Today chatbots are increasingly being used by businesses to facilitate outstanding customer experiences. An AI chatbot is the product of advanced technology and is rapidly proving itself as the future of marketing. However, chatbot development and deployment aren’t enough. It is imperative to monitor the performance of the bot using specific metrics.

Recent research by Botanalytics found that about 40 percent of people interacting with an AI chatbot engage in only one conversation. Hence, there is a need to optimize the chatbot to deliver a quality user experience. If we track these chatbots’ performance using specific metrics, we see several vulnerabilities that they are prone to.

Traditional metrics cannot be used for measuring a chatbot’s performance due to their conversational nature. Bot analytics companies have come up with certain metrics that are suitable to measure their performance. Given below are the five most useful AI chatbot metrics that will provide you insightful information about your bot’s working. Using them, you can discover the areas that need further improvement and invest in more efficient chatbot development.

Number of users

This metric enables you to know how popular your AI chatbot is proving to be among your users. The unpopularity of your chatbot or a lack of user engagement with it can boil down to several factors. One could be that your bot is deployed in a platform that is characterized by low user activity. A lack of awareness amongst people regarding the bot’s presence can also be a factor behind the low user interaction.

This problem can be solved by deploying your bot on a popular messaging platform. Besides, you can generate awareness of your chatbot by demonstrating its usage through suitable campaigns.

Active and engaging sessions

A business can hope to progress only when the user engagement with its services show signs of growth. Active and engaged sessions metric enables them to find out the percentage of active and engaged users with their AI chatbot. An ‘active’ session is characterized by users reading the message. On the other hand, when the user gives a response in the form of a message, the session is termed as ‘engaged.’ Active and engaged rates refer to the number of active and engaged sessions of a particular user out of their total number of sessions respectively.

The chatbot development company Machaao leveraged this metric to optimize the number of active and engaged sessions of users with its bot. They did so by examining the interactions of the most active and engaged users. It enabled them to determine the people’s expectations regarding bot behavior and modify their bot according to the user requirement.

Retention metric

The retention metric helps you find the number of users who have repeatedly engaged with your bot. According to the chatbot analytics data, a high retention rate signals success. A good way to increase customer retention is by undertaking promotional campaigns around the usage of your bot. Campaigns like offering users a discount on interacting with the bot can dramatically enhance the retention rates.

Keep in mind that retention rates should be optimized through an organic process. The most useful way to do it is by providing high-quality and meaningful chatbot services that are relevant to user requirements.

Confusion triggers

Sometimes, even an AI chatbot may not be able to understand what the customer is asking. The confusion trigger metric indicates the places where chatbot development can be improved. Triggers are of various types. For example, the chatbot fails to understand a response, or the user sends more than one message that are beyond the bot’s comprehension. Every trigger reveals something about a chatbot’s performance.

The confusion rate can be measured as the number of times the chatbot fell back out of the total number of messages it received. There is a need to examine the frequency of such instances of confusion and take steps to minimize their occurrence in the future.

Conversation steps

According to Botanalytics, a long conversation with the bot does not necessarily imply a great user engagement. Conversation steps refer to the messages exchanged between the user and the bot. These exchanges are counted until the completion of the goal. Ideally, the conversational steps should be minimal. It means that the user should be able to resolve the issue within a short time frame.

Chatbot app development company expert needs to know the average number of conversation steps. There is a difference between minimal conversation steps and very few conversation steps. The former indicates that the user got his/her answer quickly in a short duration. The latter suggests that the user gave up midway. During the chatbot development process, focus on critical and the most relevant conversation steps to help the users resolve their queries immediately without any hassle.

Conclusion

The chatbot metrics mentioned above help developers to accomplish the primary aim of a chatbot, that is, providing the best possible experience to the users. Using suitable metrics to monitor your bot’s performance can prove to be an incredibly insightful experience. They will also enhance the bot’s usability, thus enabling businesses to expand their reach and tap into a new audience base.

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