How to efficiently obtain large batches of tg active data? Screen number splitting and cost control practical guide

In overseas marketing, tg activity data is one of the core indicators to measure whether users have reachable value. Whether it’s community recruitment, private message promotion, or event invitations, only users who have actually used Telegram recently can be considered effective targets. However, when the amount of data reaches 10,000, 100,000 or even millions, direct screening will encounter problems such as task timeout, repeated deductions, and confusing results. This article will guide you from definitions, challenges, practical steps to cost control to master the efficient method of obtaining large batches of TG active data.

What is tg active data? Why is bulk acquisition crucial for overseas marketing?

tg active data refers to the number that has been verified as having subscribed to Telegram and has logged in/used recently through a specific time window (such as the past 7 days, 30 days). It can better reflect the user’s real-time interaction potential than a simple “opening detection”.

For overseas marketing teams, the value of large-volume (above 10,000) TG active data is:

High reach rate of private messages: Active users are more likely to read messages and reply, avoiding wasting costs on zombie accounts.
High efficiency in attracting new users in the community: Actively adding active users to the group often results in a conversion rate that is 3 to 5 times higher than adding people randomly.
Accurate event invitations: Screen out active users of specific genders and age groups, and cooperate with event invitations to make ROI more controllable.

Obtaining large batches of TG active data is not a simple one-time task, but a systematic project involving number generation, task splitting, deduplication management and cost control.

What are the common challenges encountered in large batch screening?

Task capacity and split strategy

Most sifting platforms have a limit for a single task (for example, KK-DATA has a maximum of about 1 million items at a time), but actual large-batch tasks often far exceed 1 million. If you directly submit millions of numbers, not only may the task time out due to excessive data volume, but efficiency may also be affected due to queuing. A reasonable approach is to split the data into multiple subtasks. Each batch is recommended to have 50,000 to 200,000 items, and submit them in parallel to shorten the total time.

Repeated detection and balance waste

If the same number appears in multiple batches or tasks at different times, charges will be deducted repeatedly for each test. Especially when pooling numbers from multiple sources, the duplication rate can be as high as 30%~50%. Without deduplication, a large amount of balance will be wasted on already detected numbers.

Pay attention to the number of concurrent tasks

Although multiple tasks can be submitted at the same time, please pay attention to the task queue status of the console to avoid long task queue times due to high concurrency. It is recommended to submit each batch every 5 minutes to facilitate monitoring.

How to use KK-DATA to split large batches of tg active screening tasks?

KK-DATA provides a complete tool chain from number generation, screening to deduplication. The following are the practical steps:

Step one: Generate or import target number pool for free

Use the platform’s built-in Global Number Generation module to select the target country/number segment (such as Indonesia +62, Vietnam +84) and obtain the original number list for free.
If you have your own number CSV, you can directly upload it and import it. The generated or imported numbers can be previewed and the original list downloaded for subsequent batch submission.

Step 2: Submit screening tasks in batches and set active windows

Create a new task in the console and select “Telegram” for the platform.
Check the detection type: Activated + Active.
Set an active time window (usually 7 days, 30 days, or custom). It is recommended to use 30 days of activity as a large-scale filter for the first time, and subsequently adjust to 7 days of activity for refinement as needed.
The number of single task numbers should not exceed 200,000 to balance the task completion speed and success rate.
Multiple batches can be submitted at the same time (for example, 5 batches of 100,000 items each), and the total data volume is flexibly controllable.

Step 3: Use deduplication warehouse to avoid repeated detection

Before submitting the task, turn on “Enable deduplication warehouse” in the task settings.
Choose the deduplication scope: all platforms or by project. All subsequent tasks will automatically skip the numbers that have been detected, and no fees will be charged.
The deduplication warehouse takes effect across tasks. Even if you submit in 10 batches, the same number will only be detected once.

Tips for optimizing costs

After the first number screening, export and mark the “activated + active” numbers. When doing subsequent gender testing or activity testing again, only submit this batch of numbers instead of all the original numbers, which can save a lot of balance.

What fields can KK-DATA’s tg active detection provide? How to interpret the results?

