Introduction

A/B testing is a method used to compare two versions of a product or service to determine which one performs better.

In the context of bots, A/B testing involves comparing two different versions of a bot's workflow to assess which version leads to better outcomes, such as higher engagement, conversion rates, or user satisfaction.

A/B testing involves dividing users into two groups and exposing each group to a different version of the bot. One group receives the original version of the bot, while the other group receives a modified version. By comparing the performance of the two versions, you can determine which version is more effective in achieving your goals.

It is crucial for optimizing bot performance and user experience. It allows you to make data-driven decisions by testing hypotheses and identifying which changes lead to better outcomes. By understanding how different variations impact user behavior, you can iterate and improve the bot over time, leading to increased engagement, conversions, and overall effectiveness.

Before You Begin

• Before delving into the concept of A/B testing and its role in defining bot implementation strategy, it is crucial to have an understanding of the following points:

  • Issue Types

  • Configure Bot Workflows

• Also, it's crucial to identify the specific workflow you want to test and clearly define the objectives of your test. This might involve comparing different versions of a bot, such as a Generative AI Bot versus a Traditional Bot flow. For example, you might want to assess which bot version leads to higher engagement, conversion rates, or user satisfaction.

Steps to Enable A/B Testing

1. Enable A/B Testing: Within the Deployment Settings, you have the option to enable A/B testing for qualifying and issue type bots.

2. Select Workflows: Choose two dialogue tree workflows that you want to compare. These workflows should have variations in their design, structure, or content to test different approaches.

   

3. Set Percentage Split: Specify the percentage split between the two workflows. For example, if you select a bot with a 20% split, this means that this bot will be deployed in 2 out of every 10 cases, while the other bot will be deployed in the remaining 8 cases.

4. Run Experiments: Once deployed, the system will randomly assign users to one of the two workflows based on the specified percentage split. Users interacting with the bot will be routed to either version A or version B of the workflow.

5. Analyze Results: Monitor and analyze the performance metrics of each workflow, such as user engagement, completion rates, and satisfaction scores. Compare the results to determine which version performs better based on your predefined goals and metrics.

Points to Remember

• To accurately filter the results of A/B testing in reporting, it's crucial to update case-level custom fields within each individual Dialogue Tree (DT). These custom fields serve as identifiers that distinguish between different versions or variations of the bot. By updating these custom fields, you can ensure that each case is tagged correctly with the corresponding version it was exposed to during the A/B test. This enables you to track and analyze performance metrics accurately, such as engagement rates, conversion rates, or user satisfaction, for each version of the bot.