Use gender filtering to accurately obtain American TG male data: from field understanding to stratification practical guide

In overseas marketing, American Telegram users have always been a high-value target group. However, blind mass messaging is not only inefficient, but can also easily trigger platform risk control. How to further refine “U.S. Telegram users” into “U.S. male Telegram users”? The Gender Filter function provides a clear data entry for this. This article will teach you how to use American TG male data correctly and avoid common misunderstandings from three aspects: field nature, identification boundaries and hierarchical methods.

What is the “gender filtering” of US TG male data?

The essence of gender screening is to mark fields such as “male” or “female” in the screening results based on the gender options (self-filled) in the Telegram user’s public profile or the platform algorithm inference. You can check “Gender Detection” when submitting the screening task, and the “gender” column in the final exported CSV file will contain the corresponding value.

To be clear: This is not identity verification, nor is it real-name authentication. The accuracy rate usually exceeds 90%, but there is a certain error due to factors such as user filling habits and privacy settings. Only by understanding this boundary can we use the US TG male data appropriately.

Why does overseas marketing require American Telegram male data?

The American Telegram male user group has clear commercial value in multiple industries:

• Cross-border e-commerce male category promotion: sports equipment, e-cigarettes, men’s skin care products, outdoor equipment, etc., directly targeting male users has a higher conversion rate.
• Mobile Game/E-Sports Promotion: There is a high proportion of male players in the United States. To push in-game purchases through the Telegram community, gender needs to be screened first.
• Financial/Investment Products: Cryptocurrency trading platforms, stock consulting, cross-border payments, etc., usually require high net worth male users.
• Telegram Community Operation: Direct messages for male interest communities (cars, technology, fitness) to attract new users, which can greatly reduce invalid interruptions.

Gender filtering allows you to quickly target “American male users” from “all US users”, saving budget while improving reach.

How to understand the identification boundary of gender field?

Gender field ≠ ID card level verification

The gender field comes from two sources: one is the gender (male/female/other) selected by the user in the Telegram profile, and the other is inferred by the platform algorithm based on the avatar, nickname, description, etc. Neither can achieve official document-level accuracy. It is recommended that gender screening be used as a preliminary screening layer and not as a basis for final decision-making.

Combination of gender field and age field

Many filter tasks also return an age field (such as the “age” column). For example, some gender detection results include age inferences such as “about 30 years old.” Filtering the combination of “American + Male + Age 25-40” can further focus on the target group. The age field is also based on algorithmic speculation and is not accurate, but it can significantly improve stratification efficiency.

How to conduct secondary stratification of American TG male data?

The following steps demonstrate the complete process from number preparation to result stratification, based on KK-DATA Console operations.

Step 1: Prepare the US number pool

You have two ways to get a US number:

• Global Number Generation: Select the “United States” country in the console to randomly generate US number segments in batches (240+ countries/regions supported). This function is free and can be input directly as the screen number after generation.
• Import your own numbers: Upload a CSV/TXT file containing a US number, and the system will automatically identify the number segment.

Note that a single task supports a maximum of about 1 million items, and it is recommended to batch them as needed.

Step 2: Submit the Telegram screening task and select “Gender Detection”

1. Log in to the console and enter “New Task” → “Telegram Screen ID”.
2. Upload the number file or select the generated number segment.
3. Check “Gender” in the detection type (optional, check “Activity”, “TGID Export”, etc. at the same time).
4. The system will automatically display the estimated fee (billed by item, please see the real-time price on the console for unit price details).
5. Submit the task and wait for completion (you can receive completion reminder through Telegram notification).

Step 3: Export the results and stratify using the gender field

After the task is completed, export the CSV file. You’ll see a structure similar to this:

phone	country	gender	active_last_7d	age_estimated	tgid
+1 555…	US	male	true	32	1234567890
+1 555…	US	female	false	28	1234567891
+1 555…	US	unknown	true	null	1234567892

Now you can do secondary layering:

• Basic filtering: Filter by “gender = male” in Excel/CSV.
• Accurate stratification: Combine “active_last_7d = true” to filter highly active men; then “age_estimated” to filter the 30-40-year-old age group; you can also use “tgid” to import third-party group analysis tools.

What should I pay attention to when using US TG male data?

Avoid over-reliance on single-gender fields

Because there are errors in gender recognition, it is recommended that the filtered data be verified twice through Group Active Behavior or Communication Interaction. For example, with “tgid” export, use third-party tools in Telegram to detect whether the user is active in a specific topic group.

Use data deduplication warehouse to avoid repeated deductions

If multiple screening tasks contain the same number, KK-DATA’s data deduplication warehouse will automatically identify and skip the detected numbers to avoid repeated deductions. It is especially suitable for scenarios that require continuous screening and updating of number segments to effectively control costs.

Balance management and billing reminder

There is no subscription package, everything is charged on a per-item basis. The estimated cost will be displayed before task submission. It is recommended to submit in batches after recharging to avoid task failure due to insufficient balance. Deposit only supports USDT (TRC20), with a minimum of about 50 USDT. For details on the unit price, please see Console Real-time Price.

How to effectively use gender stratification in different application scenarios?

Men’s consumer goods promotion scene

Let’s say you promote a men’s skin care kit. First use gender to filter out American male numbers, then filter out users who have been “active in the past 30 days” and send promotional information. Compared with no filtering, the conversion rate is usually increased by 2-3 times. You can also exclude minors (eg, less than 18 years old) in conjunction with the “age_estimated” field.

Financial/investment product scenarios

The target users of cryptocurrency exchanges and cross-border financial management platforms are often men aged 30-50. Recommended combination screening: American + Male + Active + Age ≥30. After exporting, you can batch check whether the user has joined the relevant investment group through tgid for further filtering.

FAQ

**Q: What is the approximate accuracy of the gender field of US TG male data? ** Answer: According to actual user feedback, the gender recognition accuracy is usually between 85% and 95%. However, due to the influence of Telegram user privacy settings, some numbers may display “unknown”. It is recommended as a preliminary screening tool and should not replace real-name authentication.

**Q: Can I export only male numbers that have been active in the past 7 days? ** Answer: Yes. When creating a screening task, check both “Gender Detection” and “Activity Detection”, and filter by “gender=male” and “active_last_7d=true” after exporting the CSV. You can also set the active window (such as 7 days/30 days) in the task parameters.

**Q: What is the format of the gender field in the exported file? ** Answer: Common values are “male”, “female”, and “unknown”. Some tasks also include a “gender_source” field to indicate the source of the data (such as “self_filled” or “ai_guess”).

**Q: If there are duplicate numbers in the number pool, will there be repeated deductions? ** Answer: No. KK-DATA has a built-in data deduplication warehouse. The same number will only be detected once in different tasks. Subsequent tasks will be automatically skipped and historical results will be used to avoid repeated deductions.

**Q: Is there an additional fee for gender screening? ** Answer: Gender testing is a type of testing for Telegram screening numbers. It is charged based on the number of ordinary tests. The unit price may be different from other testing types. The console will display an estimated price before submitting the task, please refer to the real-time price.

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The above is a practical guide for gender screening and stratification of American TG male data. Now you can log in to the KK-DATA Console to start the first screening task, or contact customer service through two-way https://t.me/kkdata_robot to obtain usage guidance. For more function introduction, please view the official documentation https://docs.kkdata.cc/.