The whole process of tg male number screening: combine tg 30-year-old data and age field to accurately acquire customers

In overseas customer acquisition and community operations, screening out qualified tg male numbers has become a key step to improve marketing efficiency. Simple gender screening may not be enough, because a “male” label cannot tell you whether the user is active and has spending power. This is exactly the value of tg 30-year-old data - by targeting people around 30 years old through the age field, you can focus your promotion resources on groups with greater purchasing power or willingness to interact.

This article will focus on practical operations and teach you step by step how to use KK-DATA to complete tg male number screening and use the age field appropriately. Whether you are doing cross-border e-commerce, overseas social promotion, or attracting new users in the Telegram community, this article can help you save time and reduce trial and error costs.

What is tg male number? Why is it more valuable when combined with age data?

tg male number refers to a number that is determined to be “male” after analyzing Telegram user information through a gender detection platform (such as KK-DATA). This type of number is often used in overseas marketing to target promotion targets of a specific gender, such as financial, gaming, and technology products that male users are more interested in.

However, there is an obvious shortcoming in screening male numbers alone: ​​what you get is a gender label, but there is no information about “how old is this user?” Combining tg male numbers with tg 30-year-old data means that you can filter out users who are male and about 30 years old from the same batch of numbers. People around the age of 30 are usually in the stage of career advancement or family establishment. Their willingness to consume and ability to pay are relatively stable. They are often high-value customers in e-commerce, financial management, insurance, education and training and other industries.

The value of combination screening is reflected in three points:

• Improved Accuracy: Double filtering of gender + age to reduce invalid push.
• Increased interaction rate: Content targeting 30-year-old men (such as workplace tools, family finance) is more likely to trigger responses.
• Resource Saving: Avoid sending messages to a large number of irrelevant users and reduce the risk of account ban.

How to filter tg male numbers? What do I need to prepare?

Screening tg male numbers is not complicated, but you need to complete several basic preparations first. The most important prerequisite is: you have an account with KK-DATA and the balance is sufficient. KK-DATA does not have a subscription package, fees are deducted on a per-item basis, and usage is flexible.

You need to prepare three things:

1. List of numbers that need to be filtered (can be collected by yourself, or created through the platform’s “Global Number Generation” function).
2. Recharge balance (supports USDT TRC20, the minimum is about 50 USDT, the balance will be automatically credited after recharging).
3. Clear filter conditions (for example: activated, active, gender detection, age field enabled).

If you don’t have a number pool yet, it is recommended to use the platform’s “global number generation” function to quickly generate a batch of numbers. The generation is completely free, only the screen numbers are charged per item.

Step 1: Generate or upload the number to be screened

After logging in to the [Application Console] (https://app.kkdata.cc/), enter the “Global Number Generation” module. You can choose:

• Generated by Country/Region: Covers 240+ countries, from popular markets to unpopular areas.
• Generate by number segment: If you know the number format of a certain country, you can customize the number segment.
• Import your own CSV/TXT: If you already have a ready-made number list, just upload it directly.

The generated number will be automatically saved to the number pool and can be called directly when creating a task later. It is recommended to generate or import more than 10,000 items at a time, because the screening task supports up to about 1 million items at a time, making batch screening more efficient.

Step 2: Create a Telegram screening task and select gender and age detection

Enter the “Screen Number Task” module and click “New Task”. Select the “Telegram” platform and configure the detection type:

Detection item	Description	Function
tg activation	Check whether the number is registered in Telegram	Filter invalid numbers
tg active	Detect whether the user is active within a certain time window	Screen high-interaction users
Gender detection	Analyze user information and determine gender (male/female)	Target TG male numbers
Age field	Output inferred age range (such as 25-35 years old)	Implement tg 30-year-old data interpretation

Key operating points:

• In the “Gender” filter item, check “Male” so that the task results will only export numbers judged to be male.
• In “Age Field”, check Enable. After exporting, you will see an “age label” column, such as “25-35 years old”, “30-40 years old”, etc. These are not precise ages, but ranges inferred by the model, but are sufficient for population orientation.

After the configuration is complete, the console will display the estimated cost. Submit the task after confirming it is correct. After the task is completed, you can get the results in time through Telegram notification (requires binding to the platform robot), and then export the CSV or TXT file.

How is tg 30-year-old data obtained? What does it stand for?

Many users will be curious: Where did tg’s 30-year-old data come from? Is it accurate?

KK-DATA’s gender detection not only identifies men and women, but also includes the age field. This field uses an algorithm to derive an age range by analyzing the user’s public information - such as the birthday information in the profile, the birth year implied by the nickname, social circle hints, etc. It is not obtained from ID card, passport or bank data, let alone accurate data “accurate to the age of the year”. Therefore, you interpret “about 30 years old” as a statistical label rather than an exact birthday.

Age field accuracy and usage recommendations

In actual use, the accuracy of the age field is reliable enough at the group targeting level. For example, if you filter out 1,000 numbers with the label “25-35 years old”, then the real age of this group of users is most likely to be between 25 and 35 years old (the misjudgment rate is usually within an acceptable range). But if you try to rely on it to target a user who is “exactly 30 years and 3 months old”, it is unrealistic.

Usage Suggestions:

• Use the age field as a auxiliary targeting condition instead of the only evidence.
• First filter activity (such as “active within 30 days”), and then superimpose gender and age. This can filter out a large number of zombie accounts or accounts that have been inactive for a long time.
• After exporting the results, use Excel or filter functions (such as pivot tables) to group by age tags to further optimize marketing copy.

