What is number filtering? ——From base material selection to data detection, thoroughly understand the entire process of overseas customer acquisition number processing

In the customer acquisition link of overseas marketing and cross-border e-commerce, there is a link that is often underestimated but crucial: number screening.

Many teams spend a lot of time finding number segments, writing copy, and configuring mass messaging tools, but ignore the most basic number quality verification. The result is: a large number of empty accounts are delivered, low activity leads to poor conversions, and even accounts are banned by the platform for reaching invalid users. The disconnect often comes from a vague understanding of the concept of “number screening”.

This article will focus on the definition of number screening, its difference from base material screening and data detection, typical application scenarios, practical steps and common misunderstandings to help you establish a clear data processing thinking framework to avoid invalid delivery and repeated deductions.

What is number screening? ——Basic concepts in acquiring customers overseas

Number screening, understood in a broad sense, is any processing of a collection of numbers, including format cleaning, deduplication, and batch detection of the attributes of the numbers themselves. However, in actual operation, a narrow definition is more commonly used in the industry: Based on the rules of a specific platform, a batch identification process is performed on numbers based on dimensions such as validity, activity, and user attributes (such as gender, age).

The definition and core aspects of number screening

Number screening is not an isolated action, but an intermediate link in the entire “generate/collect → clean → detect → export” pipeline. Its core logic is to “use algorithms or tools to identify numbers in batches”, with the goal of converting the original number list into a target user pool with marketing value.

For example: You have a batch of original mobile phone numbers in the Philippines. Number screening is to determine which numbers have been registered with Telegram (tg activated), which numbers have been active in the last 30 days (tg active), and whether the gender and approximate age group of these accounts (tg gender data) match your promotion goals. This process is essentially different from simply “generating numbers” or “data deduplication”.

The differences and connections between base material screening, number screening, and data detection

In order to understand the relationship between the three more intuitively, we can use a simple metaphor to describe:

• Base screening: It’s like picking out the potatoes that are whole and not obviously rotten from a pile of potatoes. Corresponding to the data level, it means format cleaning (removing invalid characters and check number segments), deduplication (removing duplicate numbers), and preliminary judgment whether the number format is correct (such as whether the length and country code comply with the specifications). This stage does not involve any platform attributes.
• Number filter: Just like checking whether the picked potatoes have sprouted and whether they are suitable for cooking. Corresponding to this point, we use professional tools to batch verify whether the number is open, active, and has identifiable gender/age attributes on a specific platform (TG, WhatsApp, Line, Zalo, etc.). This is the key to precision marketing.
• Data Test: Similar to using an instrument to measure the nutritional content of potatoes, accurate quantitative data can be obtained. In the number field, data detection usually occurs on a specific platform, such as verifying the current status of the number in real time through API (such as iMessage blue bubble confirmation, RCS message protocol handshake), or obtaining more granular attribute fields (such as TGID, WSID). This usually requires higher costs and technical barriers.

The three are progressively advanced: A list of numbers must first be screened to select “clean” number segments, and then its marketing value must be determined through number screening (such as TG screening number, WA screening number). Finally, in some sophisticated scenarios, data detection (such as iMessage valid number detection) may be required to finally confirm availability.

Why does the overseas marketing team need number screening?

Directly skipping the number screening and importing the original numbers into the group sending tool, there are three main pain points:

1. Waste exposure and cost of empty accounts: Among a large number of numbers, the proportion of empty accounts or unregistered corresponding platforms may be as high as 30%-50%. Every time you send a message to an empty number, it means a waste of bandwidth, time and API resources.
2. Low activity leads to low conversion and risk of account suspension: Even if the number has been opened for Telegram or WhatsApp, if the user has not logged in for a long time (belonging to a “dead account”), your contact message is likely to be judged as harassment by the system, which may affect the delivery rate at best, or cause your account or business to be restricted by the platform.
3. Marketing mismatch caused by incompatibility with the target user profile: If you promote cross-border e-commerce products for women, but send mass messages to a large number of male TG accounts, the conversion rate will inevitably be bleak. Through the gender/age field in number filter, you can eliminate unmatched users in advance and focus your limited budget on the groups most likely to convert.

Compare ROI before and after screening: For the unscreened number list, assuming that 1 million numbers are reached, the actual effective opening rate is only 30%, the activity rate is only 15%, and only 1,000 users ultimately generate effective conversations. After strict number screening (such as screening based on TG activity and gender data), for the same batch of 1 million numbers, the activation rate may increase to 50%, the activity rate may increase to 40%, and ultimately the number of effective conversation users can reach more than 5,000 people. **Screening is the starting point for accurate customer acquisition. **

What are the typical application scenarios for number screening?

