tg active data quality assessment guide: 3 steps to verify whether your TG list is authentic and valid

Among overseas customers, Telegram has become the core channel for cross-border e-commerce, community operations and promotion teams due to its high open rate and private domain attributes. However, the so-called “TG active data” that many teams spend a lot of money to purchase has greatly reduced the actual effect: the delivery rate of private messages is low, accounts are frequently restricted, and the conversion rate is less than three digits. The root of the problem lies in - data quality. A high-quality TG activity data allows you to accurately reach real users, while low-quality numbers will only waste budget and account security. This article will help you systematically master the entire process of TG active data quality assessment from quality definitions, assessment methods to practical tools.

Why is the quality of tg active data the key to success in acquiring overseas customers?

Low-quality TG active data (containing a large number of zombie accounts, false accounts or duplicate accounts) will bring three risks:

• Cost Waste: The purchase and detection cost of each number may not seem high, but once the proportion of invalid numbers is too high (for example, more than 50%), the actual customer acquisition cost will directly double.
• Account Banning Risk: Telegram will conduct anti-spam monitoring on numbers that are sent to invalid or frequently complained numbers. Mass sending to inactive users can easily trigger account bans.
• Low conversion rate: Even if the number is activated (registered), if the user is inactive for a long time, the message will still be lost. High-quality TG activity data should include recent online behaviors to ensure timely access.

Therefore, evaluating the quality of tg active data is not “icing on the cake” but a required course for the customer acquisition team.

What is high-quality tg active data? ——Definition and core indicators

Measuring the quality of TG active data can be quantified into the following dimensions:

Dimensions	Description	Ideal reference value (not absolute)
Opening rate	Proportion of numbers registered with Telegram	≥ 90%
Activity rate	Proportion of online behavior within the specified time window	According to the scenario, active in 7 days ≥ 60%
Gender/Age Recognition	Proportion of real people’s gender and age fields that can be identified	Recognition rate ≥ 40% and reasonable distribution
Duplication rate	Proportion of invalid numbers after cross-task or internal deduplication	≤ 5%
Where the number belongs	How well the number area matches the target market	Try to be consistent

Open detection vs active detection: both are indispensable

• Activation detection: Only verify whether the number is registered with Telegram, and does not care whether the user is still using it. For example, a large number of numbers in India and Nigeria may be abandoned for a long time after being activated.
• Activity Detection: Detect whether the number has been online, sent messages or joined groups within the set time window (such as 7 days, 30 days). Direct message promotion teams should prioritize using activity detection because only active users can receive messages and reply.

Note: Activating detection is the foundation, and active detection is the value enhancement. It is recommended to perform activation detection to filter out invalid numbers first, and then perform active detection on the passed numbers to avoid wasting costs.

Active window and timeliness: Why “3 days active” is more valuable than “30 days active”

The smaller the active window, the higher the probability that the number is currently reachable. Example:

• Active for 3 days: The number has used Telegram in the past 72 hours. There is a high probability that it is a real user who uses it every day. It is suitable for instant private messaging, group building, and notification promotion.
• 7 days active: Suitable for general brand exposure or event invitations, users will still have a high probability of viewing it.
• 30 days active: suitable for long-term marketing (such as member reminders), but users may have reduced the frequency of use.

When evaluating data, if the seller claims to be “active for 3 days” but the actual 7-day activity rate is less than 20%, it means the data has expired or been adulterated. Therefore, understanding the duration of the active window and the corresponding ratio is core to quality assessment.

How to quickly evaluate the quality of existing tg active data in 3 steps?

The following three steps are applicable to the sampling self-inspection of any TG number list. There is no need to purchase tools in advance, but it can be accelerated by combining the number screening function of KK-DATA.

Step 1: Randomly sample and conduct TG activation test

• Sampling ratio: If the total amount is less than 5,000 items, 10% (at least 500 items) will be selected; if the total amount is more than 50,000 items, 1% (at least 500 items) will be selected; for large-scale lists (million level), 0.5% can be selected.
• Operation: Submit the sampling number for activation testing. Most screening platforms (such as KK-DATA) support uploading CSV/TXT for direct detection.
• Judgment Criteria: An activation rate of ≥90% is considered good, and if it is below 70%, you need to be wary of data sources. If a large number of numbers show “Not Registered”, it indicates that the data may be a randomly generated number group.

Step 2: Filter hierarchically by active window and check the activity rate

For the numbers that pass the activation test, the activity rates under different active windows are then detected:

• Submit 7-day activity detection and record the number of active numbers.
• Submit another 30-day and 90-day inspection (can be done independently).
• If the seller promises “7-day active” but the actual 7-day activity rate is less than 50%, it means the data quality is questionable.
• Recommended approach: First use the 7-day active window to make the main judgment, and then combine it with the 30-day window to understand the long-term activity.

