U.S. tg data gender screening tutorial: How to use gender fields for secondary stratification and understand identification boundaries

When acquiring overseas customers, US tg data (i.e. US Telegram user number) is the key goal of many overseas teams. However, simply sending a batch of numbers often results in a low conversion rate and a waste of budget. If after obtaining the number, we can first perform secondary stratification through the gender field, and then design operational strategies for different groups, the effect will be significantly improved. This article will take the KK-DATA platform as an example to explain in detail the operating steps, field meanings and identification boundaries of gender screening, helping you use American Telegram data safely and efficiently.

---

What is the gender field in US tg data?

The gender field is not the official identity information that Telegram users actively fill in (Telegram itself does not require users to disclose their gender), but a label inferred by the algorithm based on public data such as avatar, nickname, introduction, chat keywords, etc. when the number screening platform detects the number. Usually in the test results, the gender field has three values: male, female, and unknown.

The identification boundary needs to be clear

• Inference Properties: Gender recognition accuracy is usually above 80%, but will decrease due to factors such as the user using the default avatar, no nickname, and anonymous ID.
• Not an official statement: Do not regard it as a true proof of the user’s identity, nor can it be used in scenarios that require legal confirmation (such as risk control verification).
• Auxiliary Tools: More suitable as a reference for marketing targeting and in conjunction with A/B testing optimization.

---

Why is it necessary to perform gender secondary stratification on US TG data?

Single-gender filtering (for example, only retaining male or female) can already filter out some invalid users, but for more refined operations, secondary stratification needs to be done based on dimensions such as activity level and age group.

Typical scenario

• Beauty District Community Operation: Push beauty and fashion content to female users, and active users will be invited to join the group first.
• Cross-border e-commerce targeted promotion: Recommend e-cigarettes and gaming peripherals to male users aged 25-35, and recommend clothing to female users.
• TG Add Fans: First, layer the “7-day active-male” audience package, and then send private messages or invitation links to these numbers. The conversion rate is much higher than that of full mass messaging.

Benefits brought by layering

Strategy	Resource Consumption	Expected Conversion
Full mass sending of all numbers	High (including a large number of silent numbers, gender mismatch)	Low
Filter only men	Medium	Medium
Male + 7 days active + 25-35 years old	Low (high value audience)	High

Through secondary stratification, you can focus your budget on users who are most likely to convert, while reducing the risk of being reported or banned.

---

How to perform gender filtering secondary stratification in US tg data?

Use the KK-DATA console (https://app.kkdata.cc/）为例，整个流程分为三步。

Step 1: Prepare the US number to be screened (bring your own or generate one)

You can quickly obtain a US number in the “Global Number Generation” module of KK-DATA:

1. Enter the console → Global Number Generation.
2. Select the country United States (automatically match +1 segment).
3. Click “Randomly Generate” or upload a custom number segment CSV file.
4. After the generation is completed, export the number or directly add it to the screening task.

The generation is free and will only be billed on a per-item basis when subsequent screening tasks are submitted.

Step 2: Submit Telegram screening task and choose gender detection

1. Create the “Telegram Screening” task in the console.
2. Import the number to be checked (can be entered manually, uploaded a file or called directly from the generation module).
3. Check the Detection field:
   • Gender (required)
   • Activity (recommended, choose active window such as 7 days/30 days/90 days)
   • Age (optional, for further segmentation)
4. The system will automatically calculate the estimated cost (see the real-time price on the console for details), and submit the task after confirmation.
5. After the task processing is completed, the platform will automatically deduct the detection fee from the balance and send a Telegram notification (requires binding in advance).

Step 3: Export the results and use the gender field for secondary stratification

After the task is completed, click “Export” on the task details page → select CSV or TXT format. The gender fields in the exported file are male, female, and unknown. You can use Excel, Python or any data processing tool for secondary stratification.

Example hierarchical logic:

总号码 → 按性别拆分为 male、female、unknown
  male → 再按活跃度拆分为 7天活跃、30天活跃、不活跃
  male+7天活跃 → 进一步按年龄拆分为 18-25、25-35、35+

In this way, you will get a high-value audience package like “American men - active 7 days - 25 to 35 years old”, which can be directly used for subsequent marketing.

---

Best practices and considerations for gender stratification

Understanding identification boundaries: the gender field is an inferred label, unofficial identity gender

Don’t put 100% trust in the gender field. Especially for numbers whose avatars are cartoons, landscapes or photos of celebrities, or whose nicknames contain non-English characters, the false positive rate may increase. It is recommended to test in small batches (for example, 1,000 items) first and verify the accuracy before using it on a large scale.

