Only TG Satisfaction Survey Setup Guide: Easily Collect CSAT Feedback After Telegram Bot Conversations

In Telegram Bot customer service scenarios, after a user completes a consultation, teams often face a core question: Is the user satisfied? Traditional CSAT (Customer Satisfaction) surveys via email or web forms are naturally hindered in Telegram conversations—users need to leave the chat interface, open external links, and fill out lengthy forms, resulting in very low conversion rates.

With the only TG Satisfaction solution, leveraging TG-Staff’s diversion links and visual command workflows, you can have users rate directly within Telegram using buttons or numeric input after the conversation ends—no coding required. This guide will fully demonstrate how to set up this lightweight CSAT collection process and help you continuously improve customer service quality based on data.

Why Collect CSAT Satisfaction in Telegram Bot?

CSAT is the most direct metric for measuring customer service quality. In the Telegram ecosystem, users are accustomed to instant, lightweight interactions. If you push a “Please rate this service” button after the conversation, users just tap once, resulting in much higher feedback rates than email or external forms.

What is CSAT and Why Is It Suitable for Telegram Customer Service?

CSAT is typically presented as a 1-5 star or 1-10 scale, asking users “Are you satisfied with this service?” In a Telegram Bot, CSAT can be embedded as Inline Keyboard buttons or numeric input fields, allowing users to complete it within the chat interface—zero bounce. This means for customer service teams:

• Instant feedback: Collect evaluations when users’ memories are freshest, leading to higher data authenticity.
• No development required: TG-Staff provides a visual editor where you can drag and drop to configure the rating flow.
• Increased user engagement: Users feel the team values their opinions and are more willing to use the Bot again.

Pros and Cons: Only TG Solution vs. Building Your Own CSAT System

Dimension	Only TG (TG-Staff) Solution	Self-Built CSAT System
Development cost	Zero code, drag-and-drop configuration in console	Requires backend development, database, Telegram API integration
Deployment time	15-30 minutes	Days to weeks
Data correlation	Automatically correlates user profiles, agents, conversation records	Must design data models and correlation logic yourself
Flexibility	Limited to platform’s preset nodes and flows	Fully customizable
Cost	Included in subscription (available from Standard plan)	Development and maintenance costs + server fees
Suitable teams	SMBs, startups, operations staff	Large and medium teams with dedicated developers

If your team is not large, or you want to quickly validate CSAT collection effectiveness, the only TG solution is the most practical choice.

Core Logic and Preparation for Implementing CSAT with Only TG

The core idea of TG-Staff is: After the conversation ends, the Bot automatically sends a rating message triggered by user information carried in the diversion link. This requires the following prerequisites:

• A Telegram Bot already connected to TG-Staff (Bot Token added in the console).
• A diversion link configured and published for users to enter the Bot conversation.
• At least a Standard plan (free 3-day trial available to test the full process).
• A visual command workflow to define the “after conversation ends” auto-reply.

You do not need to write any code; all configuration is done in the TG-Staff console (https://app.tg-staff.com）内完成。

Step 1: Configure Conversation Diversion and Diversion Links

The diversion link is key to CSAT attribution—it records which channel the user came from (e.g., ads, social media), and after the conversation ends, TG-Staff can identify the user and trigger subsequent workflows.

1. Log in to the TG-Staff console and go to “Project Settings” → “Diversion Links” page.
2. Click “Create Diversion Link”, and the system generates a short link in the format https://app.tg-staff.com/{code}.
3. In the URL parameters, we recommend adding source markers like utm_source (e.g., ?utm_source=twitter) for later analysis of which channels yield higher user satisfaction.
4. After saving, use this diversion link in your ads, social media posts, or website buttons.
5. In “Project Settings” → “Conversation Diversion”, set the diversion rule to Online First. This way, when agents are online, users are prioritized for assignment to online agents; if all agents are offline, users are queued according to round-robin assignment rules. This ensures users receive human service quickly, reducing wait anxiety—longer waits may lower CSAT scores.

6. Verification: Open the redirect link in a browser and confirm it redirects to your Bot, which automatically sends a welcome message. At this point, TG-Staff has recorded the user’s source information.

Step 2: Design the CSAT Feedback Command and Welcome Flow

Now, you need to create a visual command flow that allows the Bot to automatically push CSAT ratings after a conversation ends.

1. Go to the “Command Flow” page in the TG-Staff console and click “Create Flow”.
2. Drag a Trigger Node from the left node panel and set the trigger condition to “Conversation Ended”. This means the flow is activated when the user’s conversation with an agent ends (agent closes the session or user times out without replying).
3. After the trigger node, add a Message Node with the content:

   感谢您咨询我们！请为本次服务评分（1-5 星），您的反馈对我们非常重要。

4. After that message node, add an Input Node with type “Inline Keyboard Button”. Create 5 buttons corresponding to 1 to 5 stars, and set the button’s callback_data to csat_1, csat_2, etc.
5. For each button, add a Condition Branch that checks the value of the button clicked by the user. When a user clicks a star rating, trigger a Data Record Node to write the rating to a custom field in the user profile (e.g., csat_score).
6. Finally, add a Reply Node to send a thank-you message:

   您已成功提交评分！我们将继续努力为您提供更好的服务。

How to Design a User-Friendly CSAT Rating Menu?

• Keep button text concise: Use numbers or star icons directly (e.g., ⭐️⭐️⭐️⭐️⭐️). Avoid negative wording like “Very Dissatisfied”, as users may be reluctant to express dissatisfaction directly, causing ratings to cluster at 4-5 stars.
• Thank immediately after rating: Make users feel their feedback is valued, not ignored.
• Optional open feedback: If you want to collect more detailed opinions, you can add a text input node after the rating asking “Do you have any suggestions?” However, note that this may reduce completion rates; it is recommended to use only in the Pro version or for specific sessions.

