14 Key Metrics to Track for Better Chatbot Performance

Chatbots are quickly becoming essential tools for businesses, boosting user engagement, increasing sales, and making customer service more efficient in general. But having a chatbot isn’t enough anymore; you need to know if it’s doing its job properly.

That’s where tracking performance metrics comes in. By keeping an eye on the raw data, you ensure that your chatbot isn’t just functioning but thriving.

Read our guide to dive into the 14 key metrics you can monitor to get the most out of your chatbot. Doing this will help you get a closer look at how well it performs and whether it’s actually contributing to your business goals.

Here’s why it’s important to keep a close eye on how your chatbot is performing by tracking key metrics.

Your chatbot should be working in line with your overall business goals – otherwise, what’s the point? Whether you’re aiming to boost sales, generate more leads, or offer your customers top-notch support, your chatbot should make a difference.

By tracking metrics like ROI (return on investment) and cost per conversation, you can directly see how your chatbot is helping (or hindering) your business objectives.

Building a chatbot that’s quick, accurate, and easy to interact with keeps users happy and coming back to your business.

Monitoring metrics like customer satisfaction score (CSAT) and average response time can help you fine-tune your user’s experience, making your chatbot more than just functional.

Even the best chatbots have room for improvement. By monitoring your performance data, you can uncover where your chatbot is falling short. If you see poor metrics like a high error rate to a low conversion rate, it might be time to accept defeat and go back to the drawing board.

A chatbot is only as good as the knowledge it has. It’s your responsibility to give this tech lesson in your business so that it can serve your customers well without a human rep.

Metrics like keyword usage and error rates will help to show you how well your bot understands and responds to questions asked by your customers.

If you notice that certain keywords are causing problems, it might be time to update your chatbot or tweak its natural language processing (NLP) methods.

Now you know why it’s essential to track the performance of your chatbot, you’re probably wondering which key metrics you should use. Below are 14 options to choose from. Each of these will give you a way to improve how it performs – now and in the future.

Average session length is all about how long users interact with your chatbot. This metric can give you a better insight into user engagement and the complexity of tasks your chatbot is handling.

If users are spending a lot of time with your chatbot, it could mean they’re finding it helpful and engaging – or it could mean your bot is taking too long to solve their problems. On the flip side, shorter sessions might show that users are quickly leaving, possibly because the chatbot isn’t performing as well as they hoped.

To optimize your session length, make sure your chatbot is designed to handle common questions your business receives quickly and efficiently. A smooth conversation flow can also help users get what they need without any unnecessary back-and-forth.

Conversation rate measures how many users actually engage in a meaningful conversation with your chatbot compared to those who visit but don’t even interact.

A high conversation rate means users find your chatbot approachable and helpful. If the rate is low, it could be a sign that users aren’t interested or don’t see the value in engaging with your business’s chatbot.

Boosting conversation rates can be as simple as making your chatbot more visible and ensuring its greeting message pops up. Also, make sure it’s clear to users how the chatbot can help them.

The abandonment rate tells you how many users start a conversation with your chatbot but don’t finish it. A high abandonment rate could indicate user frustration.

If users are abandoning conversations, it might mean your chatbot isn’t meeting their needs or is too hard to use. This can lead to poor customer satisfaction and diminish the perceived value of your chatbot.

To lower abandonment rates, look at where users are dropping off. It could be due to slow response times, irrelevant answers, or a confusing interface. Refining your chatbot’s conversation design and improving response accuracy can help keep users engaged.

The first response time is the time it takes for your chatbot to respond to a user’s initial query. This is a crucial metric because nobody likes waiting around for an answer.

In a world where people expect instant responses, your chatbot’s first response time is key to keeping users happy. Faster responses usually lead to higher satisfaction and a better overall experience.

To speed up first response times, ensure your chatbot is hosted on a reliable server that can handle traffic without delays. Also, optimizing the bot’s NLP engine to quickly process and respond to queries can make a big difference.

Average response time measures how quickly your chatbot responds throughout the entire conversation, not just the first interaction.

Keeping response times fast throughout a conversation helps maintain the flow and keeps users engaged. If the average response time is too slow, users might lose patience and abandon the conversation, which could lead to a higher abandonment rate.

Improving average response time involves making sure your chatbot is running efficiently, minimizing the time it takes to fetch data or complete actions. This could mean upgrading the chatbot’s infrastructure.

CSAT measures how satisfied users are with their chatbot experience. It’s usually collected through a quick survey after the interaction.

High CSAT scores mean your chatbot is doing its job well and meeting user expectations. Low scores, on the other hand, indicate there are issues that need addressing, whether they’re related to response speed, accuracy, or data safety.

Chatbots need to be secure for the safety of your clients, especially if you’re in the finance sector. Check that your chatbot meets data compliance standards to keep your customers satisfied with your service.

To boost CSAT scores, consider personalizing interactions more and ensuring your chatbot accurately understands and responds to user queries. Regular updates to the chatbot’s knowledge base can also help keep it sharp and satisfying for users.

NPS gauges how likely users are to recommend your chatbot to others. While it’s traditionally used to measure customer loyalty, it’s also a great way to see how your chatbot is performing.

A high NPS shows that users are happy with their experience and see the chatbot as a valuable tool. A low NPS suggests that the chatbot might be causing frustration or isn’t delivering on its promises.

Tracking your NPS is easy if you integrate a VoIP phone system with your chatbot. It will collect data for you, including call duration and wait times. What is a VoIP phone system? It’s a tool that allows users to make voice calls and lets you track data.