Use the age field and activity field to create more subdivided stratification

In the Telegram screening results of KK-DATA, the age field is inferred based on personal information, language habits, etc. It is not an exact age, but it can describe the approximate range. Combining gender and age, you can filter out typical consumer portraits such as “female around 30 years old - active for 7 days” to improve placement accuracy.

Avoid over-reliance on a single field, and it is recommended to combine data from multiple platforms

After the single gender field is stratified, further cross-validation can be carried out: for example, the same batch of numbers can be submitted to WhatsApp screening numbers at the same time to see if the WhatsApp activity is consistent with Telegram; or the anti-fraud query function can be used to exclude marked numbers. Data from multiple platforms complement each other and can help you gain a more comprehensive understanding of user activity status and preferences.

---

Are the gender filtering results of US Telegram data accurate?

Objectively speaking, in normal social accounts, the accuracy of gender recognition can usually reach 80% to 90%. However, the following situations will reduce the accuracy:

• Use default avatar (grey little man icon)
• Nickname is empty or has only numbers
• The languages are Chinese, Japanese, Korean and other non-Latin characters (some models are not trained enough)
• The account has just been registered and there is very little information.

It is recommended to perform a small batch test: extract 2,000 numbers for gender detection, manually check 20 of them (based on avatars and nicknames), and estimate the actual accuracy. If it is less than 70%, you can adjust the strategy (such as relaxing gender conditions, retaining only clearly identified numbers).

---

How can the US tg data gender screening and activity detection be coordinated efficiently?

Strategy 1: First do activity screening, and then do gender detection on active numbers

• Advantages: Avoid gender testing for silent accounts (logged out, not logged in for a long time), saving costs (the unit price of gender testing is usually higher than activity testing).
• Disadvantages: You need to submit the task twice (activity first, then gender), and the steps are slightly more.

Strategy 2: Submit full field detection (gender + activity + age) at one time

• Advantages: All results are generated in one task and layered directly after exporting, which is highly efficient.
• Disadvantages: All numbers will be tested in all fields. If there are a large number of invalid numbers, part of the cost will be wasted.

Comparison summary

Strategy	Applicable Scenarios	Cost Consumption
Active first, then gender	The total number of numbers to be verified is large and the budget is limited	Lower (but second operation)
Full field at once	The number quality is known to be high (if it has been preliminarily cleaned)	Slightly higher (overall faster)

Recommended practice: If it is your first time to purchase a certain batch of US TG data, it is recommended to first use “Activity Single Field” to filter out invalid numbers (usually more than 50% are filtered), and then submit gender testing for the remaining numbers, so that the overall cost is controllable.

---

FAQ

**Q: Can the gender field in the US tg data accurately determine the user’s true gender? ** Answer: No. This field is inferred by the algorithm based on public information and is not actively declared by the user. Although the accuracy rate is high (usually 80%+), there are still misjudgments, especially on anonymous accounts or non-English users. It is recommended to serve as a reference for intention stratification, rather than an authoritative basis.

**Q: How to select only US numbers for gender testing on the KK-DATA platform? ** Answer: During the number preparation stage, generate or import numbers that are limited to the United States (+1 number segment). When submitting a Telegram number screening task, the platform will detect all numbers by default, without the need for additional country filtering. If you need to export the US results separately, you can filter by the country field (country) after exporting, or pre-filter before uploading.

**Q: After gender stratification, can we still do cross-analysis with other platforms (such as WhatsApp)? ** Answer: Yes. KK-DATA supports multi-platform screening of numbers. You can submit the same batch of numbers to Telegram, WhatsApp and other tasks separately, and then merge and export them. With the number deduplication warehouse, the same number will not be deducted repeatedly. Cross-analysis can provide a more comprehensive understanding of user profiles (for example, active on Telegram and female, but also active on WhatsApp).

**Q: What does the unknown filtered out by the gender field mean? How to deal with it? ** Answer: Unknown means that the algorithm cannot identify the gender, possibly due to insufficient information (default avatar, no nickname, blank profile, etc.). It is recommended that these numbers be included in other stratifications (such as by activity), or tested separately, rather than discarded directly. Sometimes unknown numbers may still be highly active users and are worth experimenting on a small scale.

**Q: What is the frequency of updates? Will the filtered gender field change? ** Answer: Gender detection results are based on user data at the time of detection and will not be updated automatically. If the user later changes their avatar or nickname, the results may be different. If you need the latest results, it is recommended to resubmit the detection task, and the platform will re-identify and overwrite the old data.

---

Secondary stratification of US TG data through the gender field is a practical technique to improve the efficiency of overseas customer acquisition. Understanding the boundaries of inferred tags, combining activity and age fields, and multi-platform cross-validation can make every penny you spend wisely. If you are building a TG operation data pool, you might as well try KK-DATA’s screening system.

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