Practical guide to filtering million-level American TG male data: the whole process of batch splitting, deduplication, notification and export

In overseas marketing, US TG Male Data has become a golden resource to accurately reach North American male users. Whether it is cross-border e-commerce product promotion, social e-commerce community invitations, or affiliate marketing for male users, being able to obtain and verify the activation status, activity and even gender information of American Telegram male numbers in batches means that the marketing team can greatly reduce the cost of invalid contact. However, in the face of hundreds of thousands or even millions of screening requirements, if you just submit large batches of numbers directly, it is easy to encounter problems such as timeouts, balance freezes, and repeated deductions. This article breaks down how to efficiently complete large-scale US Telegram male data screening from a practical perspective, covering batch splitting, data deduplication, task notifications, field interpretation and common misunderstandings, and helping overseas teams establish reusable operation processes.

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What is US TG male data? Why do you need it to acquire customers overseas?

US tg male data refers to a collection of US phone numbers that are marked as “open” and have a gender of “male” after being detected by the Telegram platform. This type of data usually includes mobile phone number, TG activation status, TG activity, gender, age (prediction), TGID and other fields. In the overseas customer acquisition scenario, having high-quality and large-scale American male user numbers allows the operations team to:

• Targeted community invitation: Invite male users into topic groups (such as game, fitness, electronic product discussion groups) in batches to increase community activity and conversion;
• Private message promotion: Send personalized messages for male preferred categories (razors, watches, men’s skin care products) to avoid female indifferent users;
• User Research: Quickly collect feedback from American male users on product functions to assist localized operation decisions;
• Verify localized signals: By detecting activity and gender distribution, determine whether the target number pool matches the portrait of a real North American male user.

It should be noted that the gender and age fields are predicted by the platform based on the model. The accuracy is affected by the interface and sample size and should not be regarded as accurate data at the ID card level. However, as an initial screening and hierarchical dimension, it has been able to help the team significantly increase ROI.

Typical application scenarios of American TG male data

• Community Invitation: Import male users into Telegram groups in batches, with automatic welcome and group entry guidance, commonly used in CPA/CPS promotions.
• Targeted private message promotion: Combined with the activity field, priority is given to American male users who have been active in the past 30 days, resulting in a higher message response rate.
• User Survey: A sample of American men over 30 years old is invited to fill in a questionnaire or participate in an internal test. The data recovery rate is better than random sampling.
• Localized operation verification: When testing new markets, first use a small amount of American male data to verify product acceptance before deciding whether to expand investment.

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One hundred thousand American Telegram male data task: Why do we need to split the batch?

The KK-DATA console supports up to about 1 million numbers in a single task (the upper limit varies slightly for different detection types), but this does not mean that cramming 100,000-level numbers into it is the best practice. In actual operations, large-volume tasks may face the following risks:

• Processing Timeout: An extremely long queue may cause the server connection to be interrupted and the task to be automatically canceled by the system.
• Balance Freeze: After the task is submitted, the system will freeze the balance according to the estimated cost. If the task is abnormal during the process, it will take time to unfreeze.
• Unstable result return: Very large tasks may fail due to memory limitations during the parsing and export stages, and the return is incomplete.

Therefore, splitting batches is the core strategy to ensure success rate.

General principles for batch division

Dimensions	Recommended values	Description
Number of numbers in each batch	50,000-100,000	Taking into account processing speed and stability; when the network condition is good, you can try 100,000-200,000.
Number source	It is recommended to separate the numbers imported from the same source or mixed sources	The numbers imported by CSV and the numbers generated by the global number generator are recommended to be submitted in batches to avoid format inconsistencies.
Detection type combination	Try to keep the detection type in the same batch as single as possible	For example, only detect “tg activated + tg active”. Do not mix gender detection with other detections in the same batch to avoid performance degradation due to an increase in fields.

