Telegram screening process case: complete practice from data generation to effective number screening

If you are engaged in overseas community operations, cross-border e-commerce promotion, or Telegram group private message marketing, you must have encountered this problem: you have a bunch of mobile phone numbers in your hand, and you don’t know which ones are registered on Telegram, which ones are online recently, and which ones are targeted gender users. Manually detect each one? That’s nearly impossible to accomplish. Telegram Number Screening is to solve this problem - quickly filter out high-value effective clues by batch testing the number’s activation status, activity, gender and other attributes. Today, we use a complete process case to take you through the entire process from number preparation to result export to see how much efficiency a systematic telegram number solution can bring.

What is Telegram screen number? ——Use cases to explain core concepts

Don’t rush to memorize the definition, let’s look at a real scenario: a cross-border team (anonymous, but the needs are very typical) need to promote an e-commerce tool to Telegram users in Vietnam and Indonesia. They collected about 200,000 mobile phone numbers from exhibitions, online forms and other channels, but they don’t know how many of them are registered on Telegram, let alone which ones are active and which are male. Their goal is simple: screen out users who have been online in the past 7 days, are male, and are about 30 years old, and then send precise private messages.

The key action here is telegram screening - batch verification of whether the number has opened Telegram (registration detection), whether it is active within the specified window (activity detection), and gender/age inference (gender detection). The combination of these three dimensions can turn the raw number pool into an actionable marketing list.

Case background: A team’s transformation from manual screening to systematic screening

Before adopting the batch screening platform, this team’s approach was to hire people to use virtual numbers to search Telegram one by one and record the status. Up to a few hundred numbers are processed every day, labor costs are high, and data is prone to errors. To make matters worse, many numbers are checked twice, wasting time and money. They urgently need an automated solution: one that can handle hundreds of thousands of numbers at once, output tables with fields such as activation, activity, gender, etc., and supports pay-as-you-go, without wasting money.

That’s why they turn to platforms like KK-DATA. Below we will dismantle the complete process of telegram sieve number according to their four steps in actual operation.

The first step of the process: Number preparation - how to obtain the number pool to be screened

The first step in any number screening task is to prepare the numbers to be tested. Sources generally fall into two categories:

Own number cleaning and deduplication

The team organizes the existing 200,000 numbers into CSV or TXT files. Before importing into the console, they first used the platform’s built-in data deduplication warehouse to compare this batch of numbers with numbers in historical tasks and eliminate duplicates. This step is crucial: repeated detection of the same number will waste the balance. After deduplication, not only can you save money, but you can also avoid duplication of work.

Expand the sample using the global number generation module

Targeting the target areas (Vietnam, Indonesia), they found that many of their own numbers were invalid. In order to supplement the sample, the team used KK-DATA’s global number generation function: select the country (Vietnam/Indonesia), specify the number range, and randomly generated 50,000 supplementary numbers. The generation itself is free, only subsequent actual testing will be deducted. These newly generated numbers are combined with the cleaned old numbers to form a total pool of about 250,000, and then enter the number screening process.

Step 2 of the process: Configure Telegram screening task - key parameter selection

When creating a new task in the KK-DATA console, you need to check the detection type and parameters. This step directly affects the cost and quality of the results.

The difference between tg activation detection and tg active detection

• tg activation detection: only verify whether the number has registered a Telegram account. The result returns “Activated” or “Not activated”. This is the most basic screening and the lowest cost.
• tg active detection: You need to specify an active window, such as “online in the past 7 days”, “online in the past 30 days”, and “online in the past 90 days”. Active detection requires one more request on top of the activated detection. The cost is slightly higher, but it can help you filter out those “zombie accounts”.

The team’s approach is to do an activation test first, filter out about 60% of unregistered numbers, keep only “activated” numbers, and then conduct activity + gender detection on these numbers. Although it is performed twice, the total cost is lower because only valid numbers are processed the second time.

Interpretation of gender test results (age field description)

In addition to returning the “male/female” label, gender detection will also come with an age field, such as “about 28 years old” and “about 32 years old”. This age comes from the big data inference model of the platform. It is not an accurate value at the ID card level, but it is completely sufficient for group targeting. For example, if the team wants to find “males around 30 years old”, they can set the age range (for example, 25–35 years old) + gender male in the filter conditions. Note that the age field is named age when exporting results in the console. You can use it directly for grouping or filtering.

