Want to find high-value Telegram users in bulk? How to filter accounts by age and activity level?

To identify high-value users on Telegram in bulk, batch screening is a highly effective method. However, many people initially face a challenge: how to efficiently filter accounts based on their actual age and activity level? Manual operation alone is not only inefficient but also makes it difficult to determine which accounts are worth long-term follow-up. Therefore, utilizing tools for batch Telegram screening has become an essential tool for many operations teams. Next, we will analyze step-by-step how to accurately identify high-value Telegram users using these two core metrics: age and activity level.

I. Why is account age important?

Account age refers to the registration date of a Telegram account. This seemingly basic data can reveal a lot of information:

• Older accounts (those registered a long time ago) are usually more stable and may be long-term users or real individuals.

• A new account may represent a new user, but it could also be a temporary registration or a marketing account.

If you don't pay attention to account age, you may encounter the following problems:

• Investing promotional resources in temporary accounts resulted in extremely low conversion rates.

• Misidentification of user identity leads to marketing messages being sent to irrelevant groups.

• Choosing an unstable account during collaborations or traffic generation can negatively impact long-term results.

Age is just a starting point; more important is to combine activity level to judge the "health status" of the account.

II. How to determine an account's activity level?

Activity level shouldn't just be judged by "recent logins," but rather by a comprehensive analysis of user behavior habits:

• Do users frequently speak in groups or channels?

• Does the account regularly update its profile picture, bio, and other information?

• Do you have any interaction history with other users?

If these active signals are ignored, the following may occur:

• Send event invitations to "zombie accounts" that never interact with the platform.

• In scenarios requiring rapid feedback, an account with a slow response time was selected.

• Mistakenly believing the account is valuable, but in reality failing to generate any conversions.

Highly active accounts are more likely to respond to messages and participate in activities, making them a priority target in operations.

III. What problems can active period analysis solve?

Active periods are not simply determined by whether "registration has occurred," but rather by:

• How long ago was the user logged in?

• Is it currently in a high-activity, medium-activity, or low-activity category?

• Can I send a message immediately? Or do I need to put it into a "cold start" process?

If you ignore this time data, the following problems may occur in your operations:

• The coupons were sent to users who hadn't logged in for a long time, but it was a complete waste of money.

• Customer service resources were allocated to inactive accounts, reducing response efficiency.

• Assigning the bot to an inactive account caused frequent system errors.

• Importing advertising leads into the CRM without considering their status results in a chaotic and inefficient backend system.

Every number should be treated equally, but rather managed in tiers based on its popularity over time.

IV. How to use tools to perform batch filtering?

Manual filtering is not only slow but also prone to errors. Therefore, choosing the right tool is crucial. A good tool should help you:

• Quickly filter accounts within a specified age range

• Automatically label activity level (high, medium, low).

• Provides export and grouping functions for easier subsequent operations.

For example, tools like ITG's global filtering can process a large number of accounts at once by setting multi-dimensional conditions and output a clear, actionable list. This not only saves time but also improves the accuracy of the filtering.

V. How to follow up efficiently after screening?

Screening is only the first step; the more important steps are the follow-up actions:

• For highly active users, instant content such as promotions and events can be pushed to them.

• For active users, reactivate them through regular reminders or light interactions.

• For low-activity users, consider whether it is worthwhile to nurture them long-term or temporarily shelve them.

Layered operations can avoid wasting resources and make each message more accurate and effective.

VI. How to use tools to filter account age?

You can use the ITG global filtering tool to efficiently filter account age. Follow these steps:

• Set time range: In the tool's filter, select the registration period of the account, such as the last month, 1-6 months, or earlier.
• Cross-validation: Combines registration time with recent login activity data to determine the actual status of the account.
• Batch processing: After filtering, add tags to accounts of different age groups in batches to facilitate subsequent group operations.
• Data visualization: Use tools to view user age distribution charts and quickly grasp the overall account structure.

Its filtering function can distinguish between new and old users with one click, and combined with activity data, it generates a clear user segmentation report, making age filtering intuitive and efficient.

Conclusion

The key to bulk screening of high-value Telegram users lies in focusing on two core metrics: account age and activity level. Using tools to assist in segmentation not only improves efficiency but also allows for more precise operational actions. Remember, a good start is half the battle – paving the way for subsequent conversions begins with the screening process.