Screen number system result quality assessment guide: 5 key indicators to determine whether the data is accurate

In overseas marketing, community operations and B2B SaaS customer acquisition, the screening system has become a core tool for batch verification of number validity, activity and crowd attributes. However, the quality of the results output by different platforms varies greatly - misjudgment of an activated number as “not activated” may lead to a high follower failure rate and low private message reach rate; false activity data may mislead the delivery strategy and waste budget. Therefore, mastering a set of implementable quality assessment methods is crucial to choosing a reliable screen number system.

This article focuses on the five dimensions of accuracy, coverage, activity, gender recognition, and deduplication capabilities, combined with operational test steps to help you determine whether the data from the screening platform is trustworthy.

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What is the result quality of the screening system? Why must overseas teams pay attention?

Result quality refers to the accuracy, completeness and timeliness of the output data from the screening platform. Specifically includes:

• Activation detection accuracy: Correctly identifies whether the number is registered on the corresponding platform.
• Activity coverage: Can you distinguish between “registered but abandoned” and “recently logged in” users.
• Field richness: whether to provide additional information such as gender, age, tgid, uid, etc.
• Duplication removal efficiency: Whether detected numbers can be automatically skipped across tasks to avoid repeated deductions.

Acquiring customers overseas relies on this data for accurate reach. The impact of low-quality data is threefold:

The triple impact of low-quality screening results on overseas customer acquisition

1. Wrong activation of numbers leads to high follower failure rate Thinking that the number has been subscribed to Telegram/WhatsApp, but after actually sending the message, the message “User does not exist” is returned, resulting in a waste of time and risk control of the account (frequent contact with invalid numbers may be restricted by the platform).

2. False activity data misleading delivery strategy If the platform marks a large number of dormant numbers as “active”, the high-conversion words you designed based on highly active users may be completely ineffective, and the production rate will drop significantly.

3. Repeated testing wastes balance Without a deduplication mechanism, the same number will be deducted repeatedly in different tasks. Especially during batch testing or batch import, the balance will be consumed much faster than expected.

Core output dimensions of high-quality screening system

A reliable screen number system should at least output the following fields (subject to console export):

Dimensions	Typical fields	Value to customer acquisition
Activated/valid	Whether to register, number type (such as iOS blue number)	Only send messages to real users to reduce the risk of account ban
Active window	Last login time (7 days/30 days/90 days)	Prioritize reaching highly active users to increase response rate
Gender/Age	Male/female, age range (such as 25-34 years old)	Targeted screening target group, such as men around 30 years old
Unique identifier	tgid, wsid, uid	Used for secondary contact or API integration, more stable than mobile phone number

Note: Gender and age data come from platform public information or algorithm speculation, and are not accurate at the level of real-name authentication.

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How to check the activation detection accuracy of a screening system?

Activation detection is the core function of Siehao. The verification steps are as follows:

1. Prepare known samples: Select 10-20 numbers that you have confirmed have activated Telegram (or WhatsApp, etc.), and 10-20 numbers that are definitely not registered (you can purchase or generate non-existent number segments by yourself).
2. Submit Test: Submit this batch of numbers to the target platform and record the test results.
3. Calculate deviation rate: Compare the actual status with the platform output. Deviation rate = (number of false positives / total number of tests) × 100%. If the deviation rate exceeds 5%, careful evaluation is recommended.

Example scenario: First use KK-DATA’s free number generation function (supports 240+ countries/regions) to randomly generate 100 numbers, then use a small amount of balance to run a Telegram activation test; then use a known activated number to run again. Compare the results of the two groups and observe the hit rate.

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How important is activity detection? How does the active window affect the delivery effect?

Activity detection can distinguish between zombie accounts that are “registered but not logged in for a long time” and real users that are “recently active”. Different industries are suitable for different active windows:

• 7 days active: suitable for immediate promotions and emergency notifications (such as event countdown).
• Active for 30 days: Suitable for regular community operations and private messaging (acceptable by most users).
• 90 days active: suitable for cold start reserve, with a large reach but a low response rate.

What can activity data be used for?

• Highly active users will be reached first: Allocate sales resources to maximize conversion rate.
• Low active number: used for secondary verification or reserve to avoid frequent interruptions.
• Layered Speech Design: Send instant discounts to users who have recently logged in, and send a “We are back” wake-up message to users who have not logged in for a long time.

Evaluation method: Use the same batch of numbers to run comparisons under different active windows to observe whether the activity rate distribution is reasonable. For example: the activity rate of a batch of numbers in a 7-day window should be significantly lower than that in a 90-day window. If the results of the two windows are almost the same, it means there is a cache or algorithm abnormality in the detection.

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Is the accuracy of gender recognition and age fields trustworthy? How to evaluate?

The gender/age fields returned by Telegram, Line, Zalo and other platforms are derived from user public information or third-party data inferences. Since the user may not fill it in or fill it in casually, the accuracy rate is usually 60%-85% (depending on the platform). Do not use for real-name authentication level targeting.