Step 3 of the process: Submit tasks and cost estimates - detailed explanation of the item-by-item billing model

After configuring the parameters, the console will automatically display the estimated cost. KK-DATA does not have a subscription package, and adopts the balance recharge + item deduction model: you need to recharge with USDT (TRC20) first, and the minimum recharge is about 50 USDT; the estimated total will be calculated before the task is submitted, and it can only be submitted when the balance is sufficient. After the task is completed, the system deducts the fee corresponding to the actual number of detections from the balance.

How to check real-time prices and estimated fees

On the task creation page, for each inspection type you select, the corresponding unit price and estimated total price will be displayed below. The team has 250,000 numbers this time and chooses “tg activation + tg active (last 7 days) + gender”. The estimated cost is about tens to hundreds of USDT (the specific number is not listed here due to real-time price changes). After confirming, click Submit and the task will enter the queue.

Step 4 of the process: Task execution and result export—from console to marketing action

Task execution time depends on number of numbers and platform load. 250,000 numbers complete in approximately 30–60 minutes (time varies based on concurrency). Upon completion, the team will receive a Telegram notification (requires binding in advance). Log in to the console to view task details, and click “Export” to download the CSV or TXT file.

Exported fields include: -Mobile phone number

• tg activation status (activated/not activated)
• Activity level (such as active in the past 7 days / active in the past 30 days / inactive)
• Gender (male/female/unknown)
• tgid (Telegram user’s unique identifier)
• Others such as age range, country, etc.

This data can be imported directly into a mass messaging tool or CRM for targeted private messages or group invitations. tgid is more stable than a mobile phone number, because the tgid does not change after the user changes his mobile phone number, and subsequent marketing can be tracked for a long time.

Data comparison after process cases: Which indicators are worth paying attention to?

Although we cannot publish specific customer data, we can make a hypothetical comparison based on common situations:

Indicators	Original pool before filter number	Effective pool after filter number	Change direction
Total Numbers	250,000	–	–
tg opening rate	About 40%	100% (filtered)	Improvement
Activity rate in the past 7 days	About 20%	About 50% (in the activation pool)	Improvement
Male proportion	About 50%	Gender filtering can reach 70%+	Accurate
Approximately 30 years old	Approximately 20%	Screening by age can reach 40%+	Accurate

In the end, the team obtained about 30,000 to 40,000 clues from 250,000 numbers that met the conditions of “open + active in the past 7 days + male + 25-35 years old”. When these clues were used in follow-up private messages, the response rate increased by more than 3 times compared to previous blind mass messages. The value of telegram screen number lies in this: not more is better, but more accurate is better.

FAQ

Question: How many numbers can Telegram filter at most at one time?

Answer: KK-DATA supports up to about 1 million number detections in a single task, and is suitable for medium to large-scale batch verification. If your number exceeds 1 million, you can submit it in batches.

Question: How is the “activity” in the filter results defined?

Answer: Activity is determined based on whether the number has been online within the preset time window (such as the past 7 days, the past 30 days). The specific window is optional during task configuration. For example, selecting “Active in the past 7 days” means that only accounts that have logged into Telegram within these 7 days will be retained.

Q: Is gender detection accurate? Can the age field be used to target people over 30 years old?

Answer: Gender and age are based on the platform’s big data inference model. They are not precise values, but can be used for targeted screening at the group level (such as screening for “males around 30 years old”). Not recommended for use in scenarios that require identity-level verification (such as finance, law). In actual marketing, this can already significantly increase conversion rates.

Question: What is the use of the tgid exported after filtering?

Answer: tgid is the unique identifier of a Telegram user and is more stable than a mobile phone number. Even if the user changes their mobile phone number, the tgid remains unchanged. It can be used for subsequent precise private messages, adding to groups, or connecting with third-party marketing tools for secondary contact.

Summary and action suggestions

The above process case shows the complete closed loop from number generation to telegram sieve number to result export. You can directly copy to the KK-DATA console for practice, without subscription and pay-as-you-go, which provides high flexibility. If you are facing the problem of low number verification efficiency and inaccurate marketing leads, you might as well try this solution.

👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot For more usage details, please check the official documentation: https://docs.kkdata.cc/

The official website also provides billing instructions and blog articles to help you be more stable on the road to overseas customers: https://kkdata.cc/