Telegram User Grouping Guide: Accurate Reaching Based on Tags, Behaviors and Activity

#Telegram User Grouping Guide: Accurate Reaching Based on Tags, Behaviors and Activity

Are you still sending the exact same message to all your Telegram Bot subscribers? New users are lost directly after receiving complicated menus, high-value customers are interrupted by low-frequency promotions, and the customer service team cannot distinguish between VIP and ordinary inquiries-these are typical symptoms of operations without segmentation.

The core value of Telegram user grouping is to divide users into different groups based on attributes, behavior and activity, and then customize messages and customer service strategies for each group. This not only increases conversion rates, but also significantly reduces complaints and blocking rates. This article will help you establish an implementable operational layering system from the grouping dimensions, practical steps to effect measurement.

Why does Telegram operation require user grouping?

Many teams are accustomed to “one-stop” mass messaging in the early days: all users receive the same welcome message, the same promotion, and the same recall message. The inefficiencies and risks of this approach are becoming increasingly apparent.

Three major pain points of non-group operations

1. New users will be lost immediately after receiving complex menus A user who is new to Bot knows nothing about the product’s features. If 8 menu buttons are displayed on the first screen, users will most likely exit directly. They want the boot sequence, not the full functionality.

2. High-value users are disturbed by low-frequency messages Paid active users who receive “limited time discount” push notifications every day will feel over-marketed. They prefer to be notified of exclusive benefits rather than mass promotions.

3. Customer service cannot distinguish between VIP and ordinary consultation All users enter the same customer service queue, and the waiting time for VIP users is the same as that of ordinary users. This both reduces VIP satisfaction and wastes the time of senior agents handling simple inquiries.

How to improve customer service efficiency and conversion by user grouping

• VIP Priority Access → Automatically transfer VIP labeled users to premium seats, reducing the average waiting time by 60%.
• New User Onboarding Sequence → Send a 3-step onboarding process to “first interaction” users, which can increase activation rates by 30–50%.
• Silent User Recall → Send low-frequency recall messages to users who have not interacted in 30 days, improving retention rate by 10–15%.

Grouping is not the “icing on the cake”, but the only way for Telegram’s operations to move from extensive to refined.

Three core dimensions of user grouping

In actual operations, we usually stratify users from three dimensions. They can be used individually or in combination.

Tag grouping: classified by user attributes and sources

Applicable scenarios: Channel source (advertising/natural search/recommendation), product preference (Product A/Product B), language (Chinese/English), paid status (free/subscription/lifetime).

Operation method: Manually tag in the customer service system (such as “Advertising_2025Q1” “Paid_English”), or automatically tag through rules. For example, when a user follows a Bot through a specific promotion link, the “Source_Advertisement” label is automatically added.

Behavior grouping: according to user operation path and interaction records

Applicable scenarios: Whether to click the menu button, whether to complete the registration process, whether to initiate a work order, whether to complete the purchase.

Operation method: Trigger records based on Bot commands, and classify users into groups such as “active buyers”, “browsing but not buying”, “high-frequency consultation”, etc. For example, after a user triggers the /purchase command, it is automatically marked as a “purchased user”.

Activity grouping: based on recent interaction time and frequency

Applicable scenarios: Today’s active users, 7-day silent users, and 30-day non-interactive users.

Operation method: Use the last message timestamp as the basis. For example, users whose last interaction time was 7 days ago are classified as “silent_7 days”, and users who have not interacted for 30 days are classified as “churn_30 days”. This dimension is often used to control group frequency.

Practical steps: Build a user grouping system in TG-Staff

The following takes TG-Staff as an example to demonstrate how to build a grouping system from scratch. TG-Staff is a customer service and operation SaaS platform for Telegram Bot, supporting real-time chat, visual process editing and batch sending.

Step 1: Add labels to conversations and establish basic groups

1. Log in to TG-Staff Console and enter the “Session” list.
2. Click on any session and find the “Label” area in the user details panel on the right.
3. Click “Add Tag”, enter the tag name (such as “VIP” “Refund Consultation”), and press Enter to confirm.

Once completed, the tag will be associated with the user profile. Later, when filtering or group sending, you can directly filter by tag conditions.

