Everyone is talking about AI chatbots and all they offer, but you can’t just put one on your website and expect it to be effective. Because it’s so simple to choose a platform and create a chatbot, too many companies jump in without evaluating properly. Even worse, they might not consider how to determine whether their chatbot is effective or where it may need help.
If you implement an AI chatbot, you have to include metrics in your strategy. Otherwise, you’ll have no way to verify that your chatbot delivers on its goals and meets customer needs and expectations. Although every organization will have its unique KPIs to consider, there are some analytics that everyone can use.
Here are eight of the most important metrics for AI chatbot performance.
This is the total number of people who visited your website (or social media page where your bot is installed) and either received the “welcome” message or saw a pop-up for your chatbot. Even if they don’t engage, you can track how many triggers occur or what causes them to ensure that the chatbot is showing up everywhere it should.
Monitoring bot triggers will allow you to refine your chatbot’s pop-up capabilities and occurrences so that more people see your bot and find reasons to use it. That can increase user engagement, as well.
This one is fairly obvious. How many of your users are engaging with your chatbot? This is expressed as a percentage and helps make sure that you have a bot that’s actually useful. For example, if you have 250 visitors and only 10 engage the chatbot, your engagement rate is 2.5%.
You can’t make people use your chatbot, but you can make it more personalized and enticing to encourage engagement. For example, if someone gets to a landing page for a particular product or service, pop up the chatbot with a related insight or suggested question to guide users on how to proceed. If they arrive on a different landing page, you can prompt the chatbot to display a different first message when it pops up, and so forth.
If user engagement is low, you can do further research to see what you can do to change that, as well.
This metric measures how many visitors got to a specific clickable message in the chatbot’s conversation flowchart. If, for example, you’re trying to use the chatbot to get people to convert to a sale, you can make the ultimate message a link to the product page.
Monitoring click-through rates shows you how effective the conversation flow of your AI chatbot is, as well as where there might be issues you can correct to improve this and other metrics.
Monitoring how many chatbot engagements get handed off to live agents is an important metric. This shows you not only the limits of your chatbot but at which point your customers prefer to talk to a real person. After all, an AI chatbot is an assistive tool, not a replacement for real human engagement.
If you notice a lot of similar agent handoffs, you can add a conversation flow to your chatbot to address that particular pain point or issue. It’s also an excellent way to ensure that your chatbot prompts people to speak to someone when necessary.
Dwell time refers to how long people stay on a site, how many pages they visit, and how long they stay on each page because of the chatbot. If they stay longer, they usually get more value from the chatbot than those leaving immediately.
Dwell time can also tell you where there might be bottlenecks or clogs in the chatbot process. This can allow you to optimize things and ensure that people get expedient service, whether from bots or humans.
Bounce rates are an essential metric in several areas of your website and online presence. This metric expresses how many people leave your site within a few seconds of arrival and without seeking further navigation. For example, if someone clicks on a social media link, sees the page load, realizes it’s incorrect, and closes it, that will show as a bounce.
You can also monitor chatbot-specific bounce rates. How many people immediately close out of the chatbot pop-up without sufficient time to even consider engaging? Why do you think this is? Perhaps your chatbot is too intrusive or popping up at the wrong places. In any case, this is information that you can use.
The number of leads you capture through your chatbot is a critical metric. It’s one of the most tangible and most understandable to someone who is a novice regarding AI. Once someone has shared contact information, they become a lead. They can do this through your chatbot when you use it to prompt them to learn more, follow through with another prompt, or even just give their contact information for follow-up in the future.
Monitoring the leads you’ve captured through your chatbot can help you optimize it to capture even more in the future. You can also see which leads and audiences are most likely to respond to the chatbot instead of submitting their information in other ways. And if you aren’t getting enough leads, you can tweak your chatbot to make improvements.
One of the most valuable metrics for your chatbot is the customer satisfaction score. This tells you how many people found the chatbot helpful and what elements they liked best. You can even use common customer satisfaction rating systems to measure how many people are satisfied with your chatbot and other service options.
And what better way to make sure your chatbot is performing than to see how your customers feel about it?
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