Verification method: Run a test with your own accounts of known gender (such as 10-20), and compare the consistency rate of the results with the real situation. If the consistency rate is lower than 50%, the field is unreliable in this batch of data. The age field is usually an interval estimate (such as 25-34 years old), which can be used to interpret people “about 30 years old” rather than precise to the ID number.

Recommendation: Combine multiple fields (such as gender + activity + region) for comprehensive judgment instead of relying on a single dimension.

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When screening numbers in batches, how to check the deduplication capability and data export quality?

The deduplication function directly affects the cost, and the export quality determines the subsequent data usage efficiency.

Test method

1. Duplication test: Create two tasks that deliberately contain duplicate numbers (for example, the first batch of 100 contains 50 numbers that are the same as the second batch). After submitting, check whether the system prompts for duplication and whether the deduction is only calculated once. If the full amount is deducted twice, it means that the platform’s deduplication is invalid.
2. Export field check: After exporting CSV or TXT, check whether it contains key fields such as tgid, wsid, uid, etc.; whether the format is standardized (no garbled columns, no garbled characters). You can try uploading it directly to other CRM or automatic email sending tools to verify compatibility.

When creating a new task in the KK-DATA console, the system will clearly prompt the number of duplicate numbers and automatically skip them to avoid repeated deductions.

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Screen number system quality assessment checklist (quick comparison)

Check items	Operation suggestions	Qualification standards
Open detection accuracy	Cross-validation with known samples	Deviation rate ≤ 5%
Active window stability	Comparison of different windows for the same batch of numbers	The activity rate increases significantly as the window becomes larger
Gender/Age Availability	Known Gender Account Test	Consistency Rate ≥ 60%
Duplicate removal effect	Duplicate numbers are submitted repeatedly	Only one fee will be deducted
Export format	Check field integrity	Contains core fields such as tgid, uid, etc.
Task estimated cost	Whether to display the cost before submission	Clearly display the current task estimated amount
Customer service response	Contact online customer service	Reply within 30 minutes during working hours

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Selection suggestions: How to balance quality and cost when the budget is limited?

Small amount testing method

• Step 1: Recharge the minimum amount (such as 50 USDT, corresponding to about 50 USD equivalent USDT TRC20).
• Step 2: Run a small task with hundreds to thousands of items (it is recommended to use free generated numbers + a small number of known samples).
• Step 3: Verify the results using the above checklist.
• Step 4: If the quality is satisfactory, increase the budget; if the quality is not ideal, change the platform and the loss will be controllable.

Advantages of pay-as-you-go billing model

KK-DATA adopts balance recharge + per-item deduction, and there is no subscription package. This means:

• You pay for what you use, and you can stop at any time if the quality is not satisfactory.
• The balance supports USDT anonymous recharge, which is suitable for team decentralized operations.
• The estimated cost will be displayed before submitting the task to avoid unexpected deductions.

How to deal with poor screen size results?

If the test results are obviously abnormal (such as the activation rate is 100% and the activity rate has not changed), you should contact the platform customer service immediately. Formal platforms will provide task reports and may refund the balance or compensate after confirmation. Keep the original submission as a basis for comparison.

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FAQ

**Q: Is there any official commitment to the accuracy of the screening system? ** Answer: Generally we do not promise 100% because there are delays and errors in the data source itself. You can use a small batch of known numbers to do cross-validation yourself. Regular platforms (such as KK-DATA) can view the details of the test results of each task on the console, making it convenient for you to sample and review.

**Q: Can Telegram gender recognition really determine whether a user is male or female? ** Answer: Not necessarily. Telegram’s gender field comes from users’ public information or third-party data inference, and some users may not fill it in or fill it in at will. The accuracy is usually higher than random (ranging from about 60% to 85%), but it is not recommended as the only criterion. It is more efficient to reference it together with the age field.

**Q: Why are the activation results different when I run the same batch of numbers twice? ** Answer: User active status changes dynamically, and users may log in or be silent within a time interval. In addition, the output of different active windows (7 days vs 30 days) is naturally different. It is recommended to run it twice under the same active window (with short intervals) to check the stability. If the deviation is too large, it may be an abnormality in platform caching or detection.

**Q: How much cost can be saved by data deduplication warehouse? ** Answer: If you submit tasks containing duplicate numbers multiple times and the platform does not deduplicate, the duplicate parts will be deducted according to normal detection. A good deduplication system can avoid this waste, and the saving ratio depends on your number duplication rate. It is recommended to first observe whether the system prompts the number of duplicate numbers when creating a new task in the KK-DATA console.

**Q: When exporting sieve numbers, what are the uses of the fields tgid and uid? ** Answer: tgid is the unique numeric ID of a Telegram user, which can be used in API scenarios (such as sending custom messages to users); uid is similar in Line/Zalo. If you need batch private messages or secondary contact later, these fields are more stable than mobile phone numbers and are less likely to be logged out and lost.

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Want to personally verify the quality of a screening system using the above checklist? It is recommended to log in to the KK-DATA console directly and use free number generation + small balance to test your data scenario. If you need free consultation or manual-assisted testing, you can get support through two-way contact customer service.

👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot Detailed documentation: https://docs.kkdata.cc/ Official website homepage: https://kkdata.cc/