How To Measure the Success and Performance of Chatbots?

A checklist to determine the success of conversational solutions

Ruchika Drabla

Jul 21 • 6 min read

Measuring Chatbot Performance

Post pandemic, almost every business understood the efficiency of chatbots. Choosing AI over traditional communication channels was the only option left. However, when the opportunity hit, chatbots proved themselves. According to AI Multiple, the chatbot industry is likely to increase by $1.3 billion by 2025. Almost all AI startups believe that chatbots and virtual assistants will be the necessity of consumers.

AI is the future. But it comes with its limitations. So, businesses need to keep a check if the results are promising. Chatbots are not new to the market, but how to measure performance can be unclear. Most marketers still wonder if the results are up to the mark. We can not just rely upon the success stories of the bot. Assessment at regular intervals will be the key to success, like any other strategy. 

To make your chatbots perform, we need to understand the matrices. If the performance metric is on track, it's a win-win for both your business and your customers. Let us learn how to measure the performance of AI bot implementation.

When the performance metric happens, the comparison is never with traditional channels. If you compare AI implementation with conventional platforms, the results will not be appropriate. To check the results, check the performance of pre and post-AI implementation.

In addition to sales, the goal of business is engagement and retention.

Let's look into some of the metrics used to measure the performance of chatbots implementation.

Interaction

Measuring the conversation between the bot and the customer like messages sent and received gives you the clarity of interactions. It will let you know how much further your bot can take the conversation. With the rule-based bot, the metric is straightforward.

Whereas, when it comes to NLP based chabot, it's not that simple. The length of the interactions varies depending on the context. Also, the use case and keywords are one of the reasons. Lengthy conversations do not always indicate failures. It might be a quick enquiry session, which might lead to conversion. Observe if the conversations bringing business are long or brief.

Engagement

Bringing customers close to the goal after the initial stage is engagement. If they get engaged in the next task, it opens our doors for the next interaction. Most visitors to your website will take initial action, but only potential customers will engage and respond to bot's conversations.

Engagement is one of the significant chatbot matrics for any business as it filters the slightly interested crowd from who came just for information. For example, you hop on to a wristwatch and accessories' website. It will welcome you, and ask you about your colour preference. You opted for, let's say, 'rose gold.' Hence, showing your interest and making your 'activation count' on the matrix. This way, you can also filter new customers and returning customers by using conversational automation.

Volunteer Users

Interaction and engagement is one thing. But do you know what is more exciting? Volunteer users. These users start the conversation with the bot on its own. They don't need any initial nudge. When hearing about the product or bot, the prospects interact with your brand over different channels.

Various offers or deals might catch the attention of a potential customer, attracting them to approach your bot and initiate the conversation. This one is the most important of all the matrices. The reason being the customer is interested in your products or services, and the lead is actionable. Unprompted customers are the ones, which don't require a lot of follow up. The thing and offer are already in their mind. They will convert soon. Do not miss out on this one. You can also use AI for lead generation that drives results.

Calculate Average

Some customers are in touch with the bot, while some find it inefficient. This way, you can keep a check on the conversations happening regularly. The average of conversations happening is one of the creative ways to find out the efficiency of the bot. You can keep it a day, week or month wise.

Calculation of average can go both ways. Either you can keep it session per user or conversations handled by the bot. Both ways stand out and are unique in their way. However, it depends on the use case of the bot. But you can measure the conversations happening in a particular period. Also, it depends on the target you are making. If the target audience is willing, your bot will come up with an increased number of conversations.

Retention Rate

The ultimate goal of any strategy is not always making it a sale. The increased retention rate gives you an actual percentage of success. Here, if the customer is coming back to the bot for any resolution, that's retention, and the reason can be resolution or knowledge-based.

You can measure the retention rate after a week or two weeks. You can observe the retention after that. A customer is not likely to initiate a conversation twice a week or after 2-3 days. A satisfied customer will also connect with the bot after a week or so. This way of measuring will help you find critical moments. This way, also adjust to your strategy accordingly.

Metrics for measuring chatbot performance infographics

Goal Completion Rate

The measurement of the efficiency of the bot depends on your goals. If your firm wants to acquire leads, then kudos! This metric is a perfect fit for you. It helps those working on actionable insights. If that's your business, GCR is for you.

Unlike other metrics, it doesn't keep a check on the engagement as engagement is never an ultimate goal. It helps you check whether you're able to accomplish the goal you designed or not. This way, it keeps track of users. The conversation chat doesn't make any difference if the goal was not successful. Hence, you cannot call the bot successful if engagement is more, but conversion is not.

Fallback Rates

FBR metric is for Natural Language Processing based bots. With NLP, training needs to be accurate to get the best results. Rule-based bots are personalized and complex as the conversations are structured and dependent on the data.

By capturing the bot's fallback rate, you can check the efficiency of its performance. Bifurcating fallbacks into different categories will surely give you a deeper insight. This way, you can enhance the service. Also, you can eliminate the flaws you found.

Customer Satisfaction Rate

Well, it is listed last, but it is an important one. In this customer-centric economy, customer satisfaction is a primary factor. Are you also one of those planning to use the bot as customer service? Well, give it a try. There is no harm in that. But measuring its performance is critical.

Well, replacing customer support with a bot can be a good idea. But remember, you are losing the end touch with your customer. So if you want a replacement, you need to keep an eye on it. You need to understand the sentiments and requirements of the customer. Now the question is how you measure it. The best way can be to make them rate their experience after every conversation. Alternatively, you can keep track of queries or use sentiment analysis. You can deploy chatbots across customer journeys to enhance CX.

Is there anything else?

The above discussed eight metrics are essential points to check the efficiency of your business. There can be more to it for sure. Apply other modus operands to check if you are on the right track. Every business, every strategy is unique. So are the tracking tools.

Explore more! For better results! For your satisfaction!

Make sure your clientele is happy.

Make sure your strategy is working.

Written by

Ruchika Drabla

Growth & Marketing Head at GenieTalk.ai

Marketing growth professional with 9+ years of experience in all verticals of Performance Marketing, I'm an AI enthusiast and loving my role as Growth leader in Conversational AI startup GenieTalk.ai

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