An AI chatbot adds lots of value to your business. It automates customer support, generates and nurtures leads, and contributes towards revenue growth. But then again, adding conversational AI is not always enough.
If you don’t know how to track down chatbot analytics, how do you expect to measure your success? You need to know if the conversational AI you have selected is working for you or not, right?
In this article, we will explain chatbot analytics and suggest some AI chatbot metrics that can help you to measure the success of your AI chatbot program. Let’s start then!
Chatbot analytics is a process to examine how well an AI chatbot is working for your organization. There are some AI chatbot metrics that you need to measure from time to time to find out if the AI chatbot is at all working for you.
By tracking the AI chatbot analytics regularly, an enterprise can be benefitted in the following ways:
Now that you’re clear about the idea of Chatbot analytics. Let’s start discussing some of the most common AI chatbot analytics.
The very first AI chatbot analytics we want to discuss is the retention rate. By retention rate, we mean the users who have interacted with your AI chatbot repeatedly over a particular period of time. Obviously, the higher the retention rate, the better it is for your business. By tracking this AI chatbot analytics, you can get a brief idea of how acceptable conversational AI is to your prospects and customers.
The second chatbot analytics we want to recommend is Bounce rate. Most of you may already be familiar with this particular metric. Bounce rate represents the volume of users that didn’t return back to your AI chatbot or the percentage of failed chatbot interaction sessions.
Your aim should always be to maintain as low a bounce rate as possible. A high bounce rate is an indication that your AI chatbot is failing to provide personalized responses to the website visitors. A high bounce rate is an indicator of poor performance. Make sure that you revise your conversational AI strategy to drive more sales and appointments.
This is an interesting AI chatbot analytics that you should consider. This metric gives you an idea of how many questions the users ask your AI chatbot before they finally get the information they’ve been looking for.
Tracking this metric can be a good strategy. It helps you plan on how you can make your AI chatbot more specific to users’ queries. But again, different users have different queries and the end-results are largely dependent on the goals of your end-users.
If the ultimate goal of your AI chatbot is to improve the interaction rate with the users, this metric is should be on the top of your list. This chatbot analytics helps you measure the average numbers of texts exchanged between your conversational AI and the users on each conversation. A higher value of these chatbot metrics makes your AI chatbot an engaging one.
This chatbot analytics is an insightful one to build your marketing strategy. Most AI chatbots are installed to provide 24/7 live support to the end-users. But have you ever wondered that in which time of the day are the users most active?
This AI chatbot metric helps you measure when do the end-users interact most with the conversational AI within a day? Once you figure out the usage distribution per hour, you can easily align your marketing strategy accordingly. For example, you can project discounts, offers or launch new products in these hours to get most users’ attention.
Monitoring this metric is very important. Most of the users expect the conversation starter messages to be natural. Don’t try to sound too pushy by pitching customers for sales right after they enter your website. Make sure that your conversation starter message seems like a warm greeting. That’s it.
Want to measure how many successful engagements your AI chatbot has encountered over a period of time? Don’t forget to measure this chatbot metric. There are different types of users visiting your website with different information needs. For example, in an eCommerce website, different groups of users come for different reasons like product information, delivery update, return purpose, etc.
With GCR chatbot analytics, you get to understand how many times your AI chatbot has been successful in providing the proper information to the users. This chatbot analytics is pretty helpful to gain users’ insights. It helps you in understanding the trends of customers’ preferances. Hence, you can design your products and services accordingly.
This AI chatbot analytics is known as customer/user satisfaction. To estimate this AI chatbot analytics, you need to create an exit survey. You can ask the users to rate their interaction experiences with the AI chatbots.
For example, you can add quetsions like, “Were we helpful?” - Yes or No. depending on your needs, you can also complicate the questions. Based on the responses acquired from customers, you’ll understand how satisfied they are. You can direct your bot accordingly.
AI chatbots don’t always go viral. But then again, nothing is impossible! If you can create a responsive AI chatbot that offers proper solutions to the users, there are chances of achieving a viral growth for your AI chatbot. So, focus on the basics and enjoy the eventual growth of your simple AI chatbot.
Finally, don’t forget to measure the numbers of leads generated through your AI chatbot. Higher the numbers of qualified leads, more succesful are your chatbots. This chatbot analytics is particularly useful for startups. It can help them gain an idea about the growth of their business operations.
If you’re looking for a conversational AI platform that can generate and nurture leads, offer broader outreach and automate customer support activities, we’ll request you to switch to Konverse.
This AI chatbot can help you generate leads, improve customer engagement and offer personalized chatbot solutions based on your requirements. Our WhatsApp for Business API can help you offering an omnichannel experience to your customers.