Best Practices for Associating CSAT Data with User Profiles

In TG-Staff, user profiles allow you to store custom fields. After a user rates, in addition to recording the score, you can also record:

• Rating time
• Session ID (to trace back the conversation)
• Agent ID (for later analysis of agent performance)

This way, in the “User Profile” module, you can see each user’s CSAT history and which agent they last spoke with. This is the foundation for subsequent data analysis.

Step 3: Test the CSAT Flow and Verify Data Collection

After configuration, be sure to perform end-to-end testing to ensure the flow is stable.

1. Simulate user path:
   • Open the redirect link on your phone or computer to enter the Bot.
   • The Bot automatically sends a welcome message (if you configured a welcome node in the flow).
   • Chat with the Bot (or wait for an agent to join), then actively end the conversation (or have the agent close the session).
2. Trigger the rating:
   • After the conversation ends, the Bot should automatically push a rating message displaying 1-5 star buttons.
   • Click any star button.
3. Verify data:
   • Log in to the TG-Staff console, go to the “User Profile” module, and search for the test user.
   • Check the custom fields to confirm csat_score is recorded and shows the correct score.
   • Go to the “Data Statistics” module (Pro version) to see if the CSAT rating distribution chart has updated.
4. Debug common issues:
   • Rating not triggered: Check if the trigger condition for the command flow is “Conversation Ended” and if the flow is published.
   • Buttons not working: Confirm that the callback_data names are spelled correctly and that the condition branches match correctly.
   • Data not recorded: Check if the data record node has the correct custom field name configured.

Using CSAT Data to Optimize Customer Service and Operational Decisions

Collecting CSAT ratings is not the end goal; using data to improve is. Here are some actionable suggestions:

• Identify low-rated sessions: In user profiles, filter for users with csat_score ≤ 3 and review conversation logs to analyze the cause of dissatisfaction (slow response? inaccurate answer? attitude issues?).
• Evaluate agent performance: Group agents by average CSAT score as a basis for internal training or incentives. Note that factors like conversation complexity should be considered to avoid a single metric.
• Optimize auto-reply flows: If certain common questions consistently receive low CSAT ratings, the auto-reply content may not be accurate enough. Adjust the welcome message or FAQ nodes in the command flow.
• Segmented operations: Push promotional messages or invite high-satisfaction users (4-5 stars) to join a community; send apology messages to low-satisfaction users (1-2 stars) with compensation (e.g., coupons) to recover the relationship.

Pro version users can also use the “User Profiles and Statistics” module to generate CSAT trend charts, viewing average score changes weekly or monthly to detect fluctuations in service quality.

Lightweight CSAT Checklist and Precautions

Before going live, go through the following checklist to ensure everything is foolproof:

• The redirect link has been created and published, and the URL parameter includes the source channel.
• The session routing rule is set to “Online First” (or a strategy suitable for your team).
• The command flow has been created with the trigger condition “Conversation Ended”.
• The callback_data for rating buttons is correctly set, and condition branches match.
• The data record node has written the rating to the user profile custom field.
• End-to-end verification has been completed in the test environment (user path + data recording).
• Confirm that the rating message will not be pushed repeatedly after the user exits the Bot (to avoid harassment).

FAQ

Q: Where will CSAT rating data be saved? Can I export it?

A: Rating data is saved in TG-Staff user profile custom fields (e.g., csat_score). The console supports viewing historical ratings on the user details page, and both Standard and Pro plans allow viewing rating distribution via the “Data Statistics” module. To export raw data (e.g., CSV), contact support Bot @tgstaff_robot to confirm export support for your current plan.

Q: After a user rates, can I see which agent served them?

A: Yes. In the user profile, you can view detailed records of the user’s most recent conversation, including agent ID and conversation start/end time. Combined with CSAT ratings, you can effectively evaluate each agent’s service quality. The Pro plan also supports CSAT average scores grouped by agent.

Q: Can I test the CSAT flow with the free trial?

A: Yes. Registering for TG-Staff gives you a 3-day free trial, including all Standard plan features (routing links, conversation routing, visual command flows). You can complete all setup and testing in this guide within the trial period. After the trial expires, renew to keep the flow running.

Q: What if users don’t click the rating button?

A: This is common. You can set up an “expiration handling” mechanism: in the command flow, add a “timeout” branch for the rating button (e.g., after 24 hours without clicking), automatically send a gentle reminder, or stop pushing. Avoid forcing users to rate, as it may cause resentment. Also, monitor conversation quality for unrated users—dissatisfied users are more likely to rate.

Q: Does CSAT support multiple languages (e.g., English, Spanish)?

A: Yes. TG-Staff’s visual command flow supports multilingual message nodes. You can create different rating flows for users of different languages, or use conditional branches in one flow to detect user language (based on Telegram’s language settings) and push corresponding rating buttons. Auto-translation also helps agents reply in the user’s language, boosting overall satisfaction.

Summary & Next Steps

With this guide, you’ve mastered the complete method of collecting CSAT feedback in a Telegram Bot using the only TG satisfaction solution (TG-Staff): from routing link configuration to command flow design, to data validation and optimization. This lightweight solution requires no development and can go live in 15 minutes, helping your team gain instant insights into user satisfaction and continuously improve customer service quality.

Next Steps:

1. Register for a free trial: Visit https://app.tg-staff.com/ to create an account and experience all features for 3 days.
2. Read official docs: At https://docs.tg-staff.com/, dive into advanced uses of routing links and command flows (e.g., conditional branches, variable references).
3. Get help: If you encounter issues during setup, contact support Bot @tgstaff_robot anytime; our tech team will assist you.

Start configuring your first CSAT flow now—user satisfaction often begins with a simple rating button.