Different overseas businesses have different needs for number screening. Below are four typical scenarios, in each of which filtering based on platform attributes plays a key role.

Scenario 1: Active user screening before batch invitation to Telegram community

When operating a TG community, it is most taboo to send invitation links to a bunch of “dead accounts”. The correct approach is: first perform background screening (cleaning and deduplication) on the TG number segment, then submit the tg active detection task and specify the active window (for example, “active in the past 7 days”). You can also further use Telegram gender data to screen out potential users who match the community profile (such as “female”, “age about 30 years old”), and then conduct targeted invitations, which can not only increase the group enrollment rate, but also ensure the quality of the community.

Scenario 2: Valid number confirmation before WhatsApp marketing campaign

Before sending bulk messages on WhatsApp, first do the WhatsApp Screener (WA activation test). After confirming that the number has been registered in WhatsApp, if you are promoting iOS products (such as high-end gifts, App paid services), you can additionally do iOS/iMessage detection to screen out iOS users who also support blue bubbles. This double cross-validation can greatly improve the accuracy of reaching high-end customers.

Scenario 3: Zalo or Viber precision marketing for the Southeast Asian market

Zalo has a very high penetration rate in Vietnam, and Viber also has a stable user base in Southeast Asia. When doing this type of regional marketing, the quality of number sources on the market varies. Through Zalo filter (activation, active, gender) or Viber filter, you can quickly filter out local users who are active and have matching attributes from a large pool, avoiding the waste of resources caused by blind mass messaging.

Scenario 4: Multi-country, cross-platform comprehensive customer acquisition data cleaning

For agency operations teams or large e-commerce companies, it is often necessary to uniformly process combined number segments that include multiple countries and multiple platforms (such as TG+WA+LINE). At this time, a platform (such as KK-DATA) that can support global number generation (generating base materials based on 240+ country number segments) and supports cross-platform batch screening (such as submitting TG and WA tasks in the same system) can greatly improve team efficiency.

What is the difference between number screening and bottom material screening? Workflow diagram

For complete clarification, we use a text workflow diagram to describe the standard processing link:

1. Get the original number: It may come from global number generation tools, database purchase, crawler collection, etc.
2. Base material screening (format cleaning + deduplication + number segment validity):
   • Remove illegal characters (spaces, brackets, non-numeric characters).
   • Remove exact duplicate numbers (Data Deduplication Warehouse comes into play at this stage).
   • Check that the number length and country code are correct.
   • This stage does not involve any platform status, you just get a list of original number segments in a standardized format and without duplication.
3. Number screening (multi-platform attribute detection):
   • Select the platform (TG, WA, LINE, etc.) and detection type (activated, active, gender) according to your own marketing goals.
   • Submit the task and the system will perform batch judgment.
   • Export results: include fields such as “Activation Status”, “Active Status”, “Gender/Age”, etc.
4. Data detection (real-time verification) (optional step):
   • Conduct more detailed real-time verification, such as iMessage blue bubble confirmation, for high-quality numbers that have been initially screened.
   • This step is usually more expensive, but also more accurate.
5. Export and Utilize: Obtain the final target user database, which can be used to import mass sending tools or perform secondary processing.

Core Difference: “Basic screening” mainly focuses on format and basic deduplication, completely separate from the platform; while “number screening” focuses on platform dimension attributes (activation/active/gender/TGID, etc.), which is the core link that determines marketing value.

How to perform effective number screening? ——Practical steps and platform selection

In actual operation, an effective number screening process should include the following steps:

1. Prepare number segments: Make sure your original number list has been screened (duplication removal, format verification). You can directly use the platform’s “Global Number Generation” function to generate base materials for free, or import your own CSV/Excel.
2. Select platform and detection type: In the screening tool, select the corresponding platform (such as “Telegram screen number”, “WhatsApp screen number”, “Line screen number”, etc.) according to your target channel. Next, select the detection type. The basic ones are Activation Detection (valid registration), and the advanced ones are Activity Detection (activity window can be specified, such as “active in the past 15 days”) and Gender/Age Detection (extracting user portraits).
3. Set task parameters: Enter or upload a list of numbers. Note that most professional tools (such as KK-DATA) allow a maximum of about 1 million numbers to be submitted in a single task. Before submitting, the system will display the estimated cost to help you control your budget.
4. Submit tasks and get notifications: After submission, the system starts batch detection in the background. You can set up task notification (usually through TG bot) to be reminded when it is completed, without having to stay in front of the screen.
5. Export results: After the task is completed, download the filtering results. Generally supports CSV, TXT and other formats. Subject to the fields exported from the console. The most common fields include: number, platform activation status (yes/no), activity (active in recent days), gender, age range, TGID/WSID, etc.
6. Data stratification and utilization: The exported results are stratified and managed according to different dimensions. For example, TG numbers that are “active + female + age 25~35” are put into the core marketing pool; numbers that are “activated but not active” are put into the cold start repurchase pool.

