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Tech Corner

Conversational analytics part 1: What bot metrics should you track?

Engati Team
Dec 1
3-4 mins

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

Today, we decided to write about metrics for chatbot and its importance in conversational analytics. As we were going through our previous blog about crafting conversational UI for chatbots, we noticed that we had only touched upon the metrics. If you haven’t had a chance to read our previous blog, you can find it here.

How do you measure productivity delivered by a chatbot? After all, there is a reason chatbots are employed in the first place, and every organisation has a unique objective that they want to achieve. There is no doubt that chatbots are going to be a big part of our future.

The funny thing is, not many companies know how to gauge the efficiency of chatbots in the first place. This, perhaps, explains why the chatbot ride has been so bumpy for a few. We say ‘a few’ because some companies have seen tremendous success implementing chatbot technology, while others have fumbled or failed in their attempts. Some chatbots took off easily, and some crashed and burned.

How does one ensure that the chatbot one invests in does not fail to perform?

The same way you ensure anything, you test it, analyse the results, tinker with it and repeat till you meet you benchmark for performance. Which brings us to our topic, how do we measure a chatbot’s efficiency?

A metric is a quantifiable measure that is used to track and assess the status of a specific business process.

Since many of the capabilities of a chatbot can be measured using metrics, it is important that you employ the right metrics to make most of the data. New analytics chatbots can show erratic fluctuations when it comes to metrics. Companies should vigilantly monitor new chatbots after implementation.

Generally, the main points of focus revolve around efficiency enhancement, greater conversion, and faster responses. Defining the right metrics plays an important role. This ensures that you can monitor chatbots success metrics for performance, efficiency, and improvement. When it comes to metrics for measuring bot performance, there are three main classifications that we will be looking at.

  • User Metrics
  • Message metrics
  • Bot metrics

User metrics

These are the metrics that help you index user-related activities. They show how engagement is distributed amongst users.

User Metrics - Engati
User metrics

This is the most fundamental metric. It informs you about the number of people that interact with your chatbot. This metric is essential as its trend showcases the change in the number of users and consequently the amount of data you expose to your chatbot. Also, this provides crucial information about the market size and your chatbot’s overall effectiveness.

Active Users

The number of people that are reading messages from your chatbot within a given time frame are your active users. These users are also your potential target audience. You can estimate your promotional activity impact using these numbers. These numbers show how many people have seen your promotional content. Just like in social media, you cannot guarantee engagement but the content is visible to users.

Engaged Users

These are the users that are exchanging messages with your chatbot; they are in communication with it. This sub-sample enables your chatbot to provide you with conversation statistics. Hence it is an important metric to apply. This metric helps inform your decisions about the effectiveness of your chatbot since a chatbot that cannot start a conversation with users is almost irrelevant.

New Users

This metric will help capture the effectiveness of promotional efforts employed for your chatbot. New users keep the pool of active users fresh; older users tend to lose interest in interaction over time. Hence, you can make promotional decisions for the chatbot informed by this metric.


Once you have an understanding of how to make informed decisions by using the right user metrics, you can layout strategies for your chatbot to be more effective in engagement. However, there are some more metrics that are crucial for making a chatbot more productive. We will be discussing message metrics and bot metrics in our next blog. Stay tuned for more information.

Meanwhile, if you want to experience a chatbot platform that saves you a lot of time and trouble and provides a smooth interface for building your dream bot, check us out!


Engati Team

At the forefront for digital customer experience, Engati helps you reimagine the customer journey through engagement-first solutions, spanning automation and live chat.

Andy is the Co-Founder and CIO of SwissCognitive - The Global AI Hub. He’s also the President of the Swiss IT Leadership Forum.

Andy is a digital enterprise leader and is transforming business strategies keeping the best interests of shareholders, customers, and employees in mind.

Follow him for your daily dose of AI news and thoughts on using AI to improve your business.

Catch our interview with Andy on AI in daily life

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