How to determine the rescreening period for US data? Complete guide to number status changes, timestamps and update trigger conditions

Teams that work on acquiring customers in the United States have basically stepped on this pitfall: the first time they screened numbers, the data of tens of thousands of American numbers looked “very clean” - the activation rate was high and the activity was good, so they sent a round of private messages with confidence. As a result, after sending it for the second time two weeks later, the open rate dropped by one-third, but user complaints and unsubscriptions increased.

You think there is something wrong with the copywriting, but in fact it is more likely: **The batch of US number data has “expired”. **

Whether you use Telegram for community operations or WhatsApp to attract traffic to independent websites, the status of your numbers is changing all the time - users change numbers, log out, platform recycles, and active windows close, which may cause your data to change from “high value” to “silent numbers” in a short period of time. One-time screening can only freeze the moment. The only way to keep the data fresh is to establish a correct re-screening cycle. **

This article will use executable language to thoroughly explain the re-screening logic and rhythm judgment method of US number data, as well as how to spend the least money on KK-DATA to achieve the most effective data preservation.

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Why does US number data need to be re-screened?

Many teams regard screening as a one-time action: run a task, get the results, and send them directly to the group. This idea has little impact when the amount of data is small and the frequency of contact is low, but once you start pushing the same batch of US data continuously, you will find that the status of the **numbers will actively change over time, and you will not be able to sense these changes without actively re-screening. **

The “life cycle” of a number: the common path from active to invalidated

A US number usually goes through the following stages from being put into use to eventually becoming invalid:

• Registration period → Active period: The new account has just been opened and can be used normally.
• Active Period → Inactive: The user changes the main device and suspends use of the platform.
• Inactive → Logout/Recycle: The operator releases unused numbers to new users.
• Taken over by a new user: The original number was registered by another user, and the active status is bound to the new user’s behavior.

This means: the “active number” you detected today may have changed its owner a week later; but if your private message is still sent to this number, you may receive harassment complaints - or even be judged as a spammer by the platform.

Differences in state stability between different platforms

Not all platforms have the same number status changes. Understanding these underlying differences can help you determine the frequency of rescreening more accurately:

Platform	State stability	Why is it easy to change
Telegram	Medium	The user logout rate is low, but the active window (such as within 30 days) will cause the number to drop from “active” to “inactive”
WhatsApp	High	The number is strongly bound to the mobile phone number, and users change numbers less frequently than social apps, but activity detection is affected by privacy policies
Line	Medium	Japan/Southeast Asia market has high user activity, and users in the US market switch frequently
iMessage	Low	After Apple users change devices/operators, iMessage activation status may become invalid
Zalo	Medium	Mainly for Vietnam, the user base in the United States is small, and number stability is affected by immigration flows

When you have US number data from mixed sources, it is recommended to set the rescreening period separately by platform - Tg and WhatsApp can be longer, iMessage and Zalo can be shorter.

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What signals tell you that “this batch of US data should be re-screened”?

Don’t wait until all data is invalid before reacting. Once the following three signals appear, it means that your US data has entered the “freshness turning point” - rescreening must be performed immediately.

Based on timestamp: detect date and data retention window

Each screening task is timestamped. If you detect a large number of numbers on a certain day, the “freshness window” of this batch of data is calculated from that timestamp. Recommended reference:

• Open Test: Keep fresh for 30–45 days (number cancellation/recycling cycle is longer)
• Activity Detection: Keep fresh for 7–21 days (the active window is usually dynamically adjusted by the platform algorithm)
• Gender/Age Detection: Keep fresh for 30 days+ (personal information changes less frequently, but it does not change forever)

Practical suggestion: When exporting the screening results from the console, keep the “Detection Time” field. When the number of days between detection and today exceeds the corresponding preservation window, rescreening should be considered.

Based on performance indicators: Send open rate, reply rate decreased

If your mass sending tool has the ability to count sending status (delivered, read, replied), then changes in performance indicators are the most intuitive “rescreening signal.”

• Open rate dropped by 20%: This indicates that many numbers may have expired or become “inactive”
• Response rate dropped from 5% to less than 1%: In addition to copywriting issues, data aging is also the main reason
• Sudden increase in complaints/unsubscriptions: The number may have been recycled to new users, and your push is considered harassment by them

When any of the above indicators is abnormal, there is no need to wait until the end of the complete cycle, and re-screening will be performed immediately for the batch number.

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How to determine the rescreening period for U.S. data? 4 scenarios and suggested solutions

There is no “universal re-screening cycle”, your business rhythm determines your re-screening frequency. Below are four typical scenarios and their recommended solutions.

Scenario 1: High-frequency contact → 7–14 days

Suitable for: TG group fans, WhatsApp batch private messages, iMessage group sending

Suggested Strategy:

• Rescreen “Activity” once a week
• Re-screen “activation + gender” once a month
• Check the data deduplication warehouse before submitting the task to avoid repeated deductions for already recorded numbers

Scenario 2: Medium frequency reach → 14–30 days

Suitable for: Quarterly product promotion, event invitations, auxiliary channels used in combination with email/SMS

Suggested Strategy: -Re-screen “activation” 14 days before each shipment

• Re-screen “active” 3 days before sending to filter out temporarily inactive numbers
• If the promotion target has gender targeting, the gender field can be rescreened at the same time in 14 days

Scenario 3: Low-frequency reach → 30–60 days

Suitable for: brand data reconstruction, annual inventory, long-term reserve data cleaning

Suggested Strategy:

• Just rescreen the “activated” status
• Activity and gender fields do not need to be updated unless there are special needs
• Use the deduplication warehouse to merge old and new results to retain a stable number pool

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How to perform the “US data re-screening” operation on KK-DATA?

