tg Filter Quality Assessment Practical Guide: Four Dimensions to Judge Whether Your Telegram Number Filter Is Worth Advertising

For overseas marketing and community operation teams, tg filtering (Telegram number filtering) is the first step in the customer acquisition process, and is also a key link that determines the efficiency of subsequent launches. However, many teams directly started bombarding with private messages after receiving the filtering results, only to find that the follower response rate was low and the account ban rate was high. In the end, the problem was attributed to “poor data quality.” Is the problem really with the data source, or have you never seriously assessed the quality of the filtering itself?

This article starts from four quantitative dimensions and teaches you step by step to build an independent TG screen number quality assessment system to avoid blind recharge and inefficient delivery, so that your telegram number filtering can truly serve your customer acquisition goals.

Why do you need to evaluate the quality of tg filtering yourself?

tg filtering is not as simple as “throwing the number in and coming out with a whitelist”. A high-quality telegram number filtering can help you save weeks of manual verification time and significantly reduce invalid contact rates; while a low-quality filtering may cause you to waste more than 50% of your budget on empty accounts or inactive users.

How much time can a “high quality” filter save?

Suppose you have 1 million potential numbers and you need to find the users who have opened Telegram and been active in the last 30 days.

• Manual verification: It takes weeks to check one by one, and fields such as gender, tgid, etc. cannot be obtained.
• System batch filter number: A tg filtering task is usually completed within a few minutes to a few hours. The list of active users is directly exported, with fields such as gender, age, tgid, etc. The subsequent delivery can be accurate to “male users who have been online within 30 days.”

There is a huge difference in efficiency between the two. But the premise is that your filtering results must be reliable.

What is tg filtering? The complete process from number detection to whitelisting

Explained in non-technical language: tg filtering refers to submitting a batch of mobile phone numbers to the screening platform. The platform detects the status of each number through Telegram’s protocol features or public interfaces, and returns a series of field tags. The typical process is as follows:

1. Import Numbers: Prepare to-be-checked list via CSV/File Upload or Global Number Generator.
2. Activation detection: Determine whether each number has been registered with Telegram.
3. Activity Detection: Determine whether the number has been online recently (such as within 7 days, 30 days).
4. Additional field detection: Get gender, age, avatar, tgid, etc.
5. Export whitelist: Only keep numbers that meet the conditions for subsequent tg followers or private messages.

Platforms represented by KK-DATA integrate this process into a “generate → filter → export” pipeline. You can first use the 240+ country number generation function to generate target number segments in batches, then submit the tg filtering task, and finally export a CSV file containing gender and tgid, and the entire process is completed on the console.

Four core dimensions for evaluating tg filtering quality

The following four dimensions are the yardstick for you to judge whether a TG Screen Number result is “available”, and each one is indispensable.

Dimension 1: Efficiency - How many numbers have actually opened Telegram?

Opening is valid (Register detection) is the most basic indicator. It tells you which of a batch of numbers have been registered with Telegram and which are empty or unregistered.

• Why is it important? Unregistered numbers cannot be reached at all, and messages sent will be directly discarded by the system or become invalid requests.
• How to judge? Check whether there is an “open/valid” field in the filter results and calculate the ratio. Suppose you submit 100,000 numbers, and only 30,000 numbers show “activated”, with an efficiency of 30%, then the actual use value of this data will be greatly reduced.

Note: The effectiveness is greatly affected by the data source. If you are using a randomly generated number, the effectiveness may be less than 10%; if you are using a number filtered based on a number segment pool, the effectiveness can usually reach 30%-60% (different countries and number segments vary significantly). The specific threshold needs to be set based on your own industry experience.

Dimension 2: Field integrity - can you get gender, age, tgid?

Just having an “activated” field is not enough. For targeted delivery (such as TG fans, private message promotion), what you need is a rich set of additional fields:

• Gender (male/female)
• Age (such as around 30 years old, 25-35 years old)
• tgid (Telegram user’s unique ID, can be used for API calls or group batch addition)
• Avatar/Language etc. (auxiliary recognition)

The higher the field integrity, the more accurate subsequent delivery will be. Take gender detection as an example. If the fill rate of the gender field in a batch of filtered results is less than 40%, then you cannot rely on it for male/female targeting and can only roughly screen based on activity.

KK-DATA’s tg filter supports exporting fields such as gender, age, tgid, etc. When submitting a task, you can check the fields that need to be checked, and the console will display the estimated cost (see the real-time price on the console for details). Be sure to confirm whether the fields you need are covered before the task.

Dimension 3: Repetition rate—Did you screen 500,000 people or the same group of people?

Duplicate numbers in data sources are the culprit of hidden waste. If you import the same number range multiple times or collect duplicate numbers from different channels, a large number of duplicate records will inevitably appear in the filtering results. At this point you think you have screened 500,000 unique users, but in fact there may only be 300,000.

