TG filtering + data deduplication warehouse: how to avoid repeated detection and repeated deductions across tasks

When acquiring customers overseas, tg filtering (Telegram filtering) is the most common batch number verification action. But have you ever encountered this situation: with the same batch of numbers, you ran a TG screening test today and again next week. As a result, most of the tests were repeated and the balance was deducted in vain? What’s worse is that when multiple people collaborate in a team, everyone is submitting the same number repeatedly, causing the task queue time to be prolonged and the cost to be doubled. This article starts from the actual pain points of tg filtering and explains in detail how to use the data deduplication warehouse to automatically block filtered numbers, and cooperate with the Telegram filtering and telegram number filtering processes to achieve the lowest cost and highest efficiency for a single task.

What is tg filtering? Why is it indispensable to acquire customers overseas without the TG screen number?

tg filtering, broadly speaking, refers to verifying the real activation status, activity level, gender and other attributes of Telegram numbers, thereby screening out high-quality users suitable for private message promotion or community operation. In actual cross-border marketing, you may have a large number of numbers purchased from number generators, number pools or third parties, but among these numbers:

• A large number of unregistered Telegram (invalid numbers)
• Long-term inactivity after registration (may be deprecated)
• Unknown gender (cannot target male/female users)

Through TG Screen Number, you can check the above dimensions in batches at one time, and only retain numbers with compound conditions such as “activated + active in the past 30 days + gender is female”, and then proceed to the next step of private messaging or group joining. For example, in telegram number filtering, select the “active window” as 7 days, and then use the age field of gender detection (which can assist in interpreting people around 30 years old) to accurately locate high-value targets.

Cross-border duplication detection: Why is cross-task duplication deduction the most easily ignored cost black hole?

When many teams use the screening platform, they only focus on the unit price of a single task, but ignore the hidden waste caused by repeated testing. Let’s look at a real scenario first:

You generated 1 million US numbers from the global number generation module. In the first round, we did Telegram filtering and screened out 200,000 TG activation numbers. A week later, you planned to do WhatsApp filtering on these 200,000 numbers again, but you did not separate the original pool and the filtered pool, but directly uploaded the original 1 million numbers to the WhatsApp task. As a result, 800,000 of these 1 million have never been tested for WhatsApp before, but there are still 200,000 that have been tested for Telegram before - and what you want to do is to detect WhatsApp, and the platform will treat it correctly (different platforms detect it separately), but the problem is: 800,000 of the number pools you uploaded do not need to be tested at all (because you only want to test the 200,000 TG activated ones), but you submitted 1 million, and spent 800,000 more testing fees.

Three typical misoperations of repeated detection:

1. Upload directly without deduplication in Excel: After the team members exported the suspected results of the previous task, they directly merged them with the original pool and submitted them again without deduplication.
2. Use the same number segment generator multiple times: Each time a number is generated, the same number segment is generated repeatedly, resulting in a high degree of overlapping of numbers in subsequent rounds of tasks.
3. Different members submitted tasks independently, and the target numbers were highly overlapping: A in the team is responsible for Telegram filtering, and B is responsible for WhatsApp filtering, but both of them used the same original number pool, and they did not know that the other had submitted similar tasks.

The impact of repeated deductions on overseas teams:

• A single task may waste 10%~30% of the balance (depending on the number overlap rate).
• A large number of invalid tasks occupy system resources, lengthen task queuing time, and affect the overall order issuance speed.
• When working in a team, repeated detection leads to excessive balance consumption, frequent need for recharge, and increased management costs.

How does the data deduplication warehouse automatically avoid duplicate detection?

KK-DATA’s built-in data deduplication warehouse is designed to eliminate this problem. Its core logic is: the platform automatically records the status of the detected number (including detection time, platform, results, etc.). When you submit a new task later, the system will compare one by one to see if the number has appeared in historical tasks on the same platform. If it already exists, the number will be automatically skipped and only the number that has not been detected will be charged.

The unit of deduplication is number x platform. For example: the number +86 138xxxx has been detected in the Telegram task. The next time the same number is submitted to the Telegram task, the duplication will be removed; but if it is submitted to the WhatsApp task, the duplication will not be removed (because different platforms need to be detected separately). This not only avoids repeated deductions, but also ensures that the detection data of each platform is independent and complete.