To improve NPS, focus on delivering top-notch service through your chatbot. This means providing accurate, timely responses and effectively resolving user issues without needing to pass them on to a human agent.

Human handover rate is the frequency at which your chatbot passes the conversation over to a human agent. While some queries will naturally need human intervention, a high handover rate might indicate that your chatbot isn’t handling basic questions as well as it should.

A high handover rate can drive up operational costs and suggest that your chatbot needs improvement in handling common queries. On the other hand, if the handover rate is too low, your chatbot might be trying to handle too much, leading to user frustration.

Try tracking what calls are regarding or whether customers are coming from your chatbot by analyzing call center data.

Then, to optimize the human handover rate, focus on training your chatbot to better handle common and simple queries. This could involve improving its understanding of user intents and expanding its knowledge base to cover more scenarios.

Error rate measures how often your chatbot fails to understand or properly respond to a user’s input. This could include responses like, “I’m sorry, I didn’t get that,” or providing incorrect information.

A high error rate can frustrate users and lead to a poor overall experience. It may also increase the abandonment rate and the frequency of human handovers, both of which can raise operational costs.

Reducing the error rate often involves refining your chatbot’s NLP capabilities and expanding its knowledge base. On top of this, it’s a good idea to make your other channels of communication easily available. Whether you use a virtual telephone number or a live chat, it’s a good idea to make these services easy to find as an alternative to your chatbot and to avoid any frustration.

Keyword usage tracks which words or phrases users commonly use when interacting with your chatbot. This data can help you understand what users are looking for and how they’re phrasing their requests.

Understanding keyword usage helps you optimize your chatbot’s responses and make sure it’s addressing the most common queries effectively. It can also reveal trends in user behavior that might guide future improvements.

To optimize keyword usage, regularly analyze this data to ensure your chatbot is equipped to handle the most frequent requests. Updating the chatbot’s knowledge base and training it on new keywords or phrases can improve its accuracy and relevance.

Peak usage times indicate when your chatbot is most active. This could be during specific hours of the day, certain days of the week, or in response to particular events or campaigns.

Knowing when your chatbot is busiest helps you plan for those times, ensuring it can handle the volume without compromising performance. It can also provide insights into user behavior and preferences, helping you tailor your chatbot’s responses more effectively.

To better manage peak usage times, consider scaling up resources during those periods, such as increasing server capacity or optimizing the chatbot’s backend to handle more simultaneous conversations. Additionally, you might want to schedule targeted campaigns or updates around these peak times to maximize engagement.

Integrating scheduling software can enhance the efficiency of deploying updates or managing resources, ensuring that your chatbot is always ready to handle high traffic without delays.

Understanding user demographics is like having a secret map to your audience. By knowing who is interacting with your chatbot – their age, location, or interests – you can create a more personalized experience that resonates with them.

For example, if you discover that a significant portion of your users are young adults, you might choose a more casual tone or incorporate trendy language that appeals to them.

On the other hand, if your user base consists mainly of professionals, a more formal approach may be appropriate. Tailoring your chatbot to fit the unique characteristics of your audience can lead to higher engagement and satisfaction, making users feel like your chatbot truly understands their needs.

Cost per conversation is a vital metric that tells you how much it costs your business to engage each user through the chatbot.

Think of it as measuring the efficiency of your chatbot as a customer service tool. If you find that your cost per conversation is on the higher side, it might be time to dig deeper and analyze where those costs are coming from.

Are there too many handovers to human agents? Is the chatbot spending too much time on each interaction? By keeping tabs on this metric, you can identify areas where you can streamline processes, reduce operational costs, and ultimately improve your chatbot’s effectiveness in serving users while keeping expenses in check.

Return on investment (ROI) is the gold standard for evaluating the effectiveness of your chatbot. This metric helps you understand the financial return your chatbot delivers in comparison to the costs associated with developing and maintaining it.

A positive ROI means your chatbot is a valuable asset, increasing sales and enhancing user satisfaction. On the flip side, a negative ROI signals that you might need to re-evaluate your strategy.

By regularly assessing your chatbot’s ROI, you can make informed decisions about where to invest resources, whether it’s improving the bot’s capabilities, expanding its knowledge base, or enhancing its user interface.

Tracking the right metrics is essential for ensuring your chatbot is performing at its best. By monitoring key performance indicators, you gain valuable insights into how well your chatbot meets user needs and contributes to your business goals.

While this article has provided an overview of the 14 key metrics you should be tracking, it’s important to note that not all platforms track every metric. Tools like Zendesk Answer Bot and Intercom Resolution Bot can help monitor important metrics such as response time, customer satisfaction (CSAT), human handover rates, and abandonment rates. For other metrics like keyword usage, cost per conversation, or return on investment (ROI), third-party analytics platforms or custom-built solutions may be required.

Now, for a more detailed look at how you can use some of these metrics and build a personalized dashboard, take a look at our AI chatbot metrics below and head over to our multi-channel AI chatbot page features for more information:

• Lifetime Conversations: Lifetime number of conversations across all chatbot channels.
• Lifetime Unique Users: Lifetime number of unique users across all chatbot channels.
• Lifetime Training Cost ($)
• Lifetime Conversations Cost ($)
• Total Conversations: Total number of conversations across all chatbot channels.
• Total Unique Users: Total number of unique users across all chatbot channels.

As chatbots continue to evolve, staying informed about these metrics will empower you to make data-driven decisions, ultimately leading to a more effective and engaging chatbot solution.

About the Author

J.P. Walti is Vice-President of Marketing, Creative, and Web at RingCentral, an AI-powered communications software provider. He has two decades’ worth of experience in the marketing and creative fields. Here is his LinkedIn.