TG Bot Auto Reply Keywords & Command Menu Design Guide: Reduce Repetitive Inquiries, Improve Conversion Quality

If you’re running a Telegram Bot and drowning in repetitive questions every day—“How much?” “How to refund?” “Where’s the link?”—your customer support team is likely overwhelmed by low-value conversations. Worse, when a real customer who needs human assistance appears, your agents are still manually typing “Please wait, let me check.” This isn’t a tool issue; it’s a poorly designed auto-reply strategy.

This article will systematically walk you through building a tg bot auto reply keyword dictionary, designing command menus, configuring routing rules, and providing a ready-to-use checklist. Whether you use TG-Staff or other platforms, this methodology is immediately applicable.

Why Your TG Bot Needs Smarter Auto-Reply Design

A common mistake is thinking “once auto-reply is set, the problem is solved.” The reality often looks like this:

• Incomplete keyword coverage: A user saying “how much” triggers a response, but “what’s the fee” goes straight to a human agent, who ends up handling many similar queries daily.
• Messy command menu: /help returns a long wall of text, users can’t find the option they need and keep typing “human” hoping for help.
• Poor escalation quality: Auto-reply has no fallback, users repeatedly type “transfer to human” before getting connected, wasting agent time on classifying the issue.

The core solution involves two things: keyword matching precision and command menu guidance efficiency. The former reduces repetitive inquiries, the latter improves conversion quality.

Step 1: Identify Frequent Repetitive Queries and Build a Keyword Library

Don’t rely on intuition to write keywords. Extract real questions from historical conversations, FAQ pages, and customer support team feedback. Follow three steps:

1. Export the last 30 days of support chat logs and mark the top 10 most frequently repeated questions.
2. Categorize the questions, e.g., price-related, feature-related, account-related, after-sales.
3. Extract core keywords for each category, including synonyms and common misspellings.

Keyword Categorization: General vs Business-Specific

Type	Examples	Matching Strategy
General high-frequency	price, fee, how much, refund, link, help	Exact match + fuzzy match (e.g., “price” matches “price list”, “how much”)
Business-specific	node, API Key, whitelist, commission, staking	Exact match or regex to avoid false positives
Emotional/urgent	complaint, urgent, outage, unreachable	Priority match, escalate directly or send emergency channel

Set Priority and Conflict Resolution Rules

When a user message matches multiple keywords, define clear priority rules. For example:

• Priority from high to low: urgent > business-specific > general high-frequency
• Same priority: match the earliest created keyword

Step 2: Build a Bot Interaction Entry Point with a Visual Command Menu

The command menu (e.g., /start, /help, /price) is the first screen users see when entering the bot. A well-designed menu allows users to find answers on their own; a poorly designed one forces them to type “human”.

Command Menu Structure Design: From Welcome Message to Branching Options

A typical efficient menu structure:

/start → 欢迎语 + 三个主要按钮
         ├── 📖 产品介绍 → 图文/说明
         ├── 💰 价格查询 → 套餐预览 + 订阅链接
         └── 👤 我的账号 → 绑定/查询状态

/help → 常见问题分类
        ├── 付款问题 → 退款/发票/支付方式
        ├── 技术问题 → API/节点/连接
        └── 联系客服 → 转人工按钮

Key principle: Each branch should have no more than 3 options, with clear option labels (e.g., ”💰 Price Inquiry” instead of “Service Information”).

Zero-Code Drag-and-Drop Editor Workflow

Using TG-Staff’s visual command flow editor as an example, building a simple “Price Inquiry” flow takes only three steps:

1. Create Node: Drag in a “Message Reception” node and set the trigger word /price or “price”.
2. Configure Reply: Edit the reply content within the node (supports text, images, buttons), e.g., “We offer three plans: Standard 8.99/month, Pro16.99/month. Click the button below for details.” and add a “View Plans” button linked to the official website.
3. Connect Diversion: If the user continues to type “transfer to human”, connect the node to an “Agent Assignment” node to automatically create a session.

For more editor tips, refer to TG-Staff Visual Flow Documentation.

Step 3: Configure Auto-Reply Rules and Human Transfer Diversion

After setting up keywords and menus, you need to configure specific auto-reply trigger rules and diversion strategies for unmatched queries.