After the task is completed, the export result (CSV/TXT) will contain a variety of fields. Commonly used fields and their meanings are as follows:

Field	Meaning
`is_registered`	Whether to activate Telegram
`is_active`	Whether active within the specified window
`active_days`	Number of active days in the past N days (window set by you)
`gender`	Gender (male / female / unknown)
`age`	Age (model inference, not ID card accuracy)
`tgid`	User’s Telegram ID (can be used for subsequent private messages)
`phone_country`	Number country

Example of active field interpretation: If the active window of the task setting is 7 days, is_active in the export result is true, which means that the user has logged in or used Telegram at least once in the past 7 days. active_days gives the specific active days. The higher the value, the higher the participation frequency.

Accuracy Note on Age Field

The source of age data is model speculation, which is suitable for crowd stratification (such as screening users “about 30 years old”), but is not suitable for scenarios that require accurate identity verification. In actual use, it is recommended to combine gender and activity for combined screening.

Combination screening skills: For example, the combination of “active in the past 7 days + male” can significantly increase the conversion rate for men’s products promotion, game guild recruitment and other scenarios. Check only the required fields when exporting to reduce file size.

How to reuse large batches of tg active data after exporting it?

Click “Export Results” on the console task details page and select CSV or TXT format.
Check the fields you need (such as mobile phone number, tgid, gender, active status) to avoid redundant data.
Use Excel, Google Sheets, or a script (such as Python pandas) to perform secondary filtering on the exported file. For example: only keep numbers is_active=True and gender=male.
Import the final data into CRM, ERP or private messaging tool (such as Telegram Bot, third-party CRM system).
Important reminder: Large batches of private messages need to comply with Telegram’s terms of use and the privacy regulations of the target country to avoid being restricted and blocked.

How to control the cost of large batch screening numbers under the billing model?

KK-DATA adopts the No subscription package, per-item deduction model. After recharging USDT (TRC20) to the balance, the corresponding number of items will be deducted from the balance after each task is completed (see the real-time price of the console for specific unit prices). Core methods to control costs:

Prioritize the use of deduplication warehouse: Eliminate duplicate inspections and directly save 20%~50% of costs.
Batch testing pricing strategy: First use a small number of numbers (such as 1,000) to test the detection costs of different active windows and genders, and then submit the large task after estimating the full cost.
Only detect necessary fields: If you only need active status, do not check gender and age detection, because different detection types have different unit prices.
Reasonable splitting of batches: Splitting large tasks into multiple batches of 100,000 to 200,000 items can avoid repeated deductions caused by the failure of a single task (detected numbers will not be repeatedly deducted, but resubmission may waste times).
Monitor balance and notification: Turn on Telegram notification after the task is completed, check the results in time, and avoid task interruption due to insufficient balance.

FAQ

**Q: What does “active” in tg active data specifically mean? How long does it take to be considered active? ** A: Activity detection will verify whether the number has logged in or used Telegram within a specified window in the past (such as 7 days, 30 days). The window length is set by the user when selecting the screen number task.

**Q: I submitted 1 million numbers, can it be processed in one task? ** Answer: KK-DATA supports a maximum of about 1 million items in a single task. However, considering the stability and speed of the task, it is recommended to split a large task with more than 200,000 items into multiple subtasks for parallel submission, and enable a deduplication warehouse to avoid repeated deductions.

**Q: After turning on the deduplication warehouse, will the same number be detected repeatedly in different projects? ** Answer: You can choose “all platforms” or “by project” for the deduplication scope. Platform-wide deduplication will take effect across all tasks, while per-project deduplication will only take effect within the same project. It is recommended to enable platform-wide deduplication for large batches of screen numbers to maximize cost savings.

**Q: Is the age field in the exported results accurate? Can it be used for precise age targeting? ** Answer: Age data is based on model speculation and is suitable for population stratification (such as “users around 30 years old”) and cannot replace ID card-level accuracy. When using it, it is recommended to combine activity and gender for combined screening.

**Q: What should I do if the balance is not enough? How to top up? ** Answer: The recharge entrance is on the “Account” page of the console. It supports USDT (TRC20) recharge, with a minimum of about 50 USDT. The balance is automatically updated after the account is received, no manual operation is required.

👉 Try it now: Log in to KK-DATA Console to start your first large-volume tg active data screening task.
💬 Having a problem? Two-way contact customer service https://t.me/kkdata_robot for real-time support.
📚 Detailed operation guide can be found in Usage Documentation.

How to efficiently obtain large batches of TG active data? Screen number splitting and cost control practical guide

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