How to use A/B testing to optimize Telegram’s welcome process: improve consultation conversion rate and user retention

Your Telegram Bot welcome message is the first impression on users. A well-designed welcome process can let users understand “what problem this Bot can help me solve” within 3 seconds, and prompt them to initiate consultation or complete preset operations. On the contrary, a welcome interface that is lengthy, confusing, or has too many options may cause users to leave directly or even block the bot.

Many operations teams intuitively design a welcome message and then hope it works. But a more reliable way is: verify with A/B testing. This article will fully explain how to A/B test the Telegram welcome process, from setting goals and designing versions to analyzing data and iterating, helping you use a data-driven approach to improve consultation conversion rates and user retention.

Why Telegram welcome process directly affects conversion and retention

The welcome flow is the starting point of the user journey. In Telegram, the /start reply that users see when they open the Bot for the first time is your “golden 3 seconds”. It needs to complete three tasks:

• Build trust: Tell users who you are and what value you can provide.
• Reduce cognitive load: Use clear and concise menus or buttons to guide the next step, preventing users from thinking “what should I order?”
• Create action hook: Make users willing to complete an action (such as clicking a button, entering keywords) to enter the subsequent process.

If the welcome process is poorly designed, such as giving 10 buttons at once, the text is too long and requires scrolling to read, or the reply content does not match the user’s expectations, the user is likely to exit directly. This is where the value of A/B testing comes in – you can compare two different welcomes or menu structures to find out which version leads to higher inquiry initiation rates and lower churn rates.

Set test goals: from consultation conversion rate to retention indicators

Before you start building the process, figure out what you want to test. Blind A/B testing not only wastes time but can also lead to misleading conclusions.

Distinguish between short-term conversion and long-term retention

A/B testing needs to distinguish between two dimensions of goals:

• Short-term conversion: Did the user complete the action you expected during the welcome process? For example, click the “Consult Customer Service” button, fill out a form, or complete an instruction interaction. Such indicators can provide quick feedback and are suitable as the basis for the first round of testing.
• Long-Term Retention: After the first interaction, do users come back to the bot again the next day or a week later? An interactive welcome process may have a low short-term conversion rate (because there are many steps), but if it allows users to have a deeper understanding of the Bot’s functions, subsequent retention will be higher.

Recommendation: Prioritize short-term conversions in the first round of testing, because samples are easier to collect in the short term. After a basic version is determined, it can be optimized and retained through subsequent testing.

Define quantifiable success criteria

You need a specific number to determine victory or defeat. For example:

• “The consultation initiation rate of version B is more than 10% higher than that of version A, and the confidence level reaches 95%”
• “The next-day user retention rate of version B is no less than 95% of that of version A”

At the same time, set the test period and minimum sample size. In general, at least 500–1000 valid interactions (i.e., users see the complete welcome flow) per version are required to achieve statistical significance. If the bot has low daily activity, it may take 1–2 weeks to run.

Design two welcome flow versions for A/B testing

The core principle of A/B testing is: Change only one variable at a time. If you modify the welcome message, number of buttons, background color, and sending timing at the same time, you will not be able to tell which change caused the data change.

The following two version examples only adjust the interactive structure of the welcome message, while other elements (such as brand tone, sending time, reply style) remain unchanged.

Version A: Simple and efficient welcome process

• Welcome: One sentence to introduce yourself + a clear value proposition. For example:

  “Welcome to [product name]! I am your customer service assistant. Click the button below to contact live customer service or view frequently asked questions immediately.”

• Menu Buttons: Only 2-3 core buttons are provided, such as “Consult Customer Service”, “Learn About Products” and “FAQ”.
• Logic: After the user clicks any button, he or she will directly enter the corresponding conversation or page without additional intermediate steps.

Applicable scenarios: The user has a clear purpose and hopes to solve the problem quickly; or the Bot has a single function (such as after-sales support).

Version B: Guided interactive welcome flow

• Welcome: greeting + an open-ended question to guide the user to choose a scenario. For example:

  “Hello! What do you want to know today? A. Product purchase issues B. After-sales service C. Others”

• Menu Button: Display the scene classification button first. After the user selects, specific submenus or instructions are displayed according to the classification.
• Logic: Multi-step interaction, the user needs to click at least twice to reach the final goal.

Applicable scenarios: Bot has complex functions (such as supporting pre-sales, after-sales, and order inquiries at the same time), or you want to collect user intent data for subsequent operations.

