TG filtering advanced: How to use the gender field for secondary stratification and accurate crowd screening

In acquiring customers overseas, tg filtering (Telegram number filtering) is a common method to batch verify whether the number is registered and active. But just verifying the account is not enough - if you can further filter the gender of the target users, then the conversion rate of each message may be doubled. This article will deeply analyze the meaning of the gender field in Telegram filtering, identify boundaries, and teach you step by step how to complete the complete operation from TG screening number to gender stratification on the KK-DATA platform.

What is the gender field in TG filtering? Where does it come from?

When many people see the “Gender” field for the first time, they mistakenly think that this is the real data filled in by the user themselves. In fact, the gender field in TG filtering is not ID-level accurate information, but the result of model inference by the system based on the public data associated with the number.

Identify sources and common fields

• Inference basis: avatar (photo/style), nickname (vocabulary pattern), public group participation, personal profile text, etc.
• Output field: usually three states: male, female, and unknown.
• Additional data: In addition to gender, fields such as age range (such as 20-30 years old), avatar URL, nickname text, etc. are often included.

Understand probability

This gender recognition is the output of a statistical model (accuracy rate is about 70%-85%), not the user’s real-name information. For example: if a number uses a basketball star’s avatar and is nicknamed “NBA Fan”, the model may determine that it is male; but if the number uses the default avatar and the nickname is a single letter, it will most likely display “unknown”.

Why add gender stratification to Telegram filtering?

Gender-neutral TG screening is “indiscriminate bombing” - you send the same advertisement to everyone who can register for TG, but the people who really need your product may only account for a small part.

Efficiency improvements brought about by gender orientation

Scene	Without gender stratification	With gender stratification
Game promotion (biased towards men)	Send 10,000 messages, female users also receive them, the conversion rate is low	Only send male numbers, the conversion rate increases by 1.5-2 times
Beauty e-commerce (female biased)	Male users are directly deleted/blocked	Only send female numbers, the effective reach rate is higher
Mother and baby community	Most invalid contacts	Accurate targeting of women + age matching

Typical example: If 10,000 TG messages are sent at one time, the open rate without stratification may be only 5%-8%; but after gender filtering (for example, only sending male to game products), the open rate can reach 15%-20%.

Scenario 1: B2C conversion promotion (such as e-commerce, App download)

• Male Users: More suitable for promoting tool apps, games, financial management, and sporting goods.
• Female Users: More suitable for beauty products, skin care products, maternal and infant products, social e-commerce, and fashion apparel.

If your product is for men, using gender screening to eliminate female numbers can significantly reduce waste.

Scenario 2: Community operation and invitation (such as men to the community, women to the community)

• Establish “male interest groups” (such as cryptocurrency, game exchange) and invite male users to join. The success rate is 40%-60% higher than undifferentiated invitations.
• Establish a “Female Health Group” or “Parent-Child Group” and invite female users to become more active.

How to use KK-DATA for TG filtering and view gender data? Step by step operation

KK-DATA is a mature customer acquisition data screening platform that supports multi-platform screening such as Telegram, WhatsApp, Line, etc., and gender detection is a built-in function. The following takes Telegram filtering as an example to demonstrate the complete process.

Step 1: Prepare numbers to be screened (generate or import)

You have two ways to get the number to be screened:

• Global Number Generation: Select “Generation Module” in the KK-DATA console, set the country (such as the United States, Philippines, Vietnam) and number segment, and generate a random number for free. The maximum number of entries per transaction is about 1 million.
• Upload CSV by yourself: If you already have a list of numbers, upload the TXT or CSV file directly, with one mobile phone number (including country code) per line.

Note: Make sure the file format (such as +8613800138000) is correct to avoid recognition failure.

Step 2: Create a task and select the detection type

1. Log in to KK-DATA Console
2. Click “Create Task” - select the “Telegram” channel
3. Configure detection items:
   • Activation Test (required): Verify whether the number is registered with TG
   • Activity Detection (recommended): Select an active window (such as 7 days, 30 days) to ensure that the user is active
   • Gender Detection (the focus of this article): Check “Gender Identification”
4. Optional additional fields: age, avatar URL, nickname, etc. (check as needed)
5. Before submitting the task, the system will display the estimated cost (see the real-time price on the console for details). Submit after confirmation.

Step 3: View results and export hierarchical data

After the task is completed (usually a few minutes to tens of minutes, depending on the quantity), on the “Task Details” page:

• Gender Distribution Chart: After the task is completed, a pie chart or bar chart is automatically generated to show the proportion of male, female, and unknown.
• Table Filter: Click on the “Gender” column in the results table to sort, or use the filter to show only male
• Export: Click the “Export” button, select the format (CSV or TXT), the system will generate a file containing only the numbers that meet the conditions

Practical Tips: If you need to export only male numbers, first filter “gender=male” and then export. Can also be combined to filter: gender=male AND active days ≥ 7 days.

What does the gender identification result after TG screening reflect? What can’t be reflected?

To avoid misuse, it is important to understand the capability boundaries of gender fields.

What can it reflect?

