Chatbot analytics is the process of analyzing historical bot conversations to gain insights about chatbot performance and customer experience.
Once a chatbot is live, a company's work as a chatbot developer does not cease. Customer experience has become a significant factor in achieving a competitive edge due to increased competition in every industry. After a company introduces a chatbot, it's time to monitor how users interact with it.
Chatbot analytics, like other analytics methodologies, allows companies to measure important chatbot KPIs and make data-driven decisions to improve chatbot performance. Many chatbot projects fail for a variety of reasons, including choosing the wrong metrics to optimize. Businesses can avoid possible failures by relying on chatbot analytics..
Improved data collection
As new data privacy restrictions such as the GDPR make it more difficult to use third-party consumer data, conversational analytics software becomes a tool for collecting first-party data insights from users who are eager to communicate with bots.
Customer insight generation
Businesses are allowed to map popular user paths, tasks, and exit points using chatbot analytics dashboards (in visual context) so that patterns, trends, and correlations can be discovered that would otherwise go unnoticed using text-based data analysis methods. This aids firms in gaining a better understanding of the client experience.
Chatbot statistics can be accessed from AlphaChat main menu. Select Reports -> Bot. Here you can see several different metrics describing the performance of the virtual assistant.
This metric shows how many times a chat window has been triggered with and without user engagement.
To gain insight on customers behavior and preferences when interacting with virtual assistants we have included a neat little way to monitor the count of interactions either with text, with buttons or with both text and buttons. Some people prefer to use free text, others know exactly what they want and quickly navigate to the desired topic through a clickable menu.
With this metric, you can see the percentage of users who interacted with the bot and did not continue the interaction with customer service agents.
Number of users who interacted with the bot and did not continue interacting with customer service agents.
Every conversation flow can end with a feedback request, which in other words is a question "Was it helpful?" The binary nature of was it helpful? – yes or no – can provide quite harsh but accurate insights on customer satisfaction and on what could be improved upon. Feedback asked/received shows the summary for all of the feedback on all topics on a selected time range. This is a useful metric that can be compared on a monthly basis to get a glimpse of the virtual assistant's performance.
This metric shows daily information about the feedback of the chats.
For deeper insight, it is important to analyze feedback for every topic that conversational AI handles. We do it by measuring helpfulness scores. Actions can be sorted in a selected time range either by groups of topics, count of sessions, feedback requested, feedback provided, feedback positive or feedback negative.
Button report displays the use of buttons in a specified time range and what is the URL/payload of the buttons. Buttons can be sorted either by the count of sessions or the URL/payload.
Analytics are often overlooked and underappreciated when it comes to chatbots. While chatbot analytics are unlikely to make or break the success of a chatbot, they can provide valuable insight into opportunities for growth and improvement by allowing chatbot builders to get into the minds of users.
You can get started with your own AlphaChat account already today and build your own chatbot (you get a 10-day free trial with no payment information needed up front). If you have additional questions, reach out to firstname.lastname@example.org or contact us through the contact form and our technical team will gladly help you out.