Precisely Screening Active Telegram Users: How to Build Your High-Value User List Using "Last Seen Time"

In Telegram community operations and precision marketing, screening Telegram active time has become a crucial entry point for identifying target audiences and optimizing engagement strategies. Whether promoting products, building communities, or conducting market research, accurately pinpointing users who are genuinely active and have high engagement levels can significantly enhance marketing efficiency and conversion rates. This article aims to delve deeply into how to systematically construct a truly commercially valuable, high-quality user list through scientific methods, particularly by leveraging the core dimension of screening Telegram active time, utilizing "Last Seen Time" data, and harnessing the powerful capabilities of the ITG tool for comprehensive screening.

I. Why is "Last Seen Time" the Gold Standard for Screening Active Users?

Among various user behavior data points, "Last Seen Time" is a unique and powerful metric. Compared to simple group membership or usernames, it directly reflects a user's recent platform usage habits and their potential reachable status.

1. Real-time Nature and Relevance: This timestamp indicates when a user last opened Telegram. A user whose "Last Seen Time" shows "just now" or "a few minutes ago" is far more likely to immediately see and respond to your message than one showing "a month ago." This directly impacts message open rates and subsequent interaction rates.

2. A Proxy for Measuring Willingness to Engage: While not directly equivalent to content engagement, users who frequently log into Telegram typically maintain a higher level of attention to information within the platform (including group messages, channel updates, and private chats). They are potential active participants, more likely to read announcements, join discussions, or click on links.

3. Efficient Exclusion of Invalid Accounts: The internet environment contains a vast number of bot accounts, dormant accounts, or inactive "ghost members." By setting a "Last Seen Time" threshold (e.g., screening for users active within the past 7 days), you can quickly and efficiently exclude these invalid audiences in bulk, focusing marketing resources on real, live user groups.

However, manually checking and recording each user's "Last Seen Time" is impractical. This is precisely where professional tools are needed. The ITG tool for comprehensive screening was created for this purpose, capable of automating and batch-processing this labor-intensive task, transforming data into actionable insights.

II. Strategies for Multi-Dimensional "Active Time" Screening Using the ITG Tool

Relying solely on single-point-in-time screening is insufficient. By utilizing the ITG tool for comprehensive screening, we can design more refined, multi-layered strategies for screening active users.

Step 1: Basic "Activity" Screening (Excluding the Dormant Layer)

• Operation: Use the ITG tool to set the "Last Seen Time" criterion to within the past 72 hours, 7 days, or 30 days. This is the most basic "liveness" filter, aiming to clean the list and ensure the users on it are "alive."

• Application Scenario: Suitable for large-scale community cleanup, initial advertising audience targeting, or eliminating clearly inactive members, saving costs for subsequent deeper engagement.

Step 2: Screening Users Active in Specific Time Slots (Identifying Patterns and Preferences)

• Operation: The ITG tool can support more complex time-period analysis. For example, screening for users whose "Last Seen Time" consistently appears during specific periods (e.g., weekday evenings 20:00-23:00, or weekend mornings).

• Insight and Application: This helps identify users' online habits. For operators who need to publish important announcements, host real-time online events, or run time-limited advertisements, this allows for precise matching with users' active windows, ensuring messages are delivered at the most opportune time, greatly increasing exposure and immediate participation rates.

Step 3: Screening Highly Frequent Active Users (Identifying Core Fans)

• Operation: Set very short "Last Seen Time" intervals for screening, e.g., online multiple times "within the past 24 hours," or perform composite screening combining "Last Seen Time" with "message sending frequency" where possible.

• Insight and Application: These users are the "super-connectors" and core participants of a community. They hold the highest value and are most suitable as:

  • Seed Users: Invited to participate in new product beta testing and provide feedback.

  • Community Leaders: Cultivated to become administrators or discussion facilitators.

  • Word-of-Mouth Propagation Nodes: Prioritize providing them with promo codes or referral incentives to encourage secondary dissemination.