Note: Version B has a higher risk of churn - users may give up on the first step. But its advantage is that users who are willing to complete multi-step interactions tend to have stronger intentions and may have higher quality subsequent conversions.

Use visual tools to build and dispatch test processes

Manually writing code to allocate traffic and record version data is a high threshold for most operations teams. A more practical approach is: Use a Bot that supports A/B testing to build a platform, configure two processes through the visual editor, and set traffic distribution rules.

For example, TG-Staff’s visual command process editor allows you to build two independent welcome process nodes by dragging and dropping. You can create two versions in the Bot settings (for example, named “Welcome flow v1” and “Welcome flow v2”), and then randomly distribute traffic to the two versions 50/50.

Specific steps:

1. Open the “Command Process” module in the console, create a new process, and name it “Welcome Process-A”.
2. Drag the “Send Message” node and fill in the welcome message and button settings of version A.
3. Copy this process, modify it to “Welcome Process-B”, and adjust the node logic according to the design of version B.
4. In the “Welcome Settings” of the Bot, enable the A/B test mode, designate the two processes as version A and version B respectively, and set the distribution ratio to 1:1.
5. Start the test. The system automatically randomly assigns a version to each new user and records subsequent behavioral data.

Collect and compare test data: conversion and retention analysis

After the test has been running for 7–14 days (depending on traffic size), you can start analyzing the data. You need to focus on the following indicators:

Metrics	Version A (concise)	Version B (guided interactive)	Differences
Total exposure	1200	1180	—
Number of people who clicked “Consult Customer Service”	240	180	A wins
Consultation initiation rate	20%	15.3%	+4.7%
Number of users returning the next day	60	72	B wins
Next day retention rate	5%	6.1%	+1.1%

The table above shows a typical scenario: version A has a higher short-term conversion rate (users are more willing to consult directly), but version B has slightly better retention performance (users who have completed multi-step interactions are more familiar with the Bot).

To determine which version wins, you need to go back to the success criteria you originally set. If the primary goal is to increase inquiry conversion rates, version A wins. If the goal is to balance conversion and retention, you need to calculate “cost per consultation” and “user lifetime value” - maybe version B has a lower conversion rate, but the long-term value brought by retained users is higher.

Iterate the welcome process based on test results

A/B testing is not the end point, but the starting point for continuous optimization. Based on the first round of data, you can enter an iterative closed loop:

1. Select the winning version: Based on the target indicators, determine a version as the new control group.
2. Design version C: Analyze the advantages and disadvantages of the two versions and try to combine the advantages of each. For example, retain the simple button structure of version A, but add the scene guidance of version B in the welcome message (such as “Have questions? Contact customer service directly; Want to learn about the product? Click the button below”).
3. Start the second round of testing: Compare version C with the current winning version to verify whether the new combination is better.
4. Repeat the above steps: Only change one variable in each round of testing (such as button text color, length of welcome message, whether to use emoji), and gradually approach the optimal solution.

Frequently Asked Questions and Notes

• **What should I do if the sample size is insufficient? ** If you have few daily active users, consider extending the test period, or lowering the requirement for statistical significance (e.g. accepting 80% confidence instead of 95%). You can also combine the traffic of multiple bots for testing (if they target similar user groups).
• **How ​​long is the appropriate testing time? ** Cover at least one complete user activity cycle. For example, if the bot’s primary users are active during weekdays, the test should include at least 5 weekdays + 2 weekend days to eliminate time bias.
• **How ​​to avoid variable confusion? ** Make sure that the two versions only differ in the structure of the welcome message. If version B uses a different send time (e.g. a 2-second delay), that’s not an A/B test, but a test of send timing.
• **What should I do if the user triggers the welcome process repeatedly? ** Only conduct A/B testing on new users who visit for the first time. Returning users should always see the same flow, otherwise it will interfere with retention calculations.

Summary and next steps

A/B testing is not a marketing term, but a practical tool for improving Telegram Bot user experience. By comparing conversion rates and retention data for different welcome processes, you can say goodbye to “I think this is better” guesswork and use real data to drive decisions.

You can do it now:

1. Log in to TG-Staff Application Console, register for a free trial (3 days), and use the visual process editor to build two versions of the welcome process.
2. Refer to the method in this article, set up traffic distribution rules, and start your first Telegram welcome process test.
3. After the test is completed, the winning version is selected based on the data and enters the next round of iteration.

Need more help? Check out TG-Staff official documentation for process design tips, or directly contact customer service Bot @tgstaff_robot for practical experience. Start optimizing your Telegram conversion rates with A/B testing today.