US TG data hierarchical screening guide: accurately locate high-value users by activation, activity, gender and age fields

In B2B overseas customer acquisition and community operations, many people buy a bunch of lists of American numbers and add Telegram friends in batches. It turns out that most of the numbers are either unregistered, offline for a long time, or even blocked after just sending a few messages. The root of the problem is: Original number list does not equal effective marketing resources. This article starts from the perspective of data stratification and teaches you how to use the KK-DATA platform to filter US TG data at four levels of “activation → active → gender → age” to make every contact action more valuable.

What is data tiering? Why does US TG data need to be stratified?

Data stratification refers to filtering the same batch of numbers step by step according to different dimensions. Each layer only retains data that meets the conditions and gradually reduces the target pool. For US Telegram data, the typical four-layer structure is as follows:

Hierarchy	Filter dimensions	Main purpose
First level	Activation detection	Filter empty/unregistered numbers
Second layer	Activity detection	Distinguish silent users from recently active users
Third layer	Gender identification	Targeting male or female users
The fourth layer	Age field	Interpret the age distribution of the population (such as around 30 years old)

Direct marketing without layering often faces three bottlenecks: Invalid numbers waste sending volume, high silence rates lead to low ROI, and random additions lead to reports and account bans. Layering can effectively reduce marketing costs - with each layer passed, the amount of data is reduced, and subsequent detection and contact costs are also reduced accordingly.

First level: Activation detection - Screening of “real registered US Telegram accounts”

Activation test (also called registration test) confirms whether the number has registered a Telegram account. This is the first and most important step in screening US TG data. For US Telegram data, accounts with development/verification codes must be filtered first, otherwise sending messages to unregistered numbers will only waste resources and even trigger the anti-spam mechanism.

How to activate detection (pain point: cost avoidance)

The process is simple:

In the KK-DATA console, upload the US number list (supports CSV, TXT and other formats).
Select the “Telegram activation detection” task type and submit the task.
The system automatically detects and exports the “activated” result after completion.

cost avoidance advice

Don’t do activity detection or gender detection on all numbers at the beginning, as that will waste a lot of balance. Go through the activation test first, and only perform subsequent operations on the numbers that have been activated, which can save 30% to 60% of the detection fee.

Which US numbers are recommended to be skipped directly?

During the activation detection phase, the filtering rates of the following types of numbers are usually very high:

Invalid number segment: Some virtual operator numbers in the United States (such as some unregistered numbers of Google Voice) have low TG registration rates.
Privacy Number/Disposable Number: This type of number usually does not register a long-term account.
Super old number segment: An empty number pool that has not been operated for a long time.

You don’t have to manually exclude these number segments. KK-DATA’s global number generation function can assist in pre-screening - first generate qualified US number segments and then submit them for activation testing, which is more efficient.

Second layer: activity layering - distinguishing silent users from active users

The activation test tells you whether the number is registered with a TG account, but registration does not mean active. Many accounts may not be logged in for months or even years. Activity Detection can customize the activity window (such as online in the past 7 days, 30 days, and 60 days), and divide the US TG screening data into three layers:

High Hot Users: Online for the past 7 days, the fastest response, suitable for immediate access.
Medium Hot Users: Online for the past 30 days and still have stable and active habits.
Cold account: An account that has not been online for more than 60 days can basically be regarded as a zombie account.

How activity data can help reduce account suspension and complaint rates

Active users are more likely to open messages and participate in interactions, so the reporting rate is significantly lower than cold accounts. Sending a large number of private messages to cold accounts can easily trigger Telegram’s anti-harassment mechanism, resulting in account closure. Using activity as a filter can significantly improve security and conversion rates.

Activity layering combined with task volume control

When marketing in bulk, it is not recommended to reach all active users at once. Recommended strategy:

Export in batches: Only 1000~5000 active numbers in the past 30 days will be exported each time.
Interval sending: Wait 6~12 hours after each sending before sending the next batch.
Dynamic Adjustment: If the same batch of active users has a high open rate, the batch size can be increased appropriately.

In KK-DATA, you can set “Only keep active for the past 30 days” in the activity detection task and export it without manual filtering.

