Detailed Explanation of Number Screening Data Quality Metrics: Evaluation Methods for Validity Rate, Activity Rate, and Multi-Platform Number Screening

In the chain of overseas customer acquisition, number screening data quality is the core variable that determines marketing costs and conversion effectiveness. Whether you are broadcasting via Telegram, sending one-on-one private messages on WhatsApp, or precisely targeting through iMessage, if the imported number pool itself is of mixed quality—containing a large number of invalid numbers, silent users, or incorrectly labeled gender data—then every subsequent outreach is essentially wasting your budget.

Many teams only focus on the unit price of number screening, overlooking the amplifying effect of data quality metrics on the final ROI. A low-priced screening system with only 60% accuracy may lead to more wasted consumption than a higher-priced system with 95% accuracy. This article will systematically define the evaluation methods for number screening data quality from metrics such as validity rate, activity rate, and gender recognition accuracy, and provide best practices for overseas customer acquisition based on high-quality metrics.

Why Number Screening Data Quality is a Key Variable in Overseas Customer Acquisition

From three dimensions, data quality metrics directly affect the success or failure of every marketing action:

1. Cost Waste: Invalid numbers (empty numbers, unregistered, inactive) generate meaningless sending costs. When telecom channel costs are high, this waste can account for more than 30% of the total budget.
2. Conversion Rate Loss: Even if the number is active, if the user has not logged into the corresponding platform for a long time, the message cannot generate interaction after delivery. The conversion rate of active users is usually 5-10 times that of inactive users.
3. Data Contamination: Importing low-quality numbers into CRM or automated marketing systems distorts subsequent analysis reports, leading to operational decision errors, and cleaning contaminated data itself consumes manpower.

The establishment of high-quality number screening data quality metrics essentially builds a quantifiable bridge between “number activation status” and “user actual reachability.”

Five Major Data Quality Metrics That Number Screening Systems Must Pay Attention To

The following five metrics form the basic framework for evaluating the data quality of any number screening system. You can use them as a checklist for selection or self-inspection:

Metric	Definition	Typical Usage
Validity Rate	Proportion of numbers registered/activated on the corresponding platform	Judge the basic usability of the number pool
Activity Rate	Proportion of numbers that have had interactive behavior within a specified time window	Screen high-engagement users
Gender Recognition Accuracy	Degree of consistency between the gender label marked by the system and the actual user gender	Precision of targeted marketing
Number Format Compliance Rate	Proportion of numbers that include country codes, remove abnormal characters, and comply with E.164 standards	Quality baseline for data preprocessing
Deduplication Rate	The system’s ability to successfully identify and remove duplicate numbers	Reduce duplicate detection costs and user experience damage

Validity Rate – Whether the Number is Actually Activated/Registered

Validity rate is the most basic data quality metric. It directly answers “Does this number exist on this platform?” However, the definition of “activation” varies slightly between different platforms:

• Telegram: Registration detection is based on the user existence status returned by the Telegram server; some unavailable numbers will return a “user does not exist” flag after the request.
• WhatsApp: Activation detection relies on the WhatsApp Business API or public interfaces. The detection result is affected by the number’s activity level and device status.
• iMessage / RCS: Detects whether Apple or Android messaging services are available for that number, usually related to carrier support.

A reliable screening system should output a validity rate above 95% (based on a real number pool). If the validity rate in a sample test is lower than 80%, it is recommended to change the screening source or adjust detection parameters.

Activity Rate – Proportion of Recently Active Users

Activity rate goes one step further than validity rate. It measures “Has the user corresponding to this number been using this platform recently?” For instant messaging tools like Telegram or WhatsApp, users may register but not log in for a long time, or the account may have been abandoned.

Activity rate is usually accompanied by an activity window parameter. Common ones include:

• 7-day activity: High-frequency users who have interacted (sent messages, opened the app) in the past week
• 15-day activity: Regular users who have used the platform in the past two weeks
• 30-day activity: Ordinary active users who have activity records within a month

If your marketing scenario is event notifications or limited-time offers, prioritize 7-day or 15-day active numbers. For long-term brand maintenance, 30-day activity is sufficient. The higher the activity rate, the better the quality of the number pool, but the unit price also rises accordingly. You need to balance based on budget and conversion goals.

