The Conversational AI Testing Checklist:

Tools, Techniques, and Metrics to Include in your testing Strategy

Chatbot use cases

With no available standard Conversational AI agent testing method, businesses face the dilemma of how can they ensure the chatbot is error-free and user-engaging? How should performance testing tools be used? What are the most effective mechanisms for testing its functionality? Therefore, a chatbot testing checklist below contains tools, ground rules, best practices, techniques, and critical considerations to help businesses set a standardized testing plan.

1. Test Your Bot’s Conversational Flow

Engage the chatbot in a conversation. Start with the broad, user-greeting questions and critical use cases or chatbot testing scenarios. The list of questions, at this stage of the chatbot testing process, should include:

  • Does the chatbot understand user questions?
  • Does it respond promptly to them?
  • Are its responses accurate and relevant?
  • Are there sufficient conversation steps
  • Does it keep the user engaged?

2. Include Developer Testing

The developers working on the chatbot should test it at each development phase.

The purpose of developer testing is to verify and validate the chatbot development and confirm whether the chatbot provides accurate and relevant answers to user queries. 

4. Run a Chatbot-Error Handling Test

While building a chatbot testing strategy, businesses should program the chatbot to reply coherently if a user enters a meaningless sentence or a not so commonly used expression. Businesses cannot anticipate the irrelevant information that users might enter. However, developers should program the chatbot with emergency replies for the exceptions expected by the company. 

5. Chatbot Testing Tools to Consider

A shortlist of 3 tools for streamlining the testing efforts is mentioned below - 

1) Chatbot test

An open-source guide with 120 questions for assessing the user experience delivered by the chatbot. It operates at three levels:

  • possible chatbot testing scenarios
  • expected scenarios
  • almost impossible scenarios

It provides 7 different metrics for evaluating the bot performance:

  • Understanding: does the chatbot understand different kinds of user input such as curse words, small talk, idioms, and emojis?
  • Answering: are the answers context-relevant and accurate 
  • Navigation: is it intuitive enough for the company to go through the conversation users have with the bot?
  • Personality: Does the chatbot's tone suit the audience and the nature of the ongoing conversation?
  • Onboarding: is the chatbot functionality apparent to the user 
  • Intelligence: does the chatbot remember the user's details and key information throughout the conversation?
  • Error management: how does the chatbot handle errors and exceptions?

2) Bot analytics

From usability to conversational flow to the delivered user experience, this custom service enables businesses to test the critical aspects of the chatbot.

3) Dimon

This chatbot testing tool integrates seamlessly with major platforms like Telegram, Slack, WeChat, and Facebook Messenger. Businesses can use it to detect any issues in the conversational flow and the user experience that the bot provides.

The way forward: Selecting the right and best conversational AI

While it’s true that adopting conversational chatbots has many advantages, including a positive impact on business revenues, it does require a one-time investment. However, in the long run, it offers a high ROI. Thus businesses need to evaluate their requirements before selecting a conversational AI provider as it is a long-term commitment. Deploying the right chatbot leads to increased customer satisfaction, and improvement in agent productivity, scale, and efficiency in handling customer queries, after implementing our chatbot for customer support. 

Wrapping Up 

We hope this article will help businesses select the right chatbot provider among the myriad of chatbot companies currently operating in the industry.

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