The Ultimate Guide to Chatbot Analytics [Essential Metrics and KPIs to Measure]

Chatbots are everywhere! From the local grocery store to the multinational airline company, every business wants to deploy a chatbot for its various business processes. Almost all businesses are trying to create a chatbot for themselves. According to 2021 chatbot trends, the chatbot industry sees positive growth in the coming year.

However, almost every single one of these businesses suffers from a common problem. They do not know whether these chatbots are actually efficient. And it shows that so many chatbots out there are inefficient, clunky, and a complete waste of time to users. Chatbots are an investment for a business. Every blog on chatbot talks about how they provide a high ROI and better customer experience. Many chatbots cost businesses a significant amount of money and time. This is why it becomes important to ensure that the chatbots are doing what they are supposed to do. Keeping that in mind, here is a detailed guide on chatbot analytics, various metrics, and chatbot KPIs that can be used to understand your chatbot better. However, before we jump into it, here are some statistics related to chatbots.

Where do we start?

Knowing where to start is very important. There are numerous chatbot KPIs that you can measure, however, not every single one of them is important for the success of your chatbots. Keeping that in mind, we have listed the most important KPIs in this blog. The best part is that all these KPIs can be found in your chatbot analytics tool. Be it a traditionally coded chatbot or a no-code chatbot like the ones you can create with Appy Pie Chatbot.The chatbot analytics metrics and KPIs have been divided into 3 parts -

1. User-Based Metrics
2. Bot-Based Metrics
3. Message-Based Metrics

There are a total of 19 metrics among all the categories. Without further delay, let’s jump into them.

User-Based Metrics

These KPIs are calculated based on the user base of your chatbot. Every chatbot deployed by a business is used by the potential customer base of that business. Here are the various user-based metrics that you can track.

• Total number of users

The most basic metric to track is the number of users that your chatbot is being used by. A chatbot is a worthy investment only if people use it. This metric can tell you how many users have used your chatbots in a given time period. Tracking it over time helps you assess the number of users and the amount of data your chatbot has collected over time. This metric also helps track the size of your market and how effective your chatbot is.

• Active Users

An active user is someone who has read a chatbot message or responded to it in a given time period. For example, all the people that interacted with your chatbot in a weekly report will count as active users. Active Users are your potential clients. They are the most important users your chatbots can have. Tracking active users helps measure the effect and success of various marketing campaigns and support statistics. The number of people who read your chatbot messages often form the base of your client pool. It is these people that are most likely to turn into customers.

• Engaged Users

Users that converse with your chatbots and send multiple messages to you are the ones that actually matter to your business. Analyzing these users helps gain the most insight nto the effectiveness of your chatbot. Comparing this number to active users helps determine the ratio of users that take their decisions based on your chatbot’s activity. Engaged users often become customers.

• New Users

This statistic is important if you deploy marketing campaigns through your chatbots. Once you have determined a set value for the number of active users, any additional user will act as a new user. New users will keep your customer base strong and help gain more customers over time.

Bot-Based Metrics

These metrics are provided directly by your chatbot. These are some of the most important metrics for your chatbot analysis. Let us discuss them.

• Retention Rate

Retention rate is the percentage of users that come back to your chatbot in a given time period. Retention rate helps derive valuable insight and helps analyze how much time customers spend with your chatbot and how well you maintain their attention. Retention rate is also an important metric to judge the success of your marketing campaigns and chatbot-related promotions.

• Goal Completion Rate

Goal completion rate analyses the number of conversions you have received after users converse with your chatbot. GCR is the direct metric responsible to judge the ROI on your chatbot and the measure of success for lead generation chatbots.

• Goal Completion Rate

GCR helps draw perspective on multiple aspects of your chatbot and can also be used to judge if other kinds of chatbots do their work properly. If your GCR is low, your chatbot needs improvement.

• Failure Rate

No chatbot is perfect. Every chatbot will make errors and might not be able to answer a customer’s queries and questions. Sometimes, it may even fail to receive relevant information from a customer. Failure rate helps in judging how many times a chatbot fails to help a user or keep their interest in your marketing campaigns. A high failure rate usually means that something is wrong with your chatbot.

• Leads Generated

Lead generation is an important metric for marketing chatbots. Leads generation chatbots motivate users to provide necessary information and to guide users to product pages. Measuring the number of users gives an idea about how many leads your chatbot has generated.

• Self-Service Rate

A metric important mainly for customer support chatbots, self-service rate tracks how many users received all the help that they needed directly from your chatbot. It is found by tracking the number of completed conversations. A high self-service rate is an indicator of good customer support.

• Takeover Rate

Some chatbots, including the ones created with Appy Pie Chatbot, often have a team of live agents they can direct complex customer issues to. This metric can help judge how many chats are redirected to live agents. This metric helps understand what keywords and customer queries your chatbots have answered too and what are the most complex problems your customers face.

Message-Based Metrics

Message metrics help gauge how active your chatbot is. They tend to understand the trends relating to your base and help categorize your users for further analysis.

• New Conversations

This metric records and shows how many new conversations were initiated by users in a given time period. This metric helps measure how many users interact with your chatbot. It can also categorize users based on what they ask your chatbot. New conversations can help gauge the retention rate and the number of new users.

• Received Messages

Received messages or in messages are the messages that users type in the chat box. These messages help measure how many times do users have to send a message before their issue is resolved. This metric can help make your chatbot more efficient.

• Missed Messages

Missed messages are messages left by users that your chatbots failed to analyze. This metric is a bit more difficult to calculate but is an excellent way to find if there are certain keywords that you miss out on.

• Total Conversations

Total conversation is a metric that tells how many conversations were completed by your chatbot in a single day.

Conclusion

That was our list of valuable metrics that you can use to improve your chatbots and make them more efficient. Each of these metrics can be analyzed with a little know-how of analytics. You can cross-reference these metrics with each other for even more insightful data. We have a future blog coming up on using these metrics! Sign up for our newsletter below to get a notification for that blog. As chatbots are still relatively new business software, more KPIs may be found in the near future. We will keep updating this blog. If you have more suggestions for KPIs to include in this list, leave a comment down below and we will add. Let’s work together and create an information resource for everybody!

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