Gender Recognition Accuracy and Multi-Platform Consistency

Gender recognition is usually based on AI judgment of user avatars (profile photos) and nickname semantics. It is not 100% accurate because avatars may not show a person, or users may use gender-neutral nicknames. Therefore, gender recognition accuracy is a metric that requires careful handling.

Evaluation method: Randomly select 100-200 numbers marked as “male” or “female,” manually check their avatars and nicknames, and calculate the system’s judgment accuracy. For overseas marketing, it is generally acceptable if the gender label error is within 10%-15%. However, if you are targeting female-oriented products (e.g., beauty, maternity), it is recommended to perform a secondary verification of gender labels.

Additionally, cross-platform gender consistency is also worth noting: Are the gender labels for the same number consistent on Telegram and WhatsApp? If not, it indicates that at least one platform’s recognition is biased, and actual verification should be used as the standard.

How to Quantitatively Evaluate the Data Quality of a Number Screening System?

The following three methods can help you establish your own data quality measurement process:

Sampling Manual Verification – Confirm Detection Results with Real Devices

The simplest but most effective method: Randomly select 50-200 numbers from the screening results. Use real devices (phone or desktop) to search for the corresponding platform’s user one by one, and manually confirm whether the number is activated and whether the avatar matches the system’s label.

Steps:

1. Export the sampled numbers from the screening results (preferably distinguishing between “valid,” “active,” and “gender recognition” labels).
2. Use another phone or emulator to simulate normal user behavior, search for and verify the number status.
3. Calculate the deviation ratio between the system’s judgment and manual verification.
4. If the deviation exceeds 10%, the quality of this screening system needs to be re-evaluated.

Cross-Task Consistency Test – Same Number Pool Screened Multiple Times

Submit the same batch of numbers to the screening system and perform two detections at different time intervals (at least 24 hours apart), then compare the differences in output results:

• Validity rate fluctuation: Under normal circumstances, the validity rate fluctuation should be within ±3%. If there is a large fluctuation (e.g., 90% the first time, 70% the second), it may be due to the system’s caching strategy or unstable detector.
• Activity rate fluctuation: Activity rate will naturally decrease over time, but the decrease should be within a reasonable range. If the activity rate drops sharply from 80% to 40% within 24 hours, the system may have misjudgments.
• Deduplication performance: Observe the deduplication warehouse’s handling of duplicate numbers: If the same number is submitted twice, is the fee charged only once, and is the result consistent?

High consistency is an important sign of stable number screening data quality. It is recommended to develop the habit of performing cross-tests every quarter.

Common Data Quality Pitfalls in Number Screening and Countermeasures

In actual operations, the following three pitfalls are most likely to cause distortion of data quality metrics:

1. Invalid numbers misjudged as active: Some low-priced screening platforms use old data or simplify the detection process to reduce costs, causing invalid numbers to be marked as “valid,” resulting in a large number of failed message deliveries during bulk sending.

   • Countermeasure: First test with a small sample, comparing the system’s output with the actual delivery status (delivery rate and open rate).

2. Misunderstanding of activity windows: Different platforms have different definitions of “active.” For example, some systems use “last sent message” as the judgment basis, while others use “last opened the app.” When comparing across platforms, you need to confirm the specific calculation logic of the activity window.

   • Countermeasure: Use the same activity window (e.g., uniformly set to 30 days) to compare activity rates across different platforms to avoid making wrong judgments due to different definitions.

3. Gender recognition bias: Gender judgment based on avatars is almost ineffective for non-human avatars (scenery, animals, text) and cannot identify transgender or gender-neutral users.

   • Countermeasure: For gender labels, it is recommended to use only three categories: “male/female/unknown,” and set a threshold for the proportion of “unknown” (e.g., if it exceeds 20%, the batch’s gender data is considered unreliable).

KK-DATA’s Design Considerations on Data Quality Metrics

As a screening platform for overseas customer acquisition, KK-DATA provides the following features to ensure data quality metrics:

• Telegram registration detection: Multi-node verification ensures the accuracy of validity rate output. Users can view the validity rate statistics after each batch detection in the console, allowing real-time monitoring of number quality changes.
• Customizable activity window: Supports different activity windows such as 7/15/30 days, making it easy to adjust the screening threshold according to different marketing scenarios. More importantly, activity data is based on recent interaction behavior, not static labels, providing stronger timeliness.
• Gender recognition source: Based on avatar recognition, the platform provides confidence information when outputting gender labels (for specific implementation details, please refer to the usage documentation). Additionally, users can compare gender label consistency across platforms (Telegram + WhatsApp).
• Data deduplication warehouse: Supports global deduplication across tasks. Before submitting numbers, the system automatically detects whether there are historical duplicate numbers to avoid duplicate detection charges. This directly improves the deduplication rate and reduces data redundancy.

