With the emergence of chatbots, businesses can easily treat their customers like a king. Customers believe that talking with chatbots is convenient and getting their queries resolved soon. A survey shows that 50% of customers prefer to communicate with chatbots instead of calling support while shopping online. On the other hand, some people, especially senior citizens, believe that engaging with chatbots is very difficult. According to statistics, 80% of retail customers are not comfortable with assistance.
Are you confused about whether you should go with a chatbot development solution or not? Developing a chatbot for your business website is definitely worth an investment. A chatbot is an AI software designed to perform multiple tasks without any human intervention. These tasks are:
- Making a reservation at a restaurant.
- Marking a date on the calendar.
- Gathering information to its users.
- Informing them about the weather.
If you want to get better results with chatbots, you need to understand the role of chatbots in enterprises. Chatbots can simulate a conversation with a person and are increasingly available in messaging apps. Overview of chatbots are shown in the figure:
To ensure that your chatbot solution gives your customers an excellent experience, you need to track your chatbot analytics. As more and more commercial businesses choose chatbot development, a question “is there a way to measure the performance of a chatbot?”
The answer is yes! You need metrics and KPIs to measure the performance and success of your bot. In this blog, you will learn about metrics and KPIs to measure the chatbot success for your business. Before moving ahead, let’s know something about chatbot analytics for better understanding.
What is Chatbot Analytics?
Chatbot analytics is the process of analyzing historical bot conversations to get proper knowledge about chatbot performance and customer experience. Seattle Balloon Assistant is an example of chatbot analytics. This chatbot solution, built with a conversational marketing platform, was designed to minimize the staff workload. This artificial intelligence chatbot is the best example of how to streamline the customer experience and save staff time. Users can get answers to several different questions with this virtual assistant. If your inquiry is specific, you can also type out a question for the chatbot. Then its natural language processing (NLP) technology can usually make sense of what you want to say. Hence, it will save booth staff and customers time and effort. So, let your employees focus on complicated tasks while your chatbot handles inquiries quickly and efficiently.
Key Features of Chatbot Analytics Tools?
#1 – Sentiment Analysis
If you want to make your bot more user-friendly and highly efficient, it is essential to understand whether your users respond positively or negatively. That’s where sentiment analysis comes in! This analysis allows them to capture and process customer sentiment from their conversations without explicitly asking.
#2 – Customer Segmentation
One of the most common features of chatbot analytics tools is customer segmentation. It allows businesses to personalize conversations and try out A/B testing to chatbot landing pages and query responses to see their customer engagement.
#3 – Intent Analysis & Transcript Search
Intent analysis helps check how developers meet messages to intent categories. Such tools help businesses to search the full text of transcripts to track the complete lifecycle of users.
#4 – Identification of Task Failures
Thanks to analytics tools, enterprises can record and divide all situations where a bot fails to complete a task. It helps developers work on bot’s responses, resulting in improved bot performance and customer satisfaction.
Let’s learn about some analytics vendors and tools in this article to clearly understand chatbot analytics tools.
You can also read about how chatbots provide Covid-19 information.
What are Leading Chatbot Analytic Tools & Vendors?
Developing a chatbot without determining your users’ behavior is like shooting in the dark. Even, it could leave the future of your business down to chance. So, it is the best decision to go for chatbot analytics. There are many tools available in the market for chatbot analytics. Some top analytic tools are mentioned below:
#1 – Dashbot – Best for Conversational understanding
If you want to understand your users’ conversation using a chatbot, Dashbot is the right choice! Dashbot is one of the most popular chatbot analytics tools used to track traditional metrics like the number of users interacting with your chatbot or how many of them continue using them. This tool uses bot-specific metrics to give you a strong understanding of your users’ conversations. For example, sentiment analysis will give you a high-level view to feel users’ sentiments. And the conversation funnel will show you categories of questions your users are asking at different points and how the conversation progresses, usually from there.
# 2 – Botanalytics – Best for Tracking User Lifecycle
Botanalytics is the best solution for tracking individual users. Their dashboard focuses on the user’s lifecycle, representing the length and date of the last conversations and the number of conversations per user. With this tool, you can identify the most active users of your chatbot. Botanalytics provides deeper user logging by offering transcripts of every conversation.
#3 – Facebook Analytics – Best for Ease of Integration (sometimes!)
