TG US Data Cross-Batch Deduplication Guide: Reduce Duplicate Detection and Keep Customer Projects Isolated

#tg Guide to cross-batch deduplication of US data: Reduce duplicate detection and maintain customer project isolation

tg US data refers to the user information obtained after detecting the US area number (+1 segment) through the Telegram screening platform, including whether to register for Telegram, active status, gender, age and other fields. This type of data is the core resource for overseas marketing teams to acquire private customers and promote community in the North American market. However, during the batch acquisition process, we often encounter a headache: a large number of duplicate numbers generated or imported in different batches, leading to repeated detection, which wastes balance and slows down the progress of the task. KK-DATA’s data deduplication warehouse function is designed to solve this pain point - it can automatically identify detected numbers in cross-batch tasks to avoid repeated deductions. It also supports project isolation to ensure that the TG US number data of different customers are not confused with each other.

What is tg US data? Why do we need to remove duplicates across batches?

tg US data refers to valid contact records obtained after screening US Telegram users (usually +1 mobile phone number). For example, if you randomly generate 100,000 US number segments through the global number generation module, and then submit the Telegram activity detection task, the results will include labels such as “activated”, “active”, “gender” and “age”. This data can be used for targeted private messages, group invitations or advertising.

The necessity of cross-batch deduplication comes from the actual business process:

• Generate numbers multiple times: You may generate a batch of US numbers on Monday and another batch on Wednesday. There may be 10%–30% overlap in the two batches.
• Duplicate import of old data: lists of numbers obtained from historical files or partners, which may have been detected.
• Parallel multi-client projects: processing US data for customer A and customer B at the same time. If the same account is used, it is easy to mix in duplicate numbers.

If deduplication is not performed, the same number may be paid to detect multiple times, resulting in increased costs, and there will be redundant data in the export results, increasing the workload of cleaning.

The core working principle of KK-DATA data deduplication warehouse

KK-DATA’s deduplication warehouse is an independent module that records the number and detection type combinations in all submitted and completed screening tasks. When you submit a new task, the system will automatically compare the records in the warehouse, filter out the numbers that have been detected, and ensure that only undetected numbers enter the deduction queue.

Input and output of deduplication warehouse

Input: List of numbers imported via the Global Number Generation module or manually uploading a CSV/TXT file.

Output: After warehouse matching, only numbers that have never been detected enter the screening task; the detected numbers are marked as “duplicate” and displayed in the task report.

For example, you submit a task containing 100,000 US numbers, 30,000 of which have been detected as “tg active” in a previous task. Then the actual deduction this time is only for the remaining 70,000 numbers. The duplicate 30,000 units will not be deducted.

Customer project isolation: How to ensure that TG US data of different customers is not confused

The deduplication warehouse performs global deduplication on numbers by account level by default. If you serve multiple customers at the same time, you need to use the Group Label or Batch Note function to manually distinguish them when submitting tasks. Specific methods:

• Fill in the customer ID (such as clientA_202503, clientB_202503) in the “Batch Name” or “Remarks” field on the task submission page.
• The deduplication warehouse will record the batch information corresponding to each number. When the same number appears again in a new task, the system checks whether the number has previously belonged to the same batch. If the batches are the same, they will be considered duplicates; if they are different, they will not be automatically blocked.
• When exporting results, you can filter by batch in the task history to obtain independent data files.

Stricter isolation suggestion: Create an independent KK-DATA sub-account for each customer so that the deduplication warehouse is completely separated and does not interfere with each other.

How to configure deduplication rules for tg US data for users or projects?

The following steps apply to the KK-DATA console (https://app.kkdata.cc/），帮助你灵活配置去重策略。

Console operation path

1. Log in to the console and enter the “Number Library” or “Data Deduplication” page (the name depends on the actual interface).
2. When submitting a new filter task, find the “Enable data deduplication” switch (enabled by default). You can turn this off to force repeated detection of the same number.
3. If you need to deduplicate a specific batch, you can select “All tasks” or “Specified batches” in “Deduplication scope”.
4. After the task is completed, check the “Deduplication Report” in the task details to understand the number of duplicate numbers filtered and the estimated balance saved.