Practical suggestions: Verify in small batches first, then promote in batches

Before submitting a 100,000-level task, it is strongly recommended to conduct a small batch verification with 1,000–5,000 numbers. Operation steps:

1. Randomly select 1,000 numbers from the target US number pool;
2. Submit the “US TG Male Data” test (including activation, activity, and gender);
3. Check the returned results: whether the proportion of males is within the expected range (for example, male users of Telegram in North America account for about 50%–60%); whether the activity meets the target window;
4. Confirm that the testing cost is reasonable (the console will display the estimated cost);
5. After passing the verification, the remaining numbers will be split according to 50,000 numbers/batch.

This method of “checking one ticket and then flushing it” can avoid large-scale waste caused by errors in number quality and detection configuration.

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Cross-task data deduplication: How to avoid duplicate detection wasting balances?

For historical number libraries accumulated by the team, or numbers purchased multiple times from different channels, the duplication rate may be as high as 10%–30%. If duplicate numbers are not removed, fees will be deducted for each detection, resulting in unnecessary costs.

KK-DATA provides the data deduplication warehouse function. Before submitting the number screening task, the system will automatically compare the detected number library, mark the detected numbers, and only detect and charge new numbers. The entrance to the deduplication operation is in the “Deduplication Warehouse” module of the console. It supports uploading CSV/TXT files for deduplication. You can also directly check “Enable deduplication” when submitting the task.

The actual cost impact of deduplication on billion-level batch tasks

Suppose you need to process 100,000 American Telegram numbers to screen male users. If the duplication rate is 10%, the duplicate 10,000 numbers should be deducted repeatedly. After deduplication is enabled, these 10,000 items will no longer be billed, and are estimated based on the unit price of the test (see the real-time price on the console for details), which is equivalent to saving 10,000 times. For larger million-level projects, the cost savings of deduplication are even more significant. Therefore, deduplication should not be considered an optional feature but should be included as a standard operating procedure.

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Task completion notification: How not to miss the return of the 100,000-level results?

Waiting for screen number results can often take hours or longer (depending on queue load). Manually refreshing the console is neither efficient nor easy to miss. KK-DATA supports sending task completion notifications through Telegram bots.

Configuration steps:

1. Open the console (https://app.kkdata.cc/）并登录；
2. Enter “Notification Settings”;
3. Click the “Bind Telegram” button and follow the instructions to add the robot;
4. Send /start to the robot in Telegram to complete the binding;
5. After submitting the task, just wait for the robot to push the message.

In this way, even if you close the browser, you can know the results and start exporting immediately.

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US Telegram gender data export: field description and file format selection

When the screening task is completed, you need to download the result file in the console. Common export formats are CSV and TXT. Different formats have their own advantages:

Format	Applicable scenarios	Description
CSV	Data analysis, secondary cleaning (Excel/Google Sheets)	Contains all available fields (mobile phone number, tg activation, tg active, gender, age, tgid, detection time, etc.) to facilitate filtering and sorting.
TXT	Directly import mass sending tools and API calls	Only keep the mobile phone number or tgid, the file size is small and the processing speed is fast.

Field Interpretation: How to use the age information in the US TG male data?

In the exported field, Age is a predicted value that represents the possible age range of the user (such as 20-25, 30-35, etc.). This field can help you filter your target age group:

• Want to reach young male users → Filter records whose age field is between 18-30 years old;
• Need to focus on middle-aged men → Screen records aged 30-40 and above.

Note: The age field is the result of model estimation, not the real date of birth. The accuracy is limited by the platform interface. It is recommended to only use it as a preliminary basis for grouping. Do not use precise expressions such as “30-year-old male” directly in the copy. A more prudent approach is: after exporting, perform a second cleaning combined with other dimensions (such as activity, group interest) to improve the credibility of the portrait.

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Common misunderstandings and precautions in large-scale screening of American TG male numbers

In actual operations, many teams are prone to making the following mistakes due to operating habits and lack of experience:

1. Ignore deduplication Directly uploading the old number database will result in repeated deductions. It is recommended to use the “Data Deduplication Warehouse” for comparison every time before submitting a task.