Step 4: Dynamic Monitoring and Re-engagement Screening

• Operation: Regularly (e.g., weekly) use the ITG tool to run screenings, monitoring changes in users' "Last Seen Time" trends. Pay special attention to user groups transitioning from a "recently active" state to a "recently inactive" state (e.g., from within 3 days to over 2 weeks).

• Insight and Application: This forms a dynamic user health monitoring system. For newly inactive users, it can automatically trigger a re-engagement process, such as sending a personalized private message greeting, an exclusive mild offer, or an interesting content recommendation, attempting to rekindle their interest and prevent complete churn.
  
  

III. Beyond Time: Building a Composite High-Value User List

"Last Seen Time" is a powerful starting point, but a truly "high-value" list requires the fusion of multi-dimensional data. The strength of the ITG tool for comprehensive screening lies in its ability to integrate other key dimensions for composite conditional screening:

1. Active Time + Identity Attributes: Combine screening based on a user's role in a group (admin, regular member), username keywords, bio information, etc. For example, finding users who were "active in the past week" and whose "bio contains 'developer' or 'programmer'" creates a precise B2B tech product promotion list.

2. Active Time + Behavioral Data: While Telegram API restrictions are significant, where possible, combining screening with a user's historical behavior in specific groups or channels (e.g., join time, approximate posting frequency) can more accurately gauge their sense of belonging and depth of participation.

3. Active Time + Source Tags: If different promotional links or invitation entry points are used in operations, users from different sources can be tagged via the ITG tool, and their subsequent active time can then be analyzed. This effectively evaluates the quality of users from different acquisition channels, optimizing marketing channel investment.

The user list constructed through this "time-centric, multi-dimensional cross-validation" method is no longer a simple contact list but a clearly structured, intent-defined, and segmentable "Digital Asset Map."

IV. Practical Guide: The Compliant and Efficient Path from Screening to Engagement

When implementing the above strategies, two core principles must be followed: efficiency and compliance.

Efficient Execution Process:

1. Define Objectives: Determine if the purpose of this screening is community cleanup, product promotion, or finding partners.

2. Configure the ITG Tool: Based on the objectives, set the corresponding "Last Seen Time" threshold and other composite conditions on the ITG tool for comprehensive screening platform.

3. Execute and Export: Run the screening task to obtain a precise list of user IDs.

4. Connect with Layered Strategies: Import the list into your CRM system, marketing automation tool, or Telegram broadcast tool, and prepare differentiated communication content and engagement strategies for users at different levels.

Compliance and Privacy Reminders:

• Respect User Privacy: Large-scale data screening and processing must strictly comply with Telegram's Terms of Service and relevant data privacy regulations (e.g., GDPR). Avoid using data for purposes not explicitly consented to by the user.

• Provide Value, Not Spam: Precise screening based on active time aims to provide users with information or value they might need at an appropriate time, not for indiscriminate information bombardment. The quality and relevance of the engagement content are key.

• Implement Clear Opt-out Mechanisms: In any proactive outreach, clear and convenient options to unsubscribe or decline further contact should be provided.
  
  

Conclusion

In today's age of information overload, the competition for user attention is increasingly fierce. For operators, marketers, and community builders within the Telegram ecosystem, the extensive broadcast model has become ineffective. The core of refined operations lies in the precise understanding and segmentation of "people." Using "Last Seen Time"—an objective, dynamic data point—as an anchor, and leveraging powerful technical tools like the ITG tool for comprehensive screening for deep mining and intelligent segmentation, enables us to efficiently identify those truly online, willing to engage, and high-potential, high-value individuals from a massive user base.

Building such a dynamic, data-driven, high-value user list is more than a technical operation; it is an upgrade in operational mindset. It propels marketing activities from "guessing" to "insight," from "interruption" to "reach," from "mass broadcasting" to "personalized dialogue," ultimately reducing costs while significantly enhancing user lifetime value and brand loyalty. This lays a solid data foundation for achieving sustainable growth on Telegram.