation, including neutral options, and make sure their choice always overrides any prediction.

Read the API reference for every field and error code. Each lookup costs 1 credit (3 when it requests AI); see pricing.

A FEW GOOD QUESTIONS

Common questions