Common configuration scenarios: single customer and multiple batches vs multiple customers and single batch

Scenario	Duplication Strategy	Operation Suggestions
Multiple batches for a single customer	Global deduplication	Keep “Enable data deduplication” turned on to submit all batches of the same customer uniformly to avoid repeated detection.
Multiple customers in a single batch (same account)	Group isolation	Add customer tags to each batch and filter by tags when exporting. If customers do not need to share the number library, it is recommended to use independent accounts.

tg US data screening process example: from generation to deduplication export

Suppose you need to accumulate 50,000 TG US number data for the North American market, and the proportion of active users shall not be less than 30%. The complete process is as follows:

1. Global Number Generation: Log in to the console, enter “Global Number Generation”, select the country “United States” (+1), and enter the quantity 50,000. The generation is free, you can directly download the original number CSV, or submit it directly to the screening number queue.
2. Submit Telegram activity detection task: On the screen number page, select the detection type “tg active” (optional active windows such as 30 days, 7 days, etc.), and paste or upload the generated number list. Make sure “Enable data deduplication” is turned on.
3. System automatically removes duplicates: If some numbers have been detected in historical tasks, the warehouse will filter out the duplicate parts. The number of numbers actually submitted for testing this time will be less than 50,000, and the deduction amount will be reduced accordingly.
4. Task Complete: After receiving the Telegram notification, log in to the console to export the results. Select CSV or TXT format, and each line of the obtained file contains fields such as number, active status, registration time, and active days.
5. Subsequent reuse: When numbers are generated again in subsequent batches, if the new number overlaps with the detected number, it will be deduplicated again to avoid repeated payment.

The impact of cross-batch deduplication on balance and task efficiency

After deduplication is enabled, the same number will no longer be billed repeatedly, directly saving costs. At the same time, because the system skips detected numbers, task processing speed is significantly improved (the number of invalid detections is reduced). For example, if a task that originally takes 5 minutes to process has a repetition rate of 30% and the actual detection volume is reduced by 30% after deduplication, the task may be shortened to 3.5 minutes.

In addition, the data deduplication warehouse also reduces redundancy in export results. You no longer need to manually deduplicate and merge multiple files, which improves data operation efficiency.

Common misunderstandings and precautions in cross-batch deduplication

• **Myth 1: The original data will be lost after deduplication. **
  Won’t. The deduplication warehouse only intercepts duplicate submissions of new tasks and does not affect files exported by historical tasks. You can still view the full results of each filter in the task history.

• **Misunderstanding 2: Deduplication affects existing task results. ** No impact. Completed screen size results remain unchanged. Deduplication only takes effect on newly submitted tasks.

• **Myth 3: It is impossible to perform multiple different tests on the same number. **
  Can. The deduplication warehouse only intercepts identical combinations of detection types. For example, the same number can detect “tg active” and “tg gender” successively without conflicting with each other. If you want to verify activity changes, just turn off deduplication.

• Note: If some numbers need to be detected repeatedly (such as verifying monthly activity fluctuations), you can manually turn off “Enable data deduplication” when submitting the task. But please note that all numbers will be re-billed after closure.

FAQ

Question: After deduplication of TG US data, can the historical test results still be found?

Answer: Yes. The data deduplication warehouse only prevents repeated submission of new tasks and will not delete or overwrite your previously completed task export records. You can still view the complete results of each filter in the task history.

Question: Different customers use the same account for their projects. How to ensure that their TG US number data does not interfere with each other?

A: You can use “group labels” or “batch notes” to distinguish customers when submitting tasks. The deduplication warehouse deduplicates by account level by default, but you can export the result files of different batches to isolate data. If stricter isolation is required, it is recommended that each customer use an independent account.

Question: After cross-batch deduplication is enabled, can the same number be tested multiple times with different detection types (such as checking activity first and then checking gender)?

Answer: Yes. The deduplication warehouse only intercepts identical combinations of detection types. If you submit “tg activity detection” and “tg gender detection” for the same number, the system will execute them separately and will not be regarded as duplicates.

Question: If I already have a previously filtered tg US data file, how can I import it into the deduplication warehouse?

Answer: You can upload the numbers in the CSV or TXT file to the deduplication warehouse through the “Number Import” function of the console (the generation module can also be used directly). The warehouse will automatically compare and mark existing numbers to avoid subsequent repeated consumption.

Question: Will the deduplication warehouse accidentally delete the duplicate data I need?

Answer: No. The deduplication warehouse only blocks duplicate numbers when submitting new tasks and will not modify any files you have exported. If you really need multiple detection results for the same number (for example, to verify activity changes), you can turn off the “Enable data deduplication” option when submitting.

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Mastering the tg US data cross-batch deduplication technique can significantly improve the cost-effectiveness and data quality of batch screening numbers. KK-DATA’s deduplication warehouse module makes this process automated and configurable. Whether you are dealing with a single project or multiple client tasks, you can achieve efficient output of TG US number data by properly configuring deduplication rules.

Act now: 👉 Log in to the console to start screening numbers If you have any questions, you can use the two-way customer service https://t.me/kkdata_robot to get real-time help.
For more detailed configuration guide, please refer to the official documentation: https://docs.kkdata.cc/