Facebook’s analytics platform goes one better as it requires no custom code at all. After all, this is only possible for Facebook Messenger. So, if you are targeting other channels, you will have to go with other tools. What’s more, Facebook analytics might not be a bad choice, especially if you need a super-fast solution to get started with analytics.
#4 – BotMetrics – Best for Flexibility
Biometrics is the best tool for those who want to be totally in control of their analytics. As it is an open-source tool, you can do whatever you want with this analytics tool. Thanks to its dynamic dashboard feature, you can gather data without knowing your requirements. For example, when you build a clearer picture of your project, you need to create new reports and dashboards dynamically from the web interface without checking the code again. In the end, if you need to retain total control over your data, BotMetrics’s on-premise installation is the right choice!
#5 – Manner – Best for Optimisation
The manner takes a more active approach to analytics. This tool tracks progress via developer-defined goals instead of simply logging the number of messages sending or receiving. It can measure the messages likely to create a successful conversation by measuring the performance and effectiveness of variations on your messages. In addition, this analytics tool lets chatbots customize the way used for different users. For instance, some users want direct, informational messages like “Delivery scheduled for 12/01,” while others prefer a conversation approach like “Great, your order will be delivered on Tuesday!”.
As you know about chatbot analytics, its features and tools, it’s time to know the reasons why you should go with chatbot analytics. This main question arises among developers and business owners “why chatbot analytics matter for businesses?”. You will get your answers in this article:
Why do Chatbot Analytics Matter for Businesses?
Undoubtedly, chatbot analytics determines your chatbot’s performance and success. But did you know that chatbot analytics can also provide valuable insights into opportunities for business growth and retention strategies? It is essential to understand the chatbot’s capabilities by constantly determining its performance and effectiveness. For this, you need to know the key chatbot metrics, which is a vital feature and a critical factor for your business’s success in today’s highly competitive market. Chatbot analytics also help businesses in marketing. How they help in marketing purpose is shown in the figure:
Here are some reasons why Chatbot Analytics Matter for your Business’s Growth:
#1 – Assess Chatbot Effectiveness
With the right chatbot analytics, you can understand and measure how chatbots help you achieve your business goals. You can measure,
- How do your customers get chatbots?
- Are they satisfied with the responses?
- Does it help users to direct towards taking certain actions?
The chatbot metrics help in tracking the bot’s effectiveness and continuously improve its performance.
#2 – Understand Customer Satisfaction
One of the most important reasons chatbot metrics are essential for your business growth is it helps deliver customer satisfaction results. Does your virtual assistant agent, such as a chatbot, have a positive impact on getting conversations? With this conversational AI analytics, you can understand how much your customers are satisfied after interacting with the bot. Are the responses serving their purpose and they return to your brand?
#3 – Measure Business ROI
57% of businesses believe that chatbots deliver higher ROI (Return on Investment) for minimal effort. Chatbot analytics helps businesses determine the KPIs such as total leads generated, complete issues resolved or cost per problem. It will help you to compare the chatbot’s performance with other channels. On top of that, chatbot analytics enables you to identify the queries your business gets over live chat. Then, filter the simple questions for your chatbot. To handle these queries and similarly annual costs, you can calculate the estimated time.
Now, it’s time to know about metrics that are used for measuring the performance and success of chatbots:
How to Measure Chatbot’s Performance & Effectiveness?
Chatbot Analytics Metrics
If you want to know the performance and effectiveness of your chatbot, you need to use chatbot metrics, including several parameters. The top 4 metrics are mentioned below for measuring the performance and effectiveness of chatbot.
Chatbot Analytics #1 – User Metrics
After understanding how a chatbot works, you can measure its performance with the aid of chatbot analytics and metrics. You can use this KPI to track the response time, conversion rate, and efficiency to improve the chatbot’s performance significantly.
The key metrics you need to evaluate on the user’s side are as follows:
# 1 – Total Users
The total number of chatbots users is one of the main KPIs you should analyze. It helps capture insights on how many customers are using your chatbot. With this, you will also get to know the overall impact and chatbot success. You can categorize your chatbot users into three categories such as active users, engaged users and new users.
#2 – Active Users
Active users who have repeated conversations with your chatbot regularly. The active user means your users are happy using your bot and keep returning to your business.