Step 3: Use the gender/age field to verify the “real person attributes”

The TG gender detection of many screening platforms (including KK-DATA) will return fields such as gender, age, and avatar. Although the age data comes from algorithm inference (non-ID card level accuracy), it can be used for distribution rationality testing:

• If the ages of all numbers are concentrated in “20-22 years old” or even “0-10 years old”, or the gender recognition rate is extremely low, then these numbers are likely to be fake accounts generated by machines.
• Normal real-person user group: age distribution is between 18-45 years old, and there is no obvious extreme in the male/female gender ratio.

Note: The age field in the gender detection result of KK-DATA can be used to assist in judgment, but it should not be used as identity verification. After exporting the fields, you can count the proportions of each age group. If it is too concentrated (for example, more than 90% are 30 years old), you need to be cautious.

Common tg active data quality traps and troubleshooting methods

Trap 1: All numbers passed the activation test but the activity rate is extremely low

Reason: The seller used “fake active” accounts registered with batches of mobile phone numbers. Although these numbers can pass the activation test, they have never been used for real.

Solution: Must rely on active detection, not just enabled detection. It is recommended to add 7-day active detection to all purchase lists, so that even if the unit price is higher, zombie accounts can be effectively filtered.

Trap 2: High repetition number rate leads to actual effective amount puffiness

Cause: Duplication was not removed between multiple purchases or internal tasks, resulting in seemingly 100,000 numbers, but in fact the duplication rate was as high as 30%.

Solution: Use a data deduplication warehouse (such as KK-DATA to provide cross-task deduplication function) to automatically compare historical records when importing a new list to avoid repeated detection of the same number, save balances and increase the effective amount.

Practical process of using KK-DATA to improve the quality of tg active data

KK-DATA is a multi-platform screening platform that supports Telegram, WhatsApp, Line, etc. The following are typical steps taking tg active detection as an example:

1. Log in to the console → Select the “Telegram Screen ID” module.
2. Upload or generate numbers: You can upload your own CSV/TXT, or use the global number generator to generate a random number segment for the target country (free).
3. Select detection type: Check “tg active detection” and set the active window (such as 7 days). If you also need a gender/age field, you can check “tg gender detection” at the same time.
4. Submit the task: The estimated cost will be displayed before submitting the task to ensure that the balance is sufficient. It is recommended to test in small batches of 100-500 items first.
5. Waiting for completion: After the task is completed, you can be notified through Telegram, and the results support CSV/TXT export.
6. Remove Duplicate Warehouse: If there are multiple batches of tasks, check “Remove Duplicate Warehouse” to avoid wasting balance due to repeated testing.

All tests are billed per item, no subscription package, pay as you use. For specific unit prices, please see the real-time price on the console.

Key considerations when assessing tg active data quality

• Do not submit too many numbers at once: It is recommended to process in batches, especially when using a new list for the first time. Batching avoids rapid balance depletion due to a large number of invalid numbers.
• Check task notifications in a timely manner: KK-DATA supports pushing the results through Telegram after the task is completed to ensure that the data is obtained as soon as possible.
• Verify official customer service: Beware of scams pretending to be KK-DATA customer service.

FAQ

Question: What is the difference between TG active data and ordinary TG activation data?

Answer: Activation data only means that the number has been registered with Telegram, but does not mean that it is still in use; activity data can detect whether the number has remained online or sent messages within a specified time window (such as 7 days). When doing private message promotion, active data is closer to “real users” than activation data.

Question: How can I verify whether the TG active list I purchased is authentic?

Answer: The “three-step verification method” can be used: ① Do sample activation testing to confirm the minimum activation rate (recommended ≥80%); ② Layered detection based on active windows and compare the claimed active windows; ③ Export the gender/age field to check whether the distribution is reasonable. Using KK-DATA, the above detection can be completed at one time.

Question: What is the reasonable setting for the active window of tg active data?

Answer: It is recommended to choose based on the marketing goals: 3 or 7 days for instant private messaging/group building; 30 days for brand exposure; 90 days for long-term maintenance. When assessing the quality of the list, 7-day and 30-day testing can be done respectively. If the 7-day activity rate is less than 50%, the data quality is worrying.

Question: How to prevent tg active data from being repeatedly detected and wasting balance?

Answer: Use the data deduplication warehouse function. KK-DATA supports cross-task deduplication. When importing a new list, the system automatically compares historical records and eliminates numbers that have been detected to avoid repeated deductions.

Q: How accurate is the gender/age field? Can it be used for precise crowd targeting?

Answer: The gender/age field is the result of algorithm analysis. It is not ID card-level accuracy, but can be used to judge macro-population distribution. It is not recommended to use it as the only basis for precise targeting. It should be used in combination with activity and number ownership. KK-DATA’s tg gender detection includes an age field, which can be used as an auxiliary filtering condition.

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Want to systematically improve the quality of your TG active data? Start shortlisting today with scientific evaluation methods. 👉 Log in to the console to start screening numbers, or contact customer service through two-way https://t.me/kkdata_robot to get one-on-one guidance. For more function introduction, please refer to KK-DATA Document.