We are aware about the gaining popularity of chatbot in businesses due to its’ value. In our last article, we have covered the steps to train a chatbot. However, that is not enough. You must also monitor and measure the performance of the chatbot against predefined objectives. Just like any technology, it is subject to periodic evaluations. After training and implementation, you need to define the required KPI’s or metrics to measure the performance of your chatbot. Just as you have a process for employee evaluations, you must also generate this for your chatbot.
What do you do to measure the performance of a new employee? If they belong to sales and development, you measure their performance by numbers; if they have been hired for marketing, you measure the performance by number of leads; if customer service, the metric to measure the performance is to evaluate the customer engagement analytics. Measuring the performance of chatbot starts with these same simple questions.
The first thing to do is to identify the bottom line objective/purpose behind building your chatbot. This is accomplished by asking one question. Was the chat bot created for customer service, sales, supply chain, or human resources. Your metrics/KPI will come clearly from the purpose of the bot development. Determining metrics follows the same strategy that you follow to define the metrics of a new hire to measure their performance. Once, you determine this, you have the answer what are you expecting the chatbot to do and the measurement criteria you will use.
Below is list of few helpful metrics to measure the performance of your chatbot. To maximize the potential of these metrics, you should have a strong integrated BOT Analytics mechanism. There are many tools available, however the Google Analytics Platform Chatbase is a better choice because it’s open source. Thanks to Google.
Making money is crucial for every business. Therefore, the top measurement question to ask of the chatbot is: does this investment produce favorable numbers? If the sole purpose of your BOT to streamline, accelerate & enhance sales like Tacobot does for TacoBell, then you have some easy analytics to assess the performance of your BOT.
The concept of numbers is frequently related to sales. However, numbers do not always have to be thought of in this way. Another way to think about numbers is as part of process optimization to decrease costs. In particular, if you have developed a customer service BOT, you should measure how much of a process that was previously manual has been replaced by your chatbot. This is determined by looking at analytics that measure how much speed or efficiency has been increased over what it was previously.
Hence, to justify the investment, it’s important to identify what the sole purpose of the chatbot is, just the same way you ask, why did you hire the new employee? Once you have that answer, you have the predetermined numbers to measure the performance & justify the investment, based on your business objectives. You can always change these goals and look for improvement over time.
Another question to ask is how satisfied the end user is? The end user in customer service is the customer and the end user in HR is the employee. The end user feedback is the most helpful metric to evaluate the performance of your chatbot in this instance. If your bot is not increasing the satisfaction rate for the end user, then the actions must be taken to improve it. The simplest way to get this feedback is to ask the end user, on a scale from 1 to 10, how satisfied they are with the interaction. This allows the business to gather information to identify areas for chatbot improvement.
Another metric to look at the performance of a chatbot is monitoring session duration. Session duration measures average customer interaction and ensuring customer engagement. If you are happy with the analytics, then the chatbot is doing its’ job.
Let’s look at an example from customer service. Your bot is there to serve customers who have a reason for contacting your business. If the chatbot appropriately greeted the customer and was able to answer their question or know when to refer them to a human, then the goal was met. This is what the full customer service lifecycle would look like if the chatbot is performing the responsibilities flawlessly. When conversation bounces back, stops, or goes around in circles, either the session duration will increase or decrease significantly from the average time. This is a clear indicator that improvement measures must be taken.
For session duration to be a useful metric, the conversation must last long enough. It is hard to determine if a chatbot was helpful when the conversation is too short. However, if the conversation goes on for long enough then it can indicate if the chatbot was useful. To fully utilize this metric, an intelligent analysis is needed.
When a customer returns at a separate time and interacts with your chatbot, this means the customer is satisfied. This is a clear indicator that the chatbot is bringing back and retaining the customer. There is a simple process to measure the performance of your chatbot. Using either your Analytics mechanism framework Analytics or any other Analytic tool allows you to define your criteria, track and measure the Analytics.The KPI in this case would be determined through retention analytics.
If you remember, we have been repeatedly asking questions to focus on the purpose of your Bot in order to measure its’ performance. When you are looking at click through rates or leads, you need to have an expected number of click through rates and leads. This number gives you a baseline to measure the Bot’s performance. The results in this case will be pretty easy to identify with the help of your Analytics mechanism. If your BOT is successfully converting your web traffic into leads, this means your chatbot is also intelligent enough to address the queries likely to turn into leads.
It is unreasonable to think that your chatbot will be completely perfect. This is because a development process can’t be completely flawless. Hence, the development of a chatbot is likely to have pros and cons. Some of these cons will leads to fallbacks. Rate of confusion is one of the most common issues that could arise from unexpected messages from customers. If the chatbot does not understand the query, then it will likely give an answer that makes no sense. One way to minimize this is to train the chatbot in a variety of scenarios and customer responses.
If you have a high confusion rate, then it means your chatbot needs more training. Part of what can contribute to a high confusion rate is that the users of the system are actually confused. Having simple guides can minimize this confusion by showing users how to use the chatbot properly. User design is also helpful because it reminds you to design the chatbot as simply as possible. When your chatbot reaches a point where it can no longer handle the conversation, it can be helpful to recalibrate the discussion. Another way to measure fallbacks is to look at when the chatbot has to refer a query to a human. Using both of these fallback methods, confusion rate and referral to humans, gives you an overall sense of the fallback rate.
You want the chatbot to learn from its’ mistakes, gain the experience, and become self-driven to justify your investment. An investment can feel wasted if you do not want your chatbot to repeat the same mistake and it builds knowledge over time. Integration of artificial intelligence and machine learning help you to measure this KPI. To determine the effectiveness of the AI, you need to look at the percentage of user questions that are completely understood.
The question that is probably on your mind is, with all of this training, can your chatbot ever learn on its’ own? The answer is yes and the solution is machine learning. chatbots that have machine learning can measure progress by looking at improvement in service over a period of time. Robust machine learning can help the chatbot to constantly run its’ own gap analysis, look for areas of improvement, and then make those improvements. In the event of failure to learn from itself, you must revisit the scope and perform the gap analysis with your development team to ensure Chatbot is learning with the integration of artificial intelligence and machine learning.
All customers, but especially millennials, want instant and satisfactory service. Therefore, the millennial demand for chatbots is clear. What customers want is simple and effective service that can be reached at any time. It is important to remember that not all chatbots can meet their KPIs and keep customers satisfied. However, you will never know this unless you take metrics of the chatbot. The market for chatbot technology just keeps growing and employing these metrics will help you to identify any gaps, offer sufficient training, and help the chatbot to excel in performing. This create the potential to stand out from the crowd.