7 key metrics for measuring the performance of AI-enabled chatbots
Chatbots have become a core part of the business industry. These intelligent conversational software agents have emerged as an effective solution to many challenges of the business world, from handling multiple user requests at the same time, engaging them 24x7, learning from their behavior, or providing them with the enhanced user experience.
AI-enabled chatbots can help improve the user conversational experience, and lessen the burden on the workforce while maintaining the quality of output delivered.
Because of the potential advantages to organizations, nearly 100,000 chatbots have been docked to Facebook Messenger so far. Around 60 percent of the world population has admitted preferring communicating with a chatbot over a human. And about 80 percent of startups and established brands are supposed to invest in chatbot development by 2020.
This clearly indicates the dire need to invest in chatbot development. But is the investment enough?
Considering the growing demand for chatbots, it is evident that you will not be the only person putting your money into this process. In other words, you have to be 100 percent sure that your chatbot performs efficiently and effectively and above all, better than the bot of the competitor so that you could enjoy better perks.
But, the main question that arises here is: How to ensure that your bot is doing well? How to be certain that you are getting the best ROI out of this investment?
While it may seem quite difficult to determine the performance of a bot, the following 7 metrics (just like that in the case of the mobile app) will help determine their success:
1. Activation Rate
The foremost performance metric to look upon is activation rate. Since chatbots were brought into the business process to respond to endless user request sooner, calculating how faster they connect with a user is also helpful to determine the chatbot performance.
In other words, you need to calculate the number of users your chatbot interacts at a particular time, the number of new users sending messages to the bot, and the number of users engaged into the process.
A higher value of these factors only defines that your bot is performing apt.
2. Session per User and Average Session Rate
Calculating the average session rate and the number of sessions per user also make it easier to determine the chatbot efficiency. If let’s suppose that your chatbot prime goal is to answer the FAQs of the users. If the user is returning back to back to your customer support portal with the same query, it implies your chatbot has not provided him with the satisfactory results in a single turn. This, in general, can affect your customer engagement and retention rate.
Likewise, the time duration of a session also indicates the chatbot productivity. If the average session duration is too short or long, when compared to the desired time duration, it shows that your AI-based chatbot is either not providing the users with the right set of information, or is too slow in processing. In both cases, the performance level of chatbot is lower.
3. Conversion Sentiment
Another way to evaluate your chatbot performance level is conversion sentiment analysis. In this analysis, you look into how the user feel while interacting with the bot and how they react. If their reaction is positive, the bot is performing efficiently. While, on the flip side, the bot may need some major improvements.
While taking the conversion sentiment analysis, bear in mind that various users use the expression, “I love you” or “I hate you” just for playing games with a bot. Analyze properly before counting them into your chatbot negative pointers.
4. Confusion Rate
Many times, chatbots get confused with the user questions and reply with an ‘I don’t understand’ phrase or start behaving abnormally. This ruins the user experience and affects your business ROI.
So, measuring the confusion rate of your chatbot is also necessary for better business ROI. A lower confusion rate implies smoother performance, while a higher confusion rate clearly defines that you need to put extra efforts in training your bot.
5. Human Fallback
Chatbots are meant to perform tasks independently and make humans free for more productive tasks. In such a scenario, how many tasks does your bot does without seeking human assistance is also a performance KPI to check for.
If a chatbot involves human into the conversation at the first stage itself, it signifies poor performance. While, if a chatbot handles most of the part of the conversation, leaving almost no need of humans, the bot is performing effectively.
6. Artificial Intelligence (AI) and Machine Learning (ML) rate
Last but not least, the AI and ML rate is also a crucial trait to consider. Measure if your AI-based bot’s ML is upgrading with time - is it learning from human interactions and putting it into its practices?
For doing this analysis, compare the results of the aforementioned KPIs periodically. A steep slope in the graph will signify that your chatbot is becoming more efficient and adding value to your business.
7. Goal Completion Rate
The analysis of whether your chatbot is performing as per the desired goal or not is yet another important performance metrics to consider. If the prime aim behind is user retentions, check if the retention rate has improved or not.
Likewise, if the revenue generation is the main goal, look into the revenue generated and compare it with the previous one. And, if the main motive behind investing in chatbot developing is providing an aid to your employees, see into how much has the bot eased their work.
In short, have a clear idea of your prime motive behind introducing chatbot into your process and analyze the bot performance based on the same.
There are various metrics to evaluate the performance of your bot. So, when your chatbot gets into action, make a list of the KPIs to look upon and execute the performance analysis periodically. Keep an eye on the results to ensure that you are getting fruitful outcomes from the investment in chatbot development.