The third layer: Gender identification - targeted customer acquisition for men

Telegram gender detection returns gender field (male, female, etc.), which can be used together with the age field. For US TG data, male users are usually more popular in the following scenarios:

B2B business (the proportion of male decision-makers in companies is relatively high)
Men’s interest communities (investment, blockchain, fitness, cars, etc.)
Cryptocurrency and trading projects (dominated by male users)

Practical boundaries for gender fields

The platform’s gender recognition is based on Telegram’s public information and user behavior inference. It is an accurate non-real-name authentication level, but sufficient to meet group-level targeting needs. For example, a list of numbers with “more than 80% male proportion” can be filtered out and used for invitations to specific groups or private message promotions.

Operationally, you need to submit the “Activity Detection” task first or directly submit the “Gender Detection” task (supported for already activated numbers). The gender field will be included in the export results.

The fourth layer: Age field - clever use of “about 30 years old” people to interpret

The age field exists in the gender detection results and can be used to filter or interpret people around 30 years old (usually 25 to 35 years old). This group of people is on the rise in the workplace and has strong consumption decision-making ability. They are especially suitable for recruitment, vocational training, financial management, and corporate service projects.

Notes on using age data

The age field does not have real-name authentication level accuracy and is recommended as a rough screening dimension rather than the only basis for judgment. For further verification, the activity and gender fields can be cross-used. Do not make up the “accurate age at the ID card level”.

In actual business, you can set the conditions: activity ≥ last 30 days + gender is male + age between 25 and 35 years old, and get a batch of high-value American Telegram data.

Practical suggestions: How to combine four layers of fields to output U.S. TG hierarchical data

Here is an example of a typical layered process:

Original Data: 10,000 US numbers.
Open detection: 7500 (75%) remain after filtering.
Activity Detection: Set to be online in the past 30 days, and 3200 (32%) remain after filtering.
Gender Test: Screen males, leaving 1800 (18%).
Age field crossover: Only 25~35 years old are retained, leaving 1000 (10%).
Export: TG ID + phone number + gender + age fields.

Each layer of filtering reduces the amount of subsequent data, which not only reduces the cost of screening, but also avoids the counter-effects of marketing to invalid users. If the final amount of data is too small, the active window (such as changing to the past 60 days) or the age range (expanding to 20 to 40 years old) can be appropriately relaxed to balance efficiency.

FAQ

Q: Are the activation detection and activity detection of US TG data the same task? Can it be done in one go?

Answer: No. Activation detection and active detection belong to different types of detection tasks and need to be submitted separately**. It is recommended to submit the activation test first, and then submit the activity test after obtaining a valid number to avoid wasting the activity test on unregistered numbers.

Q: After gender field detection, what does the exported data contain?

Answer: The export results usually include number, TG ID, gender field, age field (if enabled), etc. The specific fields are subject to the console export interface. It is recommended to verify the match between the gender field and the expected population after exporting.

Q: Can I individually screen American Telegram accounts that are “active in the past 30 days and are male”?

Answer: Yes. You need to submit the Activity Detection task first (set the active window to 30 days), and then submit the Gender Detection task for the number list of active results. The system will display the estimated cost before submitting the task.

Question: What is the accuracy of users “about 30 years old” filtered out by the age field?

Answer: The age field is based on Telegram user public information and platform statistical inference. Non-real-name authentication level accurate data can be used as a reference for population distribution (such as the overall younger or more mature). It is not recommended to be used for ID card age level verification.

Q: How is the cost of stratifying the US TG dataset calculated? Is it accumulated based on the number of tests?

Answer: Yes. Each level of detection (activation, activity, gender, etc.) is charged based on the number of items**, and the unit prices for different detection types are different. Please check the real-time price and estimated fee before submitting the task in the console. Pay-as-you-go billing is required.

The above four-layer screening method can help you transform extensive U.S. TG data into high-precision marketing assets. In actual use, it is recommended to test with small batches of data first, observe the filtering rate of each layer, and then adjust the parameters for large-scale tasks.

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U.S. TG data hierarchical screening guide: four steps of activation, activity, gender and age to accurately locate high-value users

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