The calculation formula of repetition rate: 重复记录数 / 总记录数 × 100%. It is recommended to control it within 5%. If it exceeds 10%, it means there are serious duplications in the source number list or the same batch number segment has been scanned multiple times, and it is necessary to globally remove duplicates before filtering.

Dimension 4: Activity – Are the filtered numbers really “online”?

This is the most often overlooked dimension. Many users only look at the activation rate and ignore the active status. As a result, after the private message is sent, the other party only comes online a week later, resulting in the message being drowned or officially labeled as harassment.

Activity detection usually returns the “last online time” or “activity window” (such as 7 days, 30 days). When you submit a task, you specify the window length, and the platform will detect whether the number has online behavior or records of sending and receiving messages during this period.

How to verify whether the “activity” of a tg filter is real?

The principle of activity detection is to analyze the last online timestamp of a number. The fields returned by different platforms may be different. You need to pay attention to two key points:

1. Window definition: Does it support custom active windows (such as 7 days, 30 days, 90 days)? The smaller the window, the more immediate the numbers filtered out, but the smaller the number.
2. Field validity: The result must have a specific “last online time” or “active flag”, not just “unknown” or null value.

CHECKLIST:

• Try screening 5,000 entries and check whether all active fields in the results have values (be wary if the proportion of null values exceeds 20%).
• Compare the number of results under different windows: For example, the 7-day window gives 5,000 active users, and the 30-day window gives 12,000 results, indicating that most users come online occasionally and are not active frequently. Choose the appropriate window based on your reach strategy.

Common pitfalls when judging the quality of a tg filter

• Only look at the effectiveness and not the activity: 80% efficiency looks great, but maybe 60% of the users have not been online for half a year, and your private message will be lost if it is sent out.
• Superstitious belief in gender field but lack of age dimension: The gender field fill rate is high, but the age field is almost all “unknown”, so you cannot do precise targeting like “30-year-old male”. Age and gender combinations provide the greatest value.
• Ignore missing values ​​after data completion: Even if the field definition exists, some numbers may not be able to get the field value due to privacy settings or insufficient activity. It is recommended to count the missing rate of each field. If more than 60% is missing, it is not recommended for targeted delivery.

Four-step checklist: Evaluate whether tg filtering is “available”

Each time a batch of tg filtering results are obtained, the following 4-step check is performed:

1. Check the activation efficiency: Does it meet the industry acceptable threshold? Generally, B2B customer acquisition efficiency is recommended to be ≥30% (depending on the number segment and country); B2C can be relaxed to 20%.
2. Check Activity Field: Check if there is a valid value (such as “Activity within 7-day window”). If all are empty or “unknown”, this batch of data cannot be used for instant access.
3. Check the gender/age field fill rate: If you rely on gender/age for targeting, it is recommended that the fill rate be ≥60% before it can be used; if it is less than 40%, it is recommended to abandon the targeting and only deliver based on activity.
4. Check the repetition rate in the exported data: Statistics of the proportion of repeated numbers, ≤5% is considered normal. If it exceeds 10%, it needs to be removed before use.

Only after completing the above checks can you judge whether this batch of tg filtered data is worth investing in the subsequent steps of private messaging, group sending or adding tg followers.

FAQ

**Q: If a number filtered by tg first sends a message and then registers, will it be considered “activated”? ** Answer: Forget it. The activation detection only determines whether the number has registered with Telegram, and has nothing to do with whether it has actively sent messages. Only active detection will determine whether there has been online behavior or sending and receiving messages recently.

**Q: Can the activity detection window be customized? ** Answer: Yes. Some screening platforms (such as KK-DATA) allow specifying active windows (such as the last 7 days, 30 days, and 90 days). The smaller the window, the more immediately active numbers will be filtered out, but the number will be smaller.

**Q: The gender field shows “Unknown”, can this batch of tg filters still be used? ** Answer: Yes. Gender recognition relies on the public information or activity behaviors filled in when registering the number, which is not 100% identifiable. If the missing rate of the gender field is less than 40%, it can still be used by filtering based on activity and region. If the targeting requirements are strong (for example, only males are required), it is recommended to resubmit a new task with gender detection.

**Q: How high is the repetition rate? ** Answer: It is recommended to control it within 5%. If the duplication rate of a single filter exceeds 10%, it means that the source number list has serious duplication or the same batch number segment has been scanned multiple times, and it is necessary to remove duplicates globally before filtering.

**Q: Does tg filtering support batch export of tgid? ** Answer: Supported. Most screening platforms (including KK-DATA) provide tgid field export, which can be used for subsequent API calls, batch adding followers to social groups, or data matching.

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If you want to quickly verify your tg filtering strategy, or need a complete “generate → filter → export” pipeline, you can try the KK-DATA console immediately: 👉 [Log in to the console to start filtering] (https://app.kkdata.cc/). If you have any questions about filtering parameters, recharge or data connection, please contact customer service https://t.me/kkdata_robot in both directions for help at any time. For detailed functional documentation, please refer to Usage Documentation.