Recommended operation process combining tg filtering and data deduplication warehouse

To connect tg filtering and data deduplication warehouse into an efficient pipeline, it is recommended to follow the following steps:

Step 1: Use the global number generation module to build the original pool

Log in to Application Console and enter the “Number Generation” module. Select your target country (such as Vietnam, Indonesia, Brazil) and generate number segments in batches. There is no charge for generation, and you can freely generate 100,000, 500,000 or even more numbers. At this time, instead of rushing for testing, determine the screening priority: for example, “Only screen women with active TG, and the age field matches about 30 years old.”

Step 2: Submit tg filtering tasks in batches according to policies

• First conduct a small batch test (for example, 5,000 items) to confirm the unit price and result fields. The console task submission page will display “Estimated number of tests” and “Estimated cost”.
• Submit the official task after the test is correct. It is recommended to divide the numbers into batches according to the source of the numbers: for example, the first batch is segment A, and the second batch is segment B to avoid submitting too large a batch (more than 1 million items need to be split).
• Before each submission, the system will display the “number of duplicates removed” to let you know the actual number of numbers deducted this time.

Step 3: After exporting the results, use the warehouse to automatically remove duplicates.

After the first task is completed, export to CSV or TXT. If you later need to perform detection on other platforms (such as WhatsApp) for the same original pool, upload the original pool directly, and the system will automatically exclude Telegram numbers that have been detected (but will not exclude those that have not been detected by WhatsApp). Warehouse records automatically without manual intervention. It is recommended to regularly check the “Deduplication” statistics through the “Task History” of the console to check the balance consumption.

Common misunderstandings and precautions in tg filtering

• Mistake the “age field for gender detection” as the precise age: This field is used to assist in interpreting the population distribution (such as “about 30 years old”), and is not accurate data at the resident ID card level. Do not use it in scenarios that require identity verification.
• It is believed that the data deduplication warehouse supports “log backtracking”: The warehouse only records the detection results of all historical tasks under the current account, and will not recalculate the data that you have imported before and detected on other platforms. If you have filtered with other tools before, you need to manually exclude it.
• Ignore real-time unit price changes: The unit prices of different platforms and different detection types may be adjusted with resource costs. The real-time price of the console shall prevail. Do not use old screenshots or third-party quotes.

How to confirm the true cost of tg filtering task?

In KK-DATA, before each task submission, the console will display the “estimated number of tests” and “estimated cost”. The fee is calculated by multiplying the number of “unduplicated” numbers by the current platform unit price. If you upload a duplicate number, the system will display the “number of duplicates removed” and the duplicate part will be automatically deducted from the estimated cost. The timing of deduction is deducted from the balance after the task is completed. New tasks cannot be submitted when the balance is insufficient. It is recommended to regularly pay attention to the [official website billing page] (https://kkdata.cc/billing/) to learn about the latest prices.

FAQ

**Q: Will the data deduplication warehouse also deduplicate numbers from different platforms? **

Answer: No. Remove the “Number + Platform” dimension to take effect. For example, if the same number has been detected in the Telegram task, but when you submit it again in the WhatsApp task, the system will consider this to be a new platform detection and the fee will be deducted normally (unless this number has been detected in the WhatsApp history). Deduplication only detects duplicates of the same number within the same platform.

**Q: Should I do tg filtering first or WhatsApp filtering first? Is there any recommended order? **

Answer: It is recommended to perform tg filtering on the core platform (such as Telegram) first, and then conduct supplementary testing on other platforms (such as WhatsApp, Line, Zalo) after filtering out high-value numbers. The order does not affect the effectiveness of the deduplication warehouse, but building the core platform first can help you obtain the most critical active user data as early as possible.

**Q: Is there a storage limit for the data deduplication warehouse? **

Answer: The storage limit of the warehouse depends on the total amount of your historical tasks. The platform will continue to retain status records of detected numbers. The specific capacity is subject to the console display. If you do not log in for a long time or delete tasks, the history records may be retained according to platform policies, but they will not be lost under normal circumstances.

**Q: Can I see the number of duplicate numbers before submitting each tg filtering task? **

Answer: Yes. Before the task is submitted, the console will display the “Estimated number of detected items” and “Number of duplicated items removed”. You can use this to confirm that there are no duplicate charges. If you find that the estimated charges are unusually high, it is recommended to check whether too many duplicate numbers have been uploaded.

Start your efficient screening process now

Duplicate detection across tasks is the most easily overlooked but most costly pitfall for overseas teams. Through KK-DATA’s data deduplication warehouse and the scientific tg filtering process, you can avoid wasting balances and improve team collaboration efficiency. Log in to the console now to experience it!

👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot

For more usage details, please view Usage Document or visit Official Home Page.