Auto-Reply Trigger Conditions and Reply Content Types

Most platforms support the following matching methods:

• Contains Match: Triggered when the message contains the keyword (e.g., “refund” matches “I want a refund”)
• Starts-With Match: Triggered when the message starts with the keyword (e.g., “price” matches “How much is the price”)
• Exact Match: Triggered when the message exactly equals the keyword (e.g., “human”)
• Regular Expression: Advanced matching, e.g., \b(价格|费用|多少钱)\b

Reply content is not limited to text. You can configure:

• Rich Media Messages: Product screenshots + explanatory text
• Buttons: Guide users to click and jump (e.g., a “Subscribe Now” button linked to the payment page)
• Variable Insertion: e.g., {user_first_name} 您好，您咨询的套餐价格如下： to enhance personalization

Session Diversion Rules: Reduce Ineffective Human Handling

When auto-reply cannot resolve the issue, the session needs to be transferred to a human agent. TG-Staff offers two diversion rules:

• Round-Robin Assignment: Agents are polled in order, suitable for stable agent numbers and balanced workloads.
• Online-First Assignment: Prioritize online agents; fall back to round-robin when all are offline, suitable for fluctuating agent numbers or peak hours.

With Diversion Links, you can create a complete conversion chain: Ads/Social Media → Diversion Link → Bot Auto-Reply → Human Agent Handling. Diversion links automatically capture visitor IP, browser information, and URL parameters for subsequent attribution analysis.

Step 4: Testing and Optimizing Your Auto-Reply Flow

After configuration, don’t go live immediately. First, run three rounds of tests:

1. Simulate user conversations: Use multiple accounts to send the same question phrased differently (e.g., “What’s the price?”, “How much does it cost?”, “What are your fees?”) and check if all trigger the correct reply.
2. Check match logs: Most platforms provide keyword match logs. Review which messages were not matched and add missing words.
3. Collect agent feedback: In the first week after launch, have agents mark conversations that “could have been auto-replied but were transferred to human,” and analyze the reasons.

Iteration methods:

• A/B test reply copy: Set two replies for the same keyword and see which one results in fewer follow-up questions from users (i.e., higher resolution rate).
• Adjust keyword weights: If a keyword triggers frequently but users still transfer to human, the reply content may be insufficient. Optimize the reply rather than adding more keywords.

TG Bot Auto-Reply Checklist

Before going live, verify each of the following metrics:

• Keyword coverage: Do top 10 frequent questions have corresponding keywords covering common synonyms?
• Reply timeliness: Do all auto-replies return within 1 second (to avoid users waiting and sending again)?
• Human transfer rate: Is the rate of conversations needing human transfer after auto-reply below 30% (baseline, adjust based on business)?
• Command menu clarity: Can new users find the answer or human transfer entry within 3 clicks?
• Fallback mechanism: Does every auto-reply include a “transfer to human” or “contact support” prompt?
• Logging: Is keyword match logging enabled for future analysis?

Frequently Asked Questions

Q: Can TG bot auto-reply recognize Chinese keywords?
A: Yes. Major Telegram Bot management platforms (e.g., TG-Staff) support Chinese keyword matching, including exact match, fuzzy match, and regex, suitable for Chinese customer service scenarios.

Q: What if auto-reply keywords conflict with command menus?
A: We recommend setting priority in the command flow, such as making the /start command take precedence over keyword matching. Most platforms allow custom rule ordering to ensure key commands aren’t blocked by keywords.

Q: How to evaluate auto-reply effectiveness?
A: Focus on three metrics: ① Auto-reply hit rate (proportion of inquiries matched by keywords); ② Human transfer rate (proportion of conversations requiring human after auto-reply); ③ User satisfaction (e.g., post-conversation ratings). Review and adjust weekly.

Q: Can auto-reply differentiate user groups?
A: Some platforms support different auto-reply rules based on user profiles (e.g., language, source channel), but this requires a professional plan. Basic plans typically provide uniform replies to all users.

Q: How many auto-reply keywords does the free plan allow?
A: Depends on platform limits. For TG-Staff, the free 3-day trial gives access to all features; the standard plan supports full keyword and command flow configuration. See official pricing page for details.

---

Next Steps:

• Sign up for TG-Staff free trial to experience visual command flow and auto-reply configuration: https://app.tg-staff.com/
• Check official documentation for keyword matching rules and routing settings: https://docs.tg-staff.com/
• Contact the support bot for one-on-one guidance: @tgstaff_robot