Cross-platform detection suggestion: If you need to detect TG, WA and LINE at the same time, you should split it into three independent tasks. Because the detection logic and resource consumption of each platform are different, separate submissions are beneficial to more clearly calculate costs and track status. At the same time, you’d better make use of the “data deduplication warehouse” function provided by the tool to ensure that the same batch of numbers will not be repeatedly detected between different tasks to avoid wasting balances.

The role of data detection in number screening – what is a “valid” number?

In the context of different platforms, the definition of “valid” is different, which is why number screening needs to be refined.

Multiple dimensions of valid numbers: activated, active, gender, age, TGID/WSID

• TG activation: Whether the number has been registered on Telegram. This is the most basic step in screening.
• TG Active: Whether the account has logged in within the specified time window. Activity directly determines reach.
• TG Gender/Age: User portrait inferred by analyzing platform data. Note, the gender detection accuracy is in the 70%-90% range, and the age field is statistical-level rather than identity-level accurate data (cannot be used as an ID card). Often used to assist crowd orientation.
• WhatsApp activated/active: Similar to TG, confirm whether the number is registered with WA and whether it has been online recently.
• Line activated/valid: Whether the number is registered with LINE and is a valid user (not a fake account).
• iMessage valid: Confirm whether the number is bound to an Apple ID and supports the iMessage blue bubble function, which is often used to reach high-end iOS users.
• TGID/WSID: Export these platform-specific IDs, which can be used for more advanced API calls or customer management.

How do data detection results guide subsequent marketing stratification?

• Active + Gender (Female) + Age (25~35): High-value main user pool, which can be used for e-commerce, beauty, and female community promotion.
• Active + Gender (Male): Suitable for promoting games, tool apps, B2B SaaS and other products.
• Activated but inactive: cold start resource pool. You can try a small amount of wake-up reach before subsequent holidays or promotions.
• iMessage valid number: a high-end customer pool, suitable for pushing high-priced products or paid services, and the reach environment is relatively clean.

Grasping these dimensions can change your investment from “casting net” to “fishing”.

Common misunderstandings and best practices in number screening

Even if you understand the process, there are still some pitfalls that are easy to step on in actual operation. Here are four common misunderstandings and corresponding best practices:

**Misunderstanding 1: Only do activation detection, not activity screening. **

• Consequences: Reaching a large number of “zombie accounts” who have registered but have not logged in all year round, resulting in a low message delivery rate and even being judged as invalid delivery by the platform.
• Best Practice: List Activity Filtering as a required step. For TG community invitations, it is recommended to specify the “active in the past 30 days” window; for WA marketing, select “active in the past 15 days”. If the budget is limited, you can give priority to activity testing, and then optionally do gender testing.

**Misunderstanding 2: Ignoring data deduplication leads to repeated deductions. **

• Consequences: The same number is submitted repeatedly in different batches of tasks. The test balance will be deducted for each submission, resulting in a waste of funds.
• Best Practice: Use the built-in data deduplication warehouse function of professional tools (such as KK-DATA). Before submitting the task, the system will automatically identify and eliminate the numbers that have been tested to ensure that each number is only tested once to avoid repeated deductions.

**Misunderstanding 3: Mistaking the gender/age field as ID card-level data. **

• Consequences: High-risk decisions (such as financial risk control, medical push) based on inaccurate fields, leading to user complaints or marketing accidents.
• Best Practice: Treat gender and age fields as statistical-level portraits, used for crowd orientation and trend judgment, rather than precise identification. If you need high confidence, it is recommended to combine cross-validation with multiple platforms (for example: double matching of TG gender + Line gender).

**Misunderstanding 4: No format cleaning is done during the base material screening stage. **

• Consequences: If you carry numbers with spaces, missing plus signs, or wrong country codes for platform detection, most of them will be judged as “invalid”, which not only wastes detection opportunities, but also affects the accuracy.
• Best Practice: When using professional tools (KK-DATA’s number generation/import function), utilize its built-in format verification mechanism. Try to import “clean” number segments to ensure uniform format.

