Telegram SCRM user tag practical guide: customer profiling, segmentation and follow-up methods

When working in customer service and community operations on Telegram, have you ever encountered these scenarios: the same user cannot be recognized after changing their nickname; after following up half of the consultation, colleagues have no idea about the progress after the shift; you want to send discounts to VIP users, but you can only manually browse the chat history to find the person? Telegram’s native chat tool cannot mark users and lacks historical portraits, resulting in customer service having to rely on “memorizing people” to work.

This is the core problem that Telegram SCRM User Tag wants to solve - to allow customers to change from “remembering people” to “recognizing people by reading tags”, using a structured customer portrait system to improve response speed and service quality. This article will help you master the practical methods of user labeling and grouping, and introduce how to achieve a complete service closed loop through follow-up status.

Why does Telegram SCRM need user tags and customer portraits?

Telegram’s chat interface itself only provides nicknames, avatars, and chat history. When customer service faces hundreds of conversations every day, memory alone cannot distinguish:

• Who are new users and who are repeat customers?
• Who is complaining, who is just asking about the price?
• Who needs immediate response and who can be dealt with later?

User tags are “identity tags” that assign business attributes to each contact. Combined with customer portraits (including interaction frequency, triggering behaviors, language preferences, etc.), customer service can understand the user’s background the moment the session is opened, avoid repeatedly asking for basic information, and go directly to the problem.

Take TG-Staff as an example. In its real-time two-way chat panel, each user will display tags and portrait information. Customer service can quickly determine the conversation priority based on the tags. This is the fundamental reason why SCRM system is more efficient than native Telegram.

The core components of user portraits in Telegram SCRM

A complete user portrait usually contains the following four dimensions:

Dimension	Content	Customer service scenario value
Basic information	Telegram ID, nickname, language, first interaction time, last active time	Quickly identify user identity and activity
Behavioral data	Interaction frequency, triggered Bot commands, session duration	Understand user usage habits and points of interest
Custom labels	Manual/automatic labels such as “VIP customer”, “After-sales - pending”, “Inquiry in progress”	Give users business attributes for easy filtering
Follow-up status	Pending, in follow-up, closed, need to return visit	Manage session life cycle to avoid omissions

Basic information and behavioral data: automatically build user profiles

The system will automatically record each user’s basic data without manual input. For example, TG-Staff will record the user’s Telegram ID, first interaction time, last active time, etc. When a user triggers a Bot command (such as /pricing or /support), the system writes the behavior to the user profile.

These automatic data are the basis for subsequent grouping - you can filter users who are “active within 7 days and have triggered the /pricing command” as potential purchase intention customers.

Custom labels: Manually assign business attributes to users

Automatic data can only reflect objective behavior, while custom tags allow operators to assign subjective business attributes to users. Common scenarios include:

• Customer value tag: VIP customers, ordinary users, potential customers
• Problem Type Tag: Complaint, Technical Consultation, After-Sales
• Stage Label: Inquiry in progress, order placed, return visit required

For example, customer service determines that the user is a “complaining user” during the conversation and manually adds the label “Complaint - Processing”. This label will be immediately associated with the user portrait. When other customer service personnel see this label, they will be able to directly understand the situation without repeated communication.

How to achieve efficient grouping and filtering through user tags?

With tags in place, the next step is grouping. The essence of grouping is tag combination logic, which accurately locates the target user group through the filtering conditions of “AND”, “OR” and “NOT”.

Tag combination filtering: accurately locate target user groups

Let’s say you set the following tags:

• Customer value: VIP, ordinary
• Question types: complaints, inquiries, after-sales
• Status: pending, following up, closed

You can combine filters:

• “VIP + Pending” → Prioritize response to unprocessed sessions of high-value users
• “Complaint + Follow-up in progress” → Check the users who are complaining and make sure there are no omissions
• “VIP - Closed” → Filter the unclosed sessions among all VIP users and make proactive return visits

In the user management background of TG-Staff, you can quickly filter out target groups through a combination of multiple conditions. This function is especially important when customer service staff are on shift or team collaboration - new shift customer service staff can directly filter conversations with the “pending” label without having to browse through history.

Operational actions after grouping: group sending, distribution and priority

Grouping is not the end, but the starting point of operational actions. Common operations include:

• Targeted mass sending: Send new product discount information to users in the “VIP + active within 7 days” group. The professional version of TG-Staff supports unlimited batch messaging of messages and precise reach according to groups.
• Customer Service Assignment: Assign sessions with the “Complaint” tag to senior customer service, and assign sessions with the “Inquiry” tag to sales specialists.
• Priority sorting: In the customer service session list, put sessions with the “VIP” label at the top to ensure that high-value users respond first.

Follow-up status management: complete closed loop from “pending” to “closed”

Tags define user attributes and follow-up states define session lifecycle. A complete service process usually includes:

1. Pending: New session or unresponsive session
2. Following Up: Replied but not closed, need to wait for user feedback
3. Closed: The problem has been solved and the session has ended.
4. Return visit required: Processing completed but follow-up required (such as after-sales return visit)

In TG-Staff’s real-time chat interface, customer service can quickly switch user status during the conversation without jumping out of the chat window. This instant operation avoids repeated communication caused by “forgetting to update the status after replying”.

Extended application of user tags in automatic translation scenarios

When teams serve multilingual users, the combination of tags and automatic translation can significantly improve efficiency. For example:

• Add language tags for “English users”
• The system automatically matches the translation direction according to the tag (English → Chinese)
• Chinese messages sent by customer service are automatically translated into English

TG-Staff’s automatic translation function supports AI translation (standard version) and Google/DeepL professional translation (professional version). Combined with the language tag, customer service does not need to manually select the translation direction every time. The system will automatically identify the user’s language preference and complete the translation. For cross-border customer service teams, this is an effective way to reduce operating steps and improve response speed.

FAQ: Telegram SCRM user tags and customer portraits

Is there a limit to the number of tags?

Different SCRM platforms have different limits on the number of tags. Taking TG-Staff as an example, the standard version supports basic tags, and the professional version supports more customized tags and user portrait functions. Please see the official website package page for specific quantities.

Can tags be modified or deleted in batches?

Most SCRM platforms support batch operations. TG-Staff’s user management background supports adding/removing tags in batches after selecting multiple users. It also supports modifying tag names on the tag management page. After modification, the tags of all associated users will be updated simultaneously.

How to avoid management confusion caused by too many tags?

It is recommended to set label naming conventions (such as “Business Dimension-Status”) and regularly clean up labels that are no longer used. At the same time, limit the maximum number of tags per user to avoid over-tagging.

Summary: From tags to operations, build a closed loop of Telegram user management

User tags and customer portraits are not icing on the cake, but the core of Telegram SCRM’s upgrade from a “chat tool” to an “operation system”. By defining user attributes through custom tags, reaching precise targets through group filtering, and managing the session life cycle through status follow-up, the team can achieve:

• Improved customer service efficiency: understand the user’s background immediately after opening a session, reducing repeated inquiries
• Service quality improvement: priority response to high-value users, special handling of complaints from users
• Improved operational accuracy: group messages to avoid harassing all users

If you are still using Telegram’s native chat for customer service, you might as well try TG-Staff’s user tag and customer portrait functions. Sign up to enjoy a 3-day free trial and experience the sophisticated operation model from “just chatting” to “with data, labels and follow-up”.

👉 Free trial TG-Staff 📖 View user tags and portrait configuration documents 💬 Contact customer service Bot: @tgstaff_robot