How to combine age and activity?

Recommend a typical combination:

1. Step one: Select “tg activation” test and remove unregistered numbers.
2. Step 2: Select “tg active” detection (specify the activity window, such as “active within 7 days” or “active within 30 days”).
3. Step 3: Enable “Gender Detection” and “Age Field” to target males whose age is approximately equal to 30 years old.

This process can significantly increase the “temperature” of the filtering results - what you get is not just a gender and age tag, but an active user who is actually likely to click on your message.

Screening of tg male numbers combined with practical steps for people aged about 30 years old (taking KK-DATA as an example)

Below is a complete set of steps that you can follow directly. Suppose your goal is to screen out TG male numbers that are active and about 30 years old from 10,000 randomly generated American numbers.

1. Log in to the system: Open https://app.kkdata.cc/ and enter the console.
2. Generate numbers: Enter the “Global Number Generation” module, select the country as “United States”, and generate 10,000 numbers.
3. New Task: Enter “Screen Task”, click “New Task”, and select “Telegram”.
4. Configuration detection:
   • Check “tg activation” (required, filtering is invalid).
   • Check “tg active” and select “active within 30 days”.
   • Check “Gender Detection” and select “Male” for the gender filtering option.
   • Check “Age Field” to enable.
5. View Fees: The console will display the estimated deduction amount. Submit after confirmation.
6. Waiting for completion: After the task is completed (usually ranging from a few minutes to dozens of minutes, depending on the amount of the task), you will receive a Telegram notification.
7. Export data: Click “Export” and select CSV format. After downloading, use Excel to filter the numbers whose age tag is “25-35 years old” or “30-40 years old”.
8. Marketing Implementation: Import the filtered numbers into the private messaging tool or community invitation system to start targeted promotion.

How can the filtered numbers be used to acquire customers overseas? Things to note

After getting the tg male number list, the next step is to use it. Common scenarios include:

• Private message promotion: Send product introductions and discount information to selected users.
• Community Invitation: Invite target users to join the Telegram group for centralized operations.
• Add fans: Add friends and gradually interact.

A few key notes:

• Abide by platform rules: Telegram has frequency limits on batch messages and adding friends. If you do so too frequently, your account will be blocked. It is recommended to send no more than 30-50 messages per hour and set a random delay.
• Content must be relevant: For men around 30 years old, promoting “maternal and baby products” is obviously not suitable, but they are more receptive to “financial management”, “electronic products” and “vocational courses”.
• Verify official channels: There are many fake Telegram customer service accounts on the market, and KK-DATA provides an anti-fraud verification method on the official website. If you need to contact official customer service, please use https://t.me/kkdata_robot (two-way contact robot) or the verified customer service number @kkdata_robot.

Common misunderstandings in tg male number screening

Many teams will step into several pitfalls in actual operations. Here are the four most common ones:

1. Myth 1: Gender detection is 100% accurate Correction: Gender detection is based on probability inference from users’ public information, and the accuracy is high but not 100%. Don’t invest your entire budget just on a “male” label. It is recommended to combine it with a comprehensive assessment of activity.

2. Misunderstanding 2: How old can the age field be accurate Correction: The age field gives a range (such as 25-35 years old), not a precise value. For example, “about 30 years old” means that the user is likely to be around 30 years old, not necessarily 30 years old.

3. Misunderstanding 3: Only pursue quantity and ignore quality Correction: 1 million inactive numbers are not as valuable as 10,000 active numbers. Prioritize the activity level (active for 7 days or 30 days), and then superimpose gender and age.

4. Misunderstanding 4: Giving up before one pass Correction: The output of different time periods and different detection tasks may be different. If the results of the first screening are not satisfactory, you can adjust the active window or try again with a larger sample size.

FAQ

Question: Can tg male number screening guarantee 100% accurate gender identification?

Answer: No. Gender detection is based on the user’s public information (such as nickname, avatar, personal information, etc.) for probabilistic inference. The accuracy is high but not 100%. It is recommended to only be used as an auxiliary orientation method, combined with other fields such as comprehensive assessment of activity.

Question: Is the age field in the tg 30-year-old data calculated from the ID number?

Answer: No. The age field uses the model to analyze user information (such as birthday information, social circle hints, etc.) to infer the age range, such as the “25-35 years old” range. It cannot be used as a precise age, but it is effective enough for crowd targeting.

Question: How to screen tg male numbers who are around 30 years old at the same time?

Answer: When creating a task in the KK-DATA console, check “Gender Detection” and select “Male”, and also turn on the “Age Field” detection. The export results will include age tags, and you can locally filter out records that match “about 30 years old”. Please refer to the export field description for the specific age range.

Question: How much does it cost to screen tg male numbers?

Answer: Billing by item, no subscription package. The cost depends on the test type combination you choose (such as screening only, screening + active + gender, etc.). Please check the estimated cost displayed on the console before submitting the task. The specific unit price is subject to the real-time price of the platform.

Q: Can the filtered numbers be reused? Will I be charged repeatedly?

Answer: KK-DATA provides a “data deduplication warehouse” function, which can deduplicate across tasks and avoid repeated detection of the same number. It is recommended to perform deduplication operation before importing numbers each time to save balance.

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