Chatbots engage with customers round the clock, offering them uninterrupted and instant assistance. But often the data generated from chatbots comes out as just facts and figures. If you’re truly looking to streamline your customer service processes – you need to analyze, interpret, and present this data into useful information.
While each business is different with varying use cases, there are several key metrics that can provide valuable real-time insight for any chatbot – Artificial Intelligence/Machine Learning based or Rule-based. Let us look into some important Key Performance Indicators (KPIs) that can help you to enhance your chatbot optimization practices to increase your customer’s chatbot experience.
Why Analyzing Chatbots Matter?
Successful implementation of a chatbot – be it on a website or a Social Media messenger like Facebook messenger doesn’t end at just going live. Chatbot analytics is also a crucial step of chatbot implementation as it helps businesses understand their users’ behavioral patterns and serve them better. Peter Sondergaard, Gartner says – “Information is the oil of the 21st century, and analytics is the combustion engine.” Chatbot analytics not only helps companies determine the success of their chatbot but also helps them fine-tune their future business strategies.
Here are some top reasons why analyzing your chatbots’ data is just as important as successfully implementing it.
Calibrate Current Flow
Even if your chatbot has gone live, it is essential to keep training it and improving the messaging and the customer support process. Does your bot cover all the questions your customers might have? Have new users been asking a new question? Is your bot assisting the visitors with the right answer? Do you need to optimize underperforming answers?
Remember that new needs represent new opportunities for enterprises to serve their consumer base. Broader the range of topics, easier for users to get help!
Examine And Improve Customer Service
If you are implementing a chatbot to deliver excellent customer support, you can’t do so without measuring its impact on customer satisfaction. After all, you can’t improve what you don’t measure. Chatbot analytics help assess your customer satisfaction, helping you make data-driven decisions to implement logical and tactical strategies to improve new conversations.
Get Context From Unstructured Data
With a Natural Language Processing (NLP) backed chatbot, training a bot has been made easier. However, because of the unstructured format of the conversation flow, it can be difficult to pinpoint what users are looking for. This type of chatbot conversation data needs to be extracted, cleansed, and evaluated properly to get the right information. Conversational analytics is helpful as it helps organizations understand their users’ intent and context and further improve the bot to serve them better.
Now that we’ve figured out some top reasons to analyze chatbot data, let’s look at some widely used chatbot metrics and visualizations that can bring valuable insight into opportunities for growth and improvement for your chatbot performance and the company.
Chatbot Key Metrics To Track
1. Total Chatbot Interactions
A basic but one of the most essential metrics, Total Chatbot Conversation tracks the total number of conversations between the chatbot and your visitors. It is an immediate indicator of your chatbot’s impact and success across a number of users. This metric is also a very good reflection of your market size. Tracking this metric over time gives you a good insight into seeing the trend – the volume of users you’ve helped, how often your chatbot is being used, and if the users are increasing or decreasing.
2. Average Chat Rating
Allowing users to rate your chatbot is an exceptional method of understanding how your chatbot is performing. Understanding your consumer base’s satisfaction or dissatisfaction with your services is the most straightforward way of improving your chatbot and the services it offers, especially if you’re tracking written feedback along with a grade. Tracking this metric over time can help organizations track how user satisfaction rates evolve for their customers and find ways to improve.
3. Total Transferred Chat
A human takeover is a common phenomenon where a visitor chooses to connect with a live agent after interacting with a bot. Another important metric everyone must track, most organizations like to give people an option to escalate to an agent when talking to a chatbot in case they’re not happy with the support they receive. This fallback KPI tells you how well your chatbot is doing in supporting your customers and how many times there is a need for a human to jump in. Keeping a track of this helps you fine-tune your bot and broaden the horizons of topics covered.
4. Total Tickets Created
If your chatbot is integrated with a customer service system such as HappyFox Help Desk, chatbots allow visitors to submit a ticket. Usually, a visitor submits a name, an email address, and a brief about their issue. Agents use this information to revert to the consumer with a resolution. This saves time for both the customer and the customer service agent. Stakeholders can track this KPI to maintain a balance of the incoming tickets and to see how well the deflection is for the chatbot. Keeping this number low means your chatbot is successful.
5. Missed Chats
A very important metric to gauge the performance of your chatbot, this KPI helps you visualize visitors who initiated a conversation but failed to be accepted by a chatbot or even reach the live agent. These are failed opportunities for you to engage with customers and prospects. This often is an indication of some error happening behind the scenes and needs immediate attention.
6. Top Chat Issues
Through this visualization, businesses can check which topics repeat the most to see where their users’ interests lie. What are their user’s most frequently asked questions? Through this metric, businesses can work on improving their product or service with the aim to gradually reduce this number.
7. Average Chat Duration
This metric tells you how much on average your active user spends interacting with your chatbot. This allows you to evaluate how much user interaction your bot has and can be used to analyze the satisfaction rate of your team’s customer service.
8. Total Contact Support Actions
This visualization gives you a distribution of escalation of your chatbot user requests. You can see if your customers prefer submitting a ticket or chatting to an agent. This might prove helpful in the strategic scaling of your human customer agent team.
9. Monthly Average Chat Count
Tracking what your customers are frequently questioning monthly keeps your business and product team aware of the success of the company and their customers’ true interest. This not only helps you see what months are busy but also pinpoint exact products/services. This enables teams to forecast better and prepare their chatbot and even human agents in case the need arrives for them to interject.
10. Chat Type Distribution
A successful chatbot conversation can be either Bot handled or Bot Managed. If a chatbot is solely driven by a chatbot, it is classified as a Bot Managed chat. And if there is a human intervention involved because of an escalation, the chatbot is classified as a Bot Handled Chat. This area chart gives you a look into how your chatbot is performing in handling chat requests.
11. Weekly Chat Inflow
While a chatbot is famous for providing uninterrupted round-the-clock service, monitoring the days of the week when the chatbot is the busiest can be beneficial. This metric helps you properly prepare for the greater influx of requests coming in and even help prepare your human agents if the need arises.
12. Hard Deflection
This KPI is a concrete indicator of how successful the chatbot is in providing service to your user without any human intervention. With this metric, you can track the number of times your chatbot was helpful to your consumer base. Hard Deflection comes into the picture when a visitor is satisfied with the bot and finds it “Helpful” and the chatbot is successful in navigating the visitor through their query without any escalation.
13. Soft Deflections
Soft Deflection refers to when a visitor leaves the chatbot conversation after being offered an answer but not leaving feedback or escalating to an agent. If your customer doesn’t tell you what’s wrong or right about your service, businesses resort to guess and gamble. It is an important metric to track as it helps enterprises gauge their visitors’ interests and improve their feedback mechanism through more informed decisions.
Chatbots have gained immense popularity in almost every industry – e-commerce, retail, logistics, etc. because they are successfully helping companies with self-service support automation – transforming user experience and improving customer retention rate and conversion rate. By keeping track of your must-watch key performance indicators and adjusting accordingly, you can be assured that your chatbot will provide a remarkable customer experience and drive customer delight, and foster customer loyalty. If you’re looking at integrating your chatbot data to a powerful and intuitive platform to visualize your chatbot data, connect with us today!