Number Screening Tool Selection Guide: Key Considerations When Choosing a Platform

Faced with various number processing tools on the market, you can evaluate them from the following five dimensions as your own selection framework:

1. Platform Coverage: Which social platform screens does the tool support? In addition to basic TG and WA, does it support platforms commonly used in target markets such as LINE, Zalo, Viber, iMessage, and RCS? If you are targeting Southeast Asia, the support of Zalo and LINE is a must.
2. Richness of detection types: In addition to basic activation detection, does it support activity detection (window can be specified), gender/age identification, and TGID/WSID export? The richer the detection models, the more refined the marketing stratification.
3. Task size limit: What is the maximum number that can be submitted at a time? To filter 1 million pieces of data in batches, should it be submitted in multiple batches or completed at once? A high upper limit can save a lot of time in repeated operations.
4. Billing Mode:
   • Bill by item: Pay as you go, no subscription fees. Suitable for teams with large fluctuations in data volume and flexible budgets.
   • Package System: Fixed monthly/annual fee, suitable for teams with stable data volume.
   • It is recommended to choose a platform that supports USDT anonymous recharge and the balance can be held for a long time (to avoid the waste of “secondary card” expiration).
5. Data Security and Privacy: Does the platform have clear instructions for data processing and storage? What to do with your private number list? Is there an anti-fraud inquiry mechanism (such as verifying the identity of official customer service)? This is particularly important in the context of increasing compliance requirements for overseas customer acquisition.

FAQ

Question: What is the difference between number screening and general number generation?

Answer: Number generation is to randomly or “create” an unverified number list based on number segments (such as KK-DATA’s global number generation function). This process is usually free. Number screening is to conduct attribute detection (such as activation, activity, gender) on the existing number list. This process is deducted on a per-item basis. To put it simply: generation is “making raw materials” and screening is “quality inspection”.

Question: Is it enough to use Excel to remove duplicates when selecting base materials? Do you still need tools?

Answer: Excel deduplication can handle small-scale data (less than 50,000 items), but it cannot perform platform-level detection (such as verifying whether the number is activated on TG). More importantly, the data deduplication warehouse of professional tools (such as KK-DATA) can deduplicate across tasks, preventing you from repeatedly detecting the same number in different batches, thereby saving duplicate deductions. In addition, the tool can automatically verify the number format, which is much more efficient than Excel when processing large-scale data.

Question: Can the results after number screening be used directly for advertising?

Answer: Yes, but it is recommended to do a second verification first. For example, the “active + male” account obtained through the TG screen number can be directly used in a ready-made crowd package that matches the profile; but for iMessage blue accounts, you also need to confirm whether the device supports the iMessage protocol (the screen number alone cannot be 100% determined, and it needs to be combined with device fingerprints, etc.). Different platforms have different suitability for delivering data. It is recommended to consult the policies of the corresponding advertising platform.

Question: How many numbers can be filtered at one time? How are fees calculated?

Answer: The maximum number of single tasks is about 1 million. Fees** are charged based on the number of tests**, and the unit prices are different for different platforms (Telegram, WhatsApp, Line, etc.) and different test types (activated, active, gender). The real-time price on the console shall prevail. The system will display the estimated cost before submitting the task, and it will be deducted from the balance after the task is completed. If the balance cannot be used up, the platform usually supports long-term holding without time limit.

Q: How accurate is the “Gender” field in the filter results?

Answer: Gender detection is based on platform public data or user behavior inference, and is not an official identity verification. Usually the accuracy is in the 70%-90% range, depending on the number source and quality. The age field is for statistical reference only and cannot be used in high-risk scenarios such as finance and medical care**. It is recommended to use it as an auxiliary orientation tool and combine multiple indicators (such as avatar tags, nickname analysis) to improve the confidence of the determination.

Conclusion

Number screening is not a one-time “magic operation”, but a basic data processing skill that runs through the entire process of overseas customer acquisition. Starting from base material cleaning, to activation, activity, and gender detection based on platform attributes, to the final data stratification and utilization, every step is directly related to your customer acquisition ROI.

If you have not tried using professional tools to improve screening efficiency, you can first go to the platform to experience the global number generation function (free), and then submit a small batch (such as 100~500) detection tasks to experience the entire process of “number generation → submit screening task → export results”.

👉Log in to the console to start screening numbers

Or, if you have any questions about screening strategies or tool usage, you can directly contact the official support team, who can provide two-way communication support:

• Two-way contact customer service: https://t.me/kkdata_robot
• Customer Service TG: https://t.me/kkdata_cc
• Official channel: https://t.me/kkdata_channel
• Official website: https://kkdata.cc/
• Usage documentation: https://docs.kkdata.cc/