"Dear Mr" in front of a woman's name costs more than it looks: the reader concludes nothing about your automation and everything about your company. While the base is small, salutations get fixed by hand. At a thousand contacts the manual pass ends, and campaigns fall back to a genderless "Hello" — or to a coin flip. Yet the signal is lying in plain sight: nine times out of ten, gender reads unambiguously from the first name your CRM already stores.
Does Bitrix24 have a Gender field?
There is no stock Gender field on leads, contacts, or deals in Bitrix24 (Alaio). The closest thing is Salutation on contacts and leads — a dropdown with values like Mr and Ms that stays empty in practice, because neither CRM forms nor imports nor telephony integrations ever fill it. There is no automation around it either: the platform makes no attempt to infer gender from a name. Teams that segment by gender seriously end up creating a custom list field and filling it manually or via Excel round-trips — an approach that dies on the first real volume.
How to detect gender from a name automatically?
The Gender by name robot from the Roboteka catalog solves it with one block in a workflow. Input: a name in any shape — "Mary", "mary smith", a Name field value, or a whole line from a web form. Output: four values — gender (M/F/U), a "name recognised" flag, a ready salutation ("Dear Mr"/"Dear Ms") and a short form ("Mr"/"Ms"). It runs offline against a bundled dictionary of tens of thousands of names — no external services, no daily quotas, no personal data leaving your portal — so it works equally for a single card and for sweeping the whole base.
What about tricky names?
Signals apply in order of confidence. The dictionary goes first and covers both alphabets: Mary Smith resolves to F because the robot knows Smith is a surname rather than a first name, and diacritics are folded, so José and María are found as written. Hyphenated first names are split and checked part by part. The key property is honesty: an ambiguous name with no other signal — Sasha, or unisex names like Taylor — returns U, and both salutation fields come back empty. A wrong "Dear Mr" physically cannot reach an unrecognised person.
How to insert the right salutation into an email?
The robot's return values plug into the next workflow steps as ordinary variables: put the salutation plus the last name into your email template and "Dear Ms Smith" assembles itself. The salutation language is a setting: auto (by the name's alphabet), English, German, Polish, Portuguese, Spanish or Russian — an international base gets "Sehr geehrte Frau" and "Estimada Sra." from the same scheme. Add a branch for U cases with an if-then condition: gender unknown — the email opens with a neutral greeting; recognised — with a personal one.
How to segment the base by gender?
Write the result into the card: the Update contact robot stores gender in your custom list field — and, if you want, into the stock Salutation too. From there it is a regular field: filters, CRM marketing segments, reports, process conditions all work with no caveats. To tag an existing base, use bulk workflow launch: one template — detect gender, write the field — fired across all contacts, and the base is tagged by morning. Clean the data first: CRM data cleanup raises the share of recognised names more than any dictionary ever will.
What next
Start with campaigns — that is where a wrong salutation shows the most: the robot before send, a condition on gender, two email variants. Then tag the base with a bulk launch and build your first men/women funnel report. The neighbouring blocks live in the robot catalog: conditions, entity updates, string tools. And if the block you need is missing — describe the task: Roboteka builds missing activities for free and ships them to the shared library. That is exactly how Gender by name came to exist.