• Gender orientation based on model analysis: Recognition accuracy is usually 70%-85% (varies by region and quality)
• Age interval estimation: such as 20-30 years old, 30-40 years old (not precise age, but can be used for population stratification)
• Public features related to avatars/nicknames: Numbers with avatars have a higher recognition rate, while numbers without avatars have a lower recognition rate
• In line with the target portrait of certain products: For example, “Male + 25-34 years old” is suitable for promoting games and financial products

Can’t reflect anything

• Cannot represent the gender of the user’s ID card: No authoritative source, only model speculation
• Not representative of gender identity or sexual orientation: Model based on public text/images only
• The age field is the estimation interval: For example, “25-34” indicates that the model predicts that this age group is not the exact year of birth.
• Unknown proportion may be higher: Especially in areas with sparse data (such as the Middle East and Africa), unknown can exceed 40%

Compared with gender filtering on other platforms (such as WhatsApp, Line), what are the characteristics of TG filtering?

Platform	Gender recognition basis	Recognition rate (estimate)	Special instructions
Telegram	Avatar, nickname, group, public profile	70%-85%	Users can be completely anonymous, with a high proportion of unknown
WhatsApp	Personal avatar, nickname (limited)	60%-75%	WhatsApp has no public group information, and the data source is narrow
Line	Public information (avatar, nickname, status)	75%-85%	Taiwan, Japan, Thailand and other regions have higher recognition rates

Summary of TG filtering features:

• More associated data can be obtained (group participation) → more in-depth gender identification
• But user anonymity is strong → unknown ratio is also the highest
• Suitable for B2C promotion scenarios that require large-scale screening

KK-DATA supports the screen numbers of the above three platforms at the same time. You can combine detection in one task, but this article focuses on Telegram.

Best practices for using Gender Layer secondary layering

The gender field is a “secondary filter” that can be placed after the basic filter. The following process is recommended:

Suggestion process

1. Layer 1: Basic TG filtering

   • Purpose: Verify whether the number is registered with TG and active (7-day or 30-day window)
   • Output: Only keep “activated + active” numbers

2. Second level: Gender screening

   • Filter by gender (male/female) based on active numbers
   • If you need more accuracy, you can add an age range (such as “20-35 years old”)

3. Level 3: Export and batch testing

   • Export male numbers and female numbers to different CSVs
   • Test a small batch first (such as 1-2000 males) to compare the conversion effect

Process unknown numbers

• unknown number: a number whose gender cannot be determined (usually 20%-40%)
• Coping method: Save it as a “pending batch”, which can be used as a control group test, or to replenish the test (such as repeating unknown activity changes after 30 days)

Suggestions for batch testing

It is recommended to conduct an A/B test when using gender stratification for the first time:

• Group A: Active numbers without gender screening (1000 random numbers)
• Group B: only male’s active numbers (1000)

Then send the same promotion message separately and compare the difference in conversion rates. If group B is significantly higher than group A, it means that gender orientation is effective and can be used on a large scale in the future.

FAQ

**Q: How accurate is the gender field in TG filtering? **

Answer: Gender recognition is based on public information associated with the number (avatar, nickname, geographical location, etc.) and is inferred through algorithms. The accuracy varies by region and data quality. Usually the male/female identification accuracy is between 70% and 85%, and the unknown proportion may decrease as the number of samples increases. It is not recommended to be used in scenarios that require 100% accuracy (such as law enforcement or risk control), but for precise customer acquisition, it can significantly improve ROI.

**Q: Why do many of the numbers I upload show the gender as unknown? **

Answer: Common reasons include: the number is not registered with TG; the user has set privacy restrictions (such as hiding personal information); the avatar/nickname is neutral or random (such as the default avatar, initials); or the number has low activity resulting in insufficient data. It is recommended to filter active users first and then detect gender, which can reduce the unknown proportion (from 40% to about 20%).

**Q: Can I filter by gender and age at the same time? **

Answer: Yes. The gender detection results of KK-DATA include the age field (age range, such as 18-24, 25-34, 35-44, etc.), and you can specify two conditions at the same time for secondary stratification when exporting. Please note that age is an estimated range, not an exact number. For example, filtering “Gender=male AND Age=25-34” can obtain groups with higher product matching.

**Q: Can gender data after TG screening be used in third-party tools? **

Answer: The exported CSV/TXT file can be imported into other CRM or marketing tools (such as Facebook advertising, email marketing platforms), but attention must be paid to data compliance (such as GDPR, CDPR restrictions). It is recommended to only use it to acquire customers on your own platform, and do not resell or use it for harassment. If you are processing EU user data, make sure you have the appropriate legal basis.

**Q: Which regions does KK-DATA’s TG screen number support? **

Answer: It supports 240+ country/region codes around the world. The gender model in the detection results has different adaptability to different regions. Generally speaking, the recognition rate in Europe and the United States (the United States, the United Kingdom, Germany) is higher (about 80%+), and the recognition rate in the Middle East/Africa (such as Nigeria, Saudi Arabia) is slightly lower (about 60%-70%). You can view the average recognition ratio of each country in the console, allowing you to select areas with high recognition rates for priority promotion.

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If you are looking for a flexible, pay-as-you-go TG filtering tool, try KK-DATA. You can follow the steps in this article to create your first batch of screening tasks in the console and experience the improvement in customer acquisition efficiency brought about by gender stratification. There is no subscription package, you can charge as much as you want and use it, and you will be deducted according to the item.

👉Log in to the console to start screening numbers 👉 Two-way contact customer service: https://t.me/kkdata_robot 📄 Usage documentation: https://docs.kkdata.cc/