#3 – New Users
The users’ number helps businesses get real-time information into the chatbot’s overall popularity, which is a great parameter of its success. New users are your prospective targets and how your bot helps them and they are ready to converse again.
#4 – Numbers of Bot Sessions Initiated
The chatbot metric determines the interaction between users and chatbot. It monitors and provides insights into its ability to engage in a conversation. The number of interactions indicates accurately how far down the conversational decision tree the user made. So, assessing whether the sentiments of this KPI are positive or negative is not an exact issue.
#5 – Bounce Rate
Bounce rate means total visitors who visit web pages and leave without interacting with your chatbot. In simple terms, people who navigate away after visiting just one page on your website. Having a high bounce rate means there is something wrong with your website design or content. It impacts the customer experience thanks to chatbot KPI.
Chatbot Analytics # 2 – Conversation Metrics
With the growing popularity of chatbot trends, many businesses have successfully used chatbots, while others have failed in this race. What is the main reason? To ensure that the chatbot you have invested in becomes a success or failure, you need to use chatbot conversation metrics to measure accordingly.
Key Metrics for Chatbot Conversational Analytics:
#1 – Human Interaction Vs. Chatbot Interaction
Human interaction is one of the vital chatbot evaluation metrics that measure your bot’s success. Even 86% of customers believe that choosing an agent option is a great idea when using a chatbot. Chatbot analytics help businesses to know whether the customers are happy with a bot or not. If the ratio of human handover is increasing, it’s better to turn to live chat and use the bot for gathering initial customers’ details.
#2 – Conversation Duration
The conversation duration measures the length of interaction between users and chatbot. Monitoring this KPI helps to measure the effectiveness of the chatbot. It represents if the bot is capable of having relevant conversations and keeping the user engaged or not. Ideally, the chat duration relies on the use cases and the conversation context. Short chats show that something is not working and need to be fixed. Having the number of steps per conversation is an essential metric to reach the target audience.
#3 – Interaction Rate
Measuring interaction rate KPI is the best way to measure user engagement during a conversation with your chatbot. It helps measure the average number of messages exchanged during the user chatbot conversation. The higher the interaction rate, the higher will be the effectiveness of chatbots. If your interaction rate is lower, a chatbot is not a wise decision. And you need to go for new strategies to engage users.
Chatbot Analytics# 3 – Customer Satisfaction Metrics
Customers are increasingly demanding real-time communication and businesses are making customer satisfaction their main focus. Measuring chat’s impact on overall customer satisfaction is essential if you want to implement a chatbot to deliver an excellent customer experience. CSAT & NPS surveys show businesses can ask the user to rate the conversation with this bot.
Here are some key metrics to determine customer satisfaction:
#1 – Retention Rate
The chatbot KPI helps record the user’s percentage who return to use a chatbot for their interaction within a specific time. The more frequently the audience returns to your chatbot, the greater your retention rate. In terms of significance, retention rates depend largely on essential metrics. How to increase your bot retention rate? Here are some ways you need to follow:
- Offer your customers a discount as per their behavior
- Deliver hyper-personalized conversations
#2 – Satisfaction Score
The customer satisfaction KPI estimates the user satisfaction level with chatbot conversation. At the end of the conversation, your chatbot can ask users a simple objective question about their experience. Also, there are many ways to indicate satisfaction or dissatisfaction with the chatbot, including star ratings or offering emoticons with different expressions. It is vital to get customer feedback as businesses will identify the errors in the bot conversation flow and improve it.
Defining an objective or goal is an essential step in building a successful chatbot. By using these metrics and KPIs, you can measure the performance and effectiveness of chatbots. For better results, you can get in touch with a chatbot development company – Appventurez.
How can Appventurez Help in Chatbot Analytics?
If you are looking for the best-in-class bot development solutions for better interaction with your customers, Appventurez is the right choice! As a reliable mobile app development company, we provide you with a no-code development platform for creating your chatbots without coding a single line. We can design your chatbot with various striking features using the right chatbot builder. We also add the analytics feature with your chatbot, which can provide insights into customers’ experience with your chatbot.
For more information about chatbot analytics and metrics, contact us today!
A technocrat and an entrepreneur who is the Co-Founder and Chief Delivery Head at Appventurez. Skilled in object-oriented programming (OOP), iOS Development, Design Principles, Data Structures, and Swift, he has served different organizations as Tech Lead.
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