How do you filter for active users on Telegram? Boost your accuracy by utilizing data centers and number-screening tools.
In Telegram marketing and user operations, Telegram activity screening is becoming a crucial factor in determining reach efficiency. Whether it's community promotion, mass messaging, or user growth, the lack of a scientific Telegram activity screening process leads to a significant waste of resources on ineffective accounts. Many operators complain about "no one replying to messages," and the core reason is often a gap in the screening process—they neither understand the true activity status of their target users nor utilize data centers and screening tools to improve accuracy. This article will combine practical experience to break down how to systematically conduct Telegram activity screening from five dimensions and demonstrate its practical implementation using relevant tools.
I. Why is active user filtering on Telegram more important than simply collecting phone numbers?
Many teams spend a lot of time collecting user IDs or phone numbers on Telegram, but neglect the core metric of "activity level." The end result is often:
The message read rate has consistently been below 20%, and a large number of phone numbers have never been online.
The group contains bot accounts and disposable accounts, with zero interaction or feedback.
Users who don't comment or click links for extended periods cannot be converted.
Repeatedly contacting zombie accounts resulted in Telegram restricting its sending functionality.
Successful Telegram operations are built on the foundation of "effectively reaching real, active users." The essence of Telegram's activity filtering is to sift through a massive number of phone numbers, removing inactive, dead, and bot accounts, and retaining only those accounts that have recently been online and have engaged in interactive activities.
II. A preliminary screening method based on "last online time"
This is the most intuitive and basic filtering dimension. Telegram provides information on a user's "last online" or "most recent online" moment (depending on privacy settings). Key points for operation are as follows:
Log in to the TG data center backend and batch export the last online time field corresponding to each number.
Set a time threshold: It is generally recommended to retain users who have been online within the last 7 days as "active".
Excluding numbers that have been inactive for more than 30 days, these users are basically churned.
Note: Accounts that completely hide their online status require separate behavioral assessment (see point 3).
This screening process typically filters out 40%-60% of inactive numbers, significantly reducing subsequent sending costs.
III. Intermediate Filtering Based on Group Chat Frequency and Message Interaction
Relying solely on online time is not accurate enough, as some users log in daily but do not read or reply. Add a behavioral-level filter:
Count the number of times the target number has spoken in groups within the last 30 days (≥3 times is considered active).
Check for records of initiating private messages or clicking on short links.
Identify and filter out accounts that: never post, only receive messages without responding, or provide obviously scripted replies.
Aggregate message logs from the data center and export interaction metrics in batches.
This layer of screening can further improve user quality, and is especially suitable for business scenarios that require guided interaction.
IV. Auxiliary Judgment Based on Account Age and Completeness of Profile Picture/Brief Introduction
While not an absolute indicator, the following characteristics are often positively correlated with activity levels:
Accounts registered more than 3 months ago (to avoid newly registered test accounts).
Upload a real profile picture (not the system default or plain image).
The personal profile field is not empty, and the content conforms to normal human expression.
Not joined a large number of spam groups or public bot channels
A simple scoring system can be established by scraping publicly available data fields from the data center. For example, having a profile picture, a brief bio, and being registered for more than 90 days could be considered a high-potential tag. Such auxiliary rules can improve the screening margin for error.
V. Utilize data centers and screening tools to achieve automated batch screening
Manually checking user activity status point-to-point is extremely inefficient. Teams with engineering capabilities can build the following workflow:
Step 1: Export the candidate number pool (such as a list of group members) via the Telegram API or the official client.
Step 2: Call the data center interface to retrieve the data for each number in batches.
Last online timestamp
Number of posts in the last 7 days
Does the profile picture exist?
Account registration date
Step 3: Set filtering rules (e.g., last online for ≤7 days, ≥1 post, and non-empty avatar).
Step 4: Output a list of active numbers that meet the rules, which can be used directly for message sending or imported into the operations system.
Currently, there are mature auxiliary tools on the market that can greatly simplify this process. For example, ITG Global Filter has built-in Telegram activity judgment logic, which supports batch import of numbers and automatically identifies and removes zombie accounts, unregistered accounts, and long-term offline users, while retaining complete online time and interaction behavior data. Operators only need to upload the original number pool to obtain a high-quality list of active users with one click, improving the filtering efficiency from manual one-by-one to batch processing within minutes.
Conclusion
Telegram activity screening should not be a one-off task, but rather a standard pre-processing step before every large-scale outreach. Leveraging reliable activity signals from data centers and automating the process with professional screening tools (such as ITG's global screening) is crucial to controlling risk while truly improving the accuracy of message delivery and user conversion. It is recommended to start with two layers: "online time + posting behavior," and gradually refine the activity scoring model tailored to your specific business scenario.
ITG Global Screening is a leading global number screening platform that combines global number range selection, number generation, deduplication, and comparison. It offers bulk number screening and detection for 236 countries and supports 20+ social and app platforms such as WhatsApp, Line, Zalo, Facebook, Telegram, Instagram, Signal, Amazon, Microsoft and more. The platform provides activation screening, activity screening, engagement screening, gender/avatar/age/online/precision/duration/power-on/empty-number and device screening, with self-screening, proxy-screening, fine-screening, and custom modes to suit different needs. Its strength is integrating major global social and app platforms for one-stop, real-time, efficient number screening to support your global digital growth. Get more on the official channel t.me/itgink and verify business contacts on the official site. Official business contact: Telegram: @cheeseye (Tip: when searching for official support on Telegram, use the username cheeseye to confirm you are talking to ITG official.)