The complete re-screening process is demonstrated below. Take a batch of US numbers that you tested 15 days ago as an example, and now you need to re-confirm the activity.

Operation steps (all completed in the console)

1. Log in to the console: Visit https://app.kkdata.cc/
2. Select the last detection task: Find the task completed 15 days ago in the task list and view the export file of “Detected Number”
3. Import numbers: On the “Screen Number Task” page, drag or paste this batch of numbers to upload (if there are duplicate numbers, the system will automatically prompt)
4. Select detection type: Only check the fields you want to verify - for example, only select “Telegram active” and do not select “Activation” and “Gender”
5. Enable data deduplication warehouse: Turn on “duplication detection” in the task settings, the system will automatically compare this number with the detected records in the warehouse, and only deduct fees for the “newly added this time” or “requires update” numbers.
6. Submit task: Submit after confirming that the estimated cost is correct
7. Wait for completion and compare the old and new results: After the task is completed, export the results and add the “Rescreening Date” field. Compare them horizontally with the results 15 days ago. You will be able to clearly see which numbers have changed from “active” to “inactive”.

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How to control the cost of re-screening? Pay-per-item billing and deduplication strategy

KK-DATA adopts the per-item billing + no subscription package model. You only pay for the number actually detected × detection type each time. The re-screening scenario is where this model is most cost-effective:

• Only select “variable fields”: activation, activity, gender/age are variable fields and need to be rescreened regularly; fixed fields such as tgid and uid do not need to be tested again.
• Deduplication warehouse to avoid duplication: After the number is uploaded, the deduplication warehouse automatically compares the historical records, and the checked numbers will not be billed repeatedly.
• Execution in batches: If the number pool is very large (more than 100,000), it is recommended to split it into 5,000-10,000 submissions in batches, and filter invalid numbers in segments to reduce unnecessary deductions.

The specific unit price varies depending on the platform and detection type. Please refer to the “estimated cost” prompt in the console before submitting the task; you are also welcome to learn the detailed rules on the Billing Instructions Page.

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FAQ

Question: Under what circumstances will the “active status” of a US number change from “active” to “inactive”?

Answer: There are three main situations: first, the user bound to the number has not logged into the platform for more than a certain number of days (Telegram’s common active window is 30 days, and WhatsApp’s judgment logic is more complex); second, the user manually canceled the platform’s account; third, the number was recycled by the operator and assigned to a new user, but the new user has not yet been activated. The activity detection determines the status of the “current time period” every time, so it is normal for it to be active last time and become inactive the next day.

Question: Will the “Gender/Age” field be re-tested during re-screening? Will the data be updated?

Answer: It will be updated, but please note that this type of detection is not accurate to the identity document level, but is an inference result based on the analysis of account avatars, nicknames, public information and other features. When a user modifies their profile (such as changing their avatar or nickname), the age/gender field may change during re-detection. It is recommended that you consider rescreening the gender and age fields at your own expense only after a period of more than 30 days; repeated testing in a short period of time not only wastes the balance, but also has a low update rate.

Question: Tens of thousands of U.S. numbers were tested at one time. Why are some fields the same when tested again?

A: This is completely normal. Fields such as tgid (Telegram’s unique identification ID), wsid and other fields are fixed attributes and will remain unchanged for a long time after being bound to the number itself. No new results will be obtained if it is detected again. Therefore, when doing re-screening, there is no need to check these fields. The field types to be detected can be selected individually in the task settings, so be sure to check them as needed.

Question: How do the results after re-screening compare with the previous test results?

Answer: It is recommended to keep two columns when exporting each sweat screening task: phone_number (number) and detection_time (detection time). After the re-screening is completed, use Excel or Python to perform VLOOKUP correlation by number, and you can see the status changes of the same number at different points in time. The CSV/TXT file exported by KK-DATA contains the detection time field by default. You only need to combine the two results by number to generate a comparison matrix.

Question: How much balance is required to re-screen 100,000 US numbers in batches?

Answer: It depends on the detection type you choose (activated, active, gender, etc.) and how many unique numbers have been stored in the deduplication warehouse. Since the unit prices of different platforms are different, and the activation and activation fees may also be different, please directly log in to the Control Panel and upload the number to view the “Estimated Fees”. No fees will be deducted before the task is submitted. It is recommended to check the estimated amount before deciding whether to execute it.

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If you have 10,000, 100,000 or more U.S. numbers that need to be kept fresh, establishing a re-screening system is the core means to avoid “data getting worse with use”. Start planning your re-screening cycle today - remember: Select 7-14 days for high-frequency reach, 14-30 days for medium-frequency reach, and 30-60 days for low-frequency reserve.

👉 Log in to the console to start screening numbers If you have any difficulty in selection or operation questions, please contact two-way customer service at any time https://t.me/kkdata_robot View full document: https://docs.kkdata.cc/