Step 2: Use the visual process to automatically mark behavioral groups

Manual labeling is suitable for customer service scenarios, but behavioral grouping needs to be triggered automatically. TG-Staff’s Visual Command Flow Editor can accomplish this task.

1. Enter the “Bot Process” module and click “New Process”.
2. Drag a “Command Node” and set it to /complete_purchase.
3. Add an “action node” after the command node and select “Add User Label”.
4. Enter the label name “Purchased User” and save the process.

When a user triggers the /complete_purchase command in Telegram, TG-Staff will automatically add a “Purchased User” tag to the user. The entire process requires no coding and achieves behavioral grouping at zero cost.

Step 3: Implement differentiated mass messaging and customer service distribution based on grouping

The ultimate goal of grouping is accurate reach.

Differentiated mass sending:

• Enter the “Group” module and select “Filter by tag”.
• Check the label “Active for 7 days_Unpaid”, enter the promotional copy, and set the sending time.
• The system will only send messages to users in this group to avoid disturbing other groups.

Intelligent customer service allocation:

• Enter the “Customer Service Routing” settings and create a new rule.
• Condition: User tag contains “VIP”.
• Action: Automatically transfer to “Advanced Agent Queue”.
• In this way, after VIP users enter the customer service system, they will be handled directly by experienced agents.

Best practices and common misunderstandings in group operations

Best practice: From coarse to fine, iterate step by step

• Initial grouping: First divide into 3 levels according to activity (high/medium/low), and then overlay labels (paid/free/channel). For example, “high activity_paid” and “low activity_free”.
• Iteration frequency: Review group sending data (open rate, reply rate, conversion rate) every two weeks, and adjust grouping definitions. If the conversion rate of the “medium active_paid” group is lower than that of the “high active_paid” group for a long time, consider merging the former into the latter, or adjusting the message content.

Common misunderstanding: Too detailed grouping leads to increased management costs

• Misunderstanding: It was divided into more than 20 tags at the beginning. When faced with dozens of combinations, it was difficult to choose, and finally gave up grouping and returned to “one pot”.
• Countermeasure: Keep core clusters ≤6. For example:
  • High activity_Paid
  • High activity_free
  • Medium Active_Free
  • Low active_paid
  • Silence_30 days
  • Others (all in all)

Effect measurement indicators of group operations

Whether the grouping strategy is effective needs to be quantitatively evaluated. The following 3 indicators are the most critical:

Indicators	Definition	Direction of Improvement
Message open rate	Proportion of users clicking on group messages	Targeted content + appropriate frequency
Message response rate	Ratio of users replying to messages	Interactive words + clear CTA
Conversion rate	The proportion of users completing the target action (purchase/registration)	The more accurate the grouping, the higher the conversion

It is recommended to count these indicators every week/month and compare the changes before and after the group. If the conversion rate of the “high active_paid” group is continuously higher than that of the “medium active_free” group, it means that the grouping is effective; if the difference is not large, you need to check whether the grouping definition is accurate.

Frequently Asked Questions (FAQ)

**Q1: Can labels be modified or deleted in batches? ** A: Yes. In the “User Management” module of TG-Staff, batch adding/removing tags is supported after filtering by tags. You can also edit or delete tags directly on the “Tag Management” page.

**Q2: Is there any additional charge for mass sending after grouping? ** A: The group sending function is included in the package. Both the Standard Edition and the Professional Edition support filtering group messages based on conditions. For specific quotas, please see Official Website Package Page.

**Q3: Can the tag function be used in the free trial version? ** A: Yes. Sign up to enjoy a 3-day free trial, during which all functions (including tags, user portraits, and group messaging) can be used.

**Q4: Does the grouping condition support multiple label combinations? ** A: Support. When filtering, you can select “Contains label A and contains label B”, or “Contains label A and does not contain label B” to achieve fine grouping.

Summary and next steps

User grouping is not a one-time technical operation, but a continuously iterative operational strategy. Starting from the three dimensions of tags, behavior, and activity, first build basic groups in TG-Staff, and then continuously optimize through group data review, your Telegram Bot operation efficiency will gradually improve.

• Main Action: Go to app.tg-staff.com to register for trial
• Check the documentation: Official documentation Learn more about grouping and group sending configurations
• Get help: Contact customer service Bot @tgstaff_robot for one-on-one guidance