2. Do not split the batch if it is too large Attempting to submit 200,000+ numbers at once caused the task to time out or be interrupted. Adhere to each batch of 50,000-100,000 items and operate steadily.

3. Balance not checked The fees required for large batch tasks may exceed the account balance, causing submission failure. Be sure to check your available balance and estimated charges in the console before submitting.

4. Ignore notification settings Relying on manually refreshing the page will miss the result export time. Be sure to bind the Telegram bot to receive notifications.

5. Excessive expectations for field accuracy It is believed that the gender and age fields are 100% accurate and can be directly used for precise operations. It should actually be used as a probability signal and cross-validated with other data sources.

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Summary: Screening workflow for millions of American TG male data

The following is a set of reusable standard operating procedures (Checklist), which the team is recommended to execute in order:

1. Number preparation

   • Use the global number generator to generate a US number, or import an existing CSV/TXT;
   • Estimate the total number of numbers and calculate the required testing fees (see the real-time price on the console for details).

2. Small batch verification

   • Randomly select 1,000-5,000 items and submit an American TG male data test;
   • Check the quality of the returned fields (opening rate, male proportion, activity distribution);
   • After confirming that the fee is reasonable, proceed to the next step.

3. Enable deduplication

   • Upload all numbers to be detected to the data deduplication warehouse;
   • Or check “Enable deduplication” when submitting the task.

4. Split the batch

   • Divide the numbers according to the principle of 50,000-100,000 per batch;
   • Separate batches of numbers from different sources or different test types.

5. Binding Notification

   • Bind Telegram bot in the console (https://t.me/kkdata_robot）；
   • Set notification recipients (individual or group).

6. Submit task

   • Select the detection type (tg activation, tg active, gender, etc.);
   • Confirm estimated cost < current balance;
   • Submit the task and record the task ID.

7. Wait for notification → Export results

   • After receiving the push from the robot, enter the task details to download CSV or TXT;
   • If the result is incomplete, contact customer service for assistance and try again.

8. Second Cleaning and Import

   • Filter by gender, age, and activity in Excel;
   • Remove obvious abnormal data (such as age field being 0 or null);
   • Import the final number into the mass sending tool or API service.

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FAQ

Question: How long does it take to complete the 100,000-level American TG male data task?

Answer: The time to complete the task is related to the detection platform, server load, and the number of orders queued. Usually 100,000-level tasks can be returned within a few hours, but the peak period may be extended. It is recommended to avoid submission during peak hours of the platform (such as evening hours in North America).

Question: Does American tegram male data (age 30+) support precise screening?

Answer: The platform provides the age field in gender detection, which can be used to initially screen people over 30 years old. This is the model prediction result. The accuracy is affected by the platform interface. Please do not use it directly for precise user portraits.

Question: How to avoid repeated deductions when screening American TG male numbers in large quantities?

Answer: Use the “Data Deduplication Warehouse” function of the console. The system will automatically compare the detected records before submitting the task. Duplicate numbers will only be deducted once. It is recommended to unify the manual collection and historical import numbers before deduplication and submission.

Question: After the results are exported, how to determine whether the American Telegram male data is valid?

Answer: In the export field, the “tg activation” mark is “yes” and the “tg active” number is highly active within the specified window (such as the past 30 days). The gender field (male) and age field (if required) are used for auxiliary filtering. It is recommended to make a comprehensive judgment based on the fields rather than relying on a single field.

Question: What is the “tgid” in the US tg male data? What’s the use?

Answer: tgid is the unique identifier of a Telegram user. After exporting this field, you can use the API to send messages directly (without parsing the mobile phone number), and support advanced operations such as targeted exclusion and user grouping. Suitable for operations teams that require precise access.

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Through the above steps, you can complete hundreds of thousands or even million-level US TG male data screening efficiently and at low cost. Go to the console immediately to start your first task, or contact customer service through two-way for free consultation and technical support.

👉 Log in to the console https://app.kkdata.cc/ Two-way contact customer service https://t.me/kkdata_robot Complete usage documentation https://docs.kkdata.cc/