Gender Determination for TG Users Without Avatars: Processing Strategies and Export Rules for Batch Filtering

In出海 marketing and Telegram community operations, batch filtering is almost a daily task for every team. However, many operators find that filter results always contain a batch of users marked as “unknown gender” — these users typically have no avatar, or their avatars cannot be correctly identified. Faced with these “avatar-less” users, releasing them directly risks wasting budget, while excluding them outright might miss real active users. How to scientifically handle these TG users of unknown gender is exactly the core issue this article aims to solve.

This article will explain, based on actual operations on the KK-DATA platform, from data causes and proportion analysis to export and filtering strategies, systematically discussing how to effectively handle avatar-less/unknown gender data during batch filtering and improve your TG customer acquisition accuracy.

Why Do You Encounter “Unknown Gender” Users When Filtering TG?

To understand “unknown gender” data, we first need to clarify how TG gender identification works.

The Telegram platform itself does not provide a user “gender” field (e.g., male/female). KK-DATA’s gender identification function is based on AI visual analysis of user avatar images. When a user sets a clear avatar containing a face, the system can classify it as “male” or “female.” However, when the following situations occur, the system cannot make a judgment and will mark the record as “unknown gender”:

• No personalized avatar set: The user uses Telegram’s default gray circular avatar (i.e., no avatar state).
• Avatar content is non-face: The user’s avatar is a landscape, animal, text, abstract pattern, cartoon character, or other image that cannot determine gender.
• Avatar is unclear or too small: The image resolution is insufficient or heavily compressed, and the AI model cannot extract effective features.
• Avatar is a group photo: The system cannot determine the target for analysis.

This is not a problem with the filtering tool, but an objective limitation determined by the Telegram platform mechanism. Regardless of the filtering tool used, as long as it relies on avatar recognition, “avatar-less users → unknown gender” is an unavoidable logical outcome.

Actual Proportion of Avatar-less Users and Unknown Gender Data in Filtering Results

In actual operations, what proportion of “unknown gender” data is normal? It depends on the source of your numbers.

• Number source is randomly generated: If you use a number generation tool to randomly generate a large number of TG numbers, many of these numbers, although registered on the platform, are not actively used by users. Their avatars are often the system default “no avatar” state. In such cases, the proportion of “unknown gender” can be as high as 50%–70%.
• Number source is crawled from active groups: User numbers crawled from public communities, since they are already in an active state, most users will set personalized avatars. In this case, the proportion of “unknown gender” is usually between 20%–40%.
• Number source is from whitelists/KOL placements: If it’s a targeted acquisition of high-quality users, the number of avatar-less users will significantly decrease, with the proportion possibly below 10%.

Key judgment principle: If you are targeting a broad audience for testing or bulk adding followers, “unknown gender” data is just as valuable as “male” and “female” data. However, if you need precise targeting (e.g., women’s beauty community), you should actively filter out this batch of users and keep only numbers with clear gender.

How to Export and Identify TG User Data Without Avatars/Unknown Gender

Below are practical steps using the KK-DATA console to demonstrate how to filter and export “unknown gender” users.

Step 1: Select Gender Identification When Submitting a Telegram Filtering Task

When creating a new Telegram filtering task in the console, in the “Detection Type” options list, make sure to check “Gender Identification.”

Step 2: Filter by Gender Dimension in the Filtering Results

After the task is completed, go to the “Task Details” page and find the result data list. In the filter area, click the “Gender” dropdown menu; you will see three options:

• Male
• Female
• Unknown Gender

After checking “Unknown Gender,” the list will display only that portion of data. You can click the “Export” button to export this batch of data separately in CSV or TXT format for subsequent analysis.

Three Common Strategies for Handling “Unknown Gender” Users

Once you have the “unknown gender” data, how should you use it? Different marketing goals determine different processing paths.

Strategy 1: Direct Filtering (for Precise Targeting Delivery)

Applicable scenarios: Promotions heavily reliant on gender labels, such as women’s beauty communities, men’s sneaker communities, couple dating apps, etc.

Operation: In the console results page, directly filter to export “Female” or “Male” data, excluding “unknown gender” users. This ensures that every number you contact has a clear gender label.

Caution: This may miss some real users who are female but have not set an avatar. First evaluate whether the proportion of users without avatars among your target audience will significantly impact your business.

Strategy 2: Unify into a “General Audience” Pool (for Testing or Cold Start)

Applicable scenarios: Cold start of new projects or when you need to quickly acquire a base number of users.