Best Practices for Number Screening Based on High-Quality Data Metrics

From the number source to export and use, the following five steps can help you establish a closed loop of quality numbers from generation to screening:

1. When choosing a number source, prioritize randomly generated number pools or real number pools purchased from compliant channels. Do not use publicly available number lists from unknown sources on the internet, as their data quality is usually extremely low.
2. Set dual screening thresholds: First use the validity rate to filter out invalid numbers (e.g., directly discard batches with a validity rate below 80%). Then set an activity rate threshold for valid numbers (e.g., only keep numbers with a 7-day activity rate > 60%). This two-stage mechanism can improve the proportion of high-quality numbers while controlling costs.
3. Submit tasks in batches: Do not submit millions of numbers at once. First test with 5,000-10,000 numbers to ensure that the validity rate and activity rate meet expectations, then gradually expand the task scale.
4. Perform secondary verification after export: After exporting the screening results, randomly select 50-100 numbers and manually verify them with a real device, forming a cycle of “system output → manual verification → feedback to adjust thresholds.”
5. Use the data deduplication warehouse to maintain the main number pool: Store high-quality numbers generated each time in the deduplication warehouse. When needed later, directly screen based on the warehouse to avoid repeated generation costs.

Frequently Asked Questions

Q: What is the difference between “validity rate” and “activity rate” in number screening data quality?

A: The validity rate indicates whether the number has been registered (activated) on the corresponding platform; it is a judgment of number existence. The activity rate measures whether the user has had interactive behavior within a certain time window (e.g., 7/15/30 days); it is a judgment of user usage frequency. Combining the two allows you to screen high-quality numbers that are both actually existing and recently reachable. For example, a number may be activated on Telegram, but if the user hasn’t logged in for 60 days, it is “valid but inactive.”

Q: How can I judge whether the “gender recognition” data provided by a screening platform is accurate?

A: It is recommended to first take a small sample (100-200 pieces) and manually compare the gender labels marked by the system with the actual avatars and nicknames, then calculate the accuracy rate. Also, pay attention to whether the platform supports filtering by gender and whether it provides recognition confidence indicators. If the sampling accuracy is below 80%, the platform’s gender data is not suitable for precise targeting.

Q: Why does the validity rate result for the same number fluctuate across different screening batches?

A: Possible reasons include differences in detection time windows (e.g., temporary platform failures), changes in the number’s own status (e.g., user deactivation), or the screening system’s caching strategy. To reduce fluctuations, it is recommended to use stable detection with “validity period verification” and use a deduplication warehouse to avoid duplicate detection costs. If the same number changes frequently across batches, the stability of the screening system needs attention.

Q: What does “deduplication rate” mean in number screening data quality and why is it important?

A: The deduplication rate refers to the screening system’s ability to effectively identify and exclude duplicate numbers within the same task. A high deduplication rate avoids duplicate detection charges, reduces data redundancy, and prevents the same user from being contacted multiple times, affecting their experience. It is recommended to choose a platform that supports global deduplication across tasks, such as KK-DATA’s deduplication warehouse, which provides this capability to reduce ineffective spending.

Q: When using a number screening system, should I prioritize validity rate or activity rate?

A: It depends on the marketing scenario. If you are doing bulk broadcasting (e.g., Telegram channel promotion), activity rate is more important because messages are only meaningful if seen by active users. If you are doing precise one-on-one private messaging, validity rate is the foundation, and activity rate determines the reach effect. Generally, it is recommended to use two-stage filtering: first use validity rate to filter invalid numbers, then set an activity rate threshold for valid numbers to balance cost and conversion.

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The above is a comprehensive analysis of number screening data quality metrics. If you want to experience the performance of these metrics on real data, you are welcome to log in to the KK-DATA console for free number generation and small-scale screening tests, and view the visual presentation of statistics such as validity rate and activity rate in real time. You can also refer to the official documentation for detailed detection type descriptions. If you have any questions, please feel free to contact customer service @kkdata_cc.