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We are updating our gender checker database with new names from around the world on a regular basis to improve the genderization quality. In addition, we manually check queries for which we could not find a result but that have already been requested several times.

After several months of working on an update of our data, we are happy to announce this big update to our name checker database with about 300,000 new entries. Our team added about 5,000 of those names manually.

Internal backtest showed that this update increased the genderization rate of names by about 2%.

The regular updates ensure the quality of our gender checker database.

Through our manual review, we ensure that the genderization results meet both our requirements and those of our customers.

Watch our short video and learn how to upload an Excel file for genderization.

Find a sample Excel file here.

The file can contain up to 50 columns, which means that you can also add custom columns to the Excel file, e.g. with a unique ID. All columns are copied into the target file one by one.

After the file upload, you can chose the column that you need to be checked against our name checker database.

Before the entire Excel file gets processed, you receive a preview of the genderized results.

After the processing of the file against our name checker database has been completed, the file can be downloaded with a single click.

The genderized file will contain an additional column called ga_gender which contains the gender of every row.

Just recently we were mentioned in a comprehensive benchmark and comparison of several available gender inference services.

The team of Gender Gap in Science who conducted the survey assembled a list with more than 7,000 people and their gender and used this list as a basis for the comparison. They combined different error metrics and constraints to define benchmarks for realistic situations (e.g.minimize the proportion of all inaccuracies while keeping the mix-ups between female and male assignments under the threshold of 5. In all benchmarks, and on almost all sources comprising the test data set, Gender API shows the best results.

"In all our benchmarks, and on almost all sources comprising our test data set, Gender API shows the best results."

This outcome makes us very proud and confirms that conducting not just a simple database lookup, but working with data in detail is the best service we can offer to our customers.

In our third announcement, we would like to introduce our next improvement: Enhanced support for variant forms of spelling.

Sometimes customers enjoy writing their names in different ways. A very common way to do this is using Leetspeak to replace chars with similar glyphs.

For example, the name John is written as J0hn.

To enhance support for names spelt like this, we added a new field called name_sanitized to the API response.

This field gives you the name we found and used for genderization after the name was sanitized in different ways.