Operation: Export “unknown gender” data along with numbers of other genders as a “general audience” pool. First send a round of low-cost trial messages and observe the response rate. If the response rate is not significantly different from “male” or “female” users, it indicates that this batch of avatar-less users also has promotional value.

Strategy 3: Combine with Activity Level and TGID for Secondary Operations (for Community Growth)

Applicable scenarios: Scenarios involving bulk adding people to communities or private domain operations requiring long-term engagement.

Operation: In the filtering results, do not only focus on the gender label but also the “activity level” dimension. For users marked as “unknown gender” but with an activity level ≥7 days/15 days, export their TGID (Telegram unique identifier) and conduct secondary contact by adding them as friends or inviting them to groups.

What to Do When the Proportion of Avatar-less Users Is Too High?

If the proportion of “unknown gender” in the filtering results exceeds 60% or even higher, it is recommended to investigate from the following aspects:

1. Check the number source: Are your numbers from a random generation tool or crawling from a low-activity group? It is recommended to first use the “activity detection” function to confirm the online status of this batch of numbers and eliminate a large number of dead accounts.
2. Confirm that gender identification is checked: Occasionally, users forget to check the “Gender Identification” option in the task, causing some numbers not to be identified. Go back to the task creation page to confirm.
3. Is the task submission volume too large? If a single task submits more than 500,000 numbers, some image identifications may fail due to system load. It is recommended to submit in batches, with each batch controlled within 200,000 numbers.
4. Utilize the data deduplication repository: If you frequently encounter a high proportion of unknown gender, it is recommended to import historical task results into the KK-DATA data deduplication repository, remove duplicates and invalid numbers, and then resubmit a filtering task.

Best Practices for TG Gender Data Export in Different Scenarios

Promotion Scenario	Gender Processing Strategy	Export Rule	Handling for Unknown Gender Users
Precision marketing (e.g., beauty, apparel)	Only keep users with clear gender	Filter and export “Female” or “Male”	Directly exclude
General audience testing (cold start)	Keep all gender data	Export users of all genders	Include in general audience pool
Bulk add to community (private domain)	Prioritize high-activity users	Filter by “activity + gender” export	Keep high-activity unknown gender users
Holiday event targeting (e.g., Valentine’s Day)	Send invitations targeted by gender	Export TGIDs of corresponding gender	Exclude or divert to general pool

Frequently Asked Questions

Q: Why are TG users without avatars marked as “unknown gender”?

A: TG gender identification relies on AI analysis of user avatar images. If a user has not set a personalized avatar (using the default gray circular avatar), or the image content is not a human face (e.g., landscape, animal, text image), the system cannot determine gender from visual features and thus marks it as “unknown gender.” This is an objective limitation determined by the Telegram platform mechanism, not a problem with the filtering tool.

Q: If I only want female users, can I completely exclude “unknown gender” data?

A: Yes. In the KK-DATA console’s filtering results, you can filter by the “gender” dimension to keep only “Female” users, directly excluding “Male” and “Unknown Gender” users. However, note that this may miss some users who are actually female but have not set an avatar. It is recommended to decide based on your business’s actual tolerance.

Q: Will exporting “unknown gender” users incur additional charges?

A: Gender identification is an optional detection type for the Telegram filtering function. Regardless of whether the result is marked as “Male,” “Female,” or “Unknown Gender,” charges are only based on the actual number of numbers detected, with no additional fee due to different gender results. See the console real-time pricing for specific unit prices.

Q: Is it normal for the proportion of “unknown gender” in filtering results to exceed 50%?

A: If the number source is mainly randomly generated numbers or crawled from public groups, a high proportion of avatar-less users is normal. It is recommended to first check whether the number source is valid, then confirm whether the “Gender Identification” option is checked in the task. If still high, try using the “Global Number Generation” function to customize number segments, combining activity and gender for combined filtering.

Q: In subsequent operations, how can I use the exported avatar-less user data?

A: You can write it into a Telegram TGID import tool or group add bot for secondary contact. Since avatar-less users may not display an avatar due to privacy settings, they may still be interested in your promotional content. It is recommended to first send a small batch of test messages, observe the response rate and group join rate, and then decide whether to expand the push volume.

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Act now: Log in to the KK-DATA console, create a Telegram filtering task and check Gender Identification. Directly use the gender filter on the results page to export “unknown gender” users for separate analysis. For more details on gender identification rules, please refer to the User Documentation. If you encounter abnormal data proportions or export issues, contact customer service via Telegram @kkdata_cc.