Chatbot Analytics: 10 essential metrics that you can’t ignore
Table of contents
We live in an era where real-time customer support has become a bare minimum. Customers want quick answers, ideally as soon as they reach out to you. One cost effective way of addressing these needs is through chatbots.
You’d have observed many businesses – be it in retail, healthcare, finance or hospitality – use chatbots on their website or mobile app for handling customer interactions. It’s definitely a faster way to give customers what they want, when they want.
But that’s just one side of the story. There’s enough precedence on how chatbots can go off script and frustrate – at times, even offend – customers. Such experiences can tarnish your business’s reputation and even negatively impact your bottom line.
So, how do you ensure that your chatbots don’t err in customer interactions? That’s where it’s important to continuously track the effectiveness of your chatbot. This can be done through analytics. Let’s find out how.
Table of Contents
- What are chatbot analytics?
- How do chatbot analytics enable data-driven decision making?
- Top 10 metrics you cannot ignore in chatbot analytics
- Maximizing Chatbot Success with Essential Analytics
What are chatbot analytics?
Chatbot analytics refers to tracking data from all the interactions between users and chatbots and interpreting them to optimize your customer experience workflows. Monitoring chatbot analytics can help you refine your chatbot’s responses, streamline issue resolution, track user sentiment, and find ways to personalize support.
How do chatbot analytics enable data-driven decision making?
Chatbot analytics give you actionable insights on user interaction and the effectiveness of the bot. These insights can be used by businesses to make data-driven decisions to optimize customer service, drive conversion rates, and improve operational efficiency.
Read on to get a quick overview of how monitoring chatbot analytics can help you make data-driven decisions.
- Behavioral data: Track user interactions, commonly asked queries, and popular keywords. Understanding this data helps you pinpoint customer interests and preferences. This allows you to tailor chatbot responses and recommendations to align with what the user wants.
- Sentiment analysis: You can analyze the intent behind queries to understand the customer’s tone. If a customer expresses negative sentiment on a specific topic, it could indicate a problem with your service.
- Response times: Monitor how quickly your chatbots respond to customers. Delayed responses can indicate a problem with the chatbot’s workflow. For instance, the bot may not be able to accurately identify the user intent in a query. This indicates a problem with the chatbot’s NLP capabilities which needs to be addressed immediately.
- Customer service patterns: Chatbot analytics can help you uncover repetitive patterns in customer service. Let’s say you notice a high volume of queries during a specific time period or a large proportion of customers raising tickets for similar issues. Understanding this data can help you identify peak hours, adjust staffing levels, and even create additional help resources around a certain topic.
- A/B testing: Use chatbot analytics to measure the impact of different versions of scripts or workflows. This can help you narrow down the most effective workflows and optimize your bot to maximize user engagement.
Top 10 metrics you cannot ignore in chatbot analytics
Here is a breakdown of the 10 essential chatbot metrics you should track and how it can help your business.
1. Average chat duration
The average chat duration measures how long a user interacts with the chatbot during a single session. To calculate it, you need to take the sum total of the time duration of all chats over a specific period and divide that number by the total number of sessions during that period.
Average chat duration = Total number of chat durations
———————————
Total number of sessions
Here’s why you should track average chat duration:
Get insights into how well users engage with the chatbot. A longer chat duration could indicate that customers need to go through multiple steps before they get their queries resolved, suggesting that your conversation flow needs to be improved.
For instance, say a retail business uses chatbots to handle common queries around order status, delivery updates, and returns. On tracking the average chat duration, the business finds that each chatbot session takes around 8 minutes to close. This is slightly longer than the time needed to resolve simple queries.
Further analysis revealed that sessions were taking longer because users were asking multiple follow-up questions as the initial responses offered by the chatbot were not very clear. The business uses these insights to make improvements to the chatbot script and offer more contextual responses.
2. Goal completion rate
This refers to the percentage of conversations in which users successfully completed a particular goal. Goal completion rate can be calculated by dividing the number of successful sessions by the total number of sessions during a certain time period.
Average chat duration = Number of goals successfully completed
——————————————- x 100
Total number of sessions
For instance, say a travel company implements a chatbot on their website to help users book a trip. Now, if the chatbot had 300 sessions over a week out of which 90 customers successfully booked a trip, you could say the bot had a goal completion rate of 30%.
Here’s why you should track goal completion rate:
If you track goal completion rate, you get a very good idea of how effectively your chatbot is achieving its intended purpose. The goal can be defined as lead generation or query resolution. A low goal completion rate tells you that users could be facing a problem with the chatbot and abandoning their interactions altogether. In such cases, you’ll want to go back and make data-driven adjustments to your chatbot workflow to improve the user experience.
For instance, a bank has a chatbot that is designed to help users open an account. It has a goal completion rate of just 15%. This was much lower than the expected GCR of 35%. On analyzing further, the bank discovered that customers were getting frustrated at the identity verification step due to lack of clear instructions and a complicated
To fix this, the bank simplified the document upload step as part of the chatbot experience. A month after these changes, the goal completion rate of the chatbot went up to 25%file upload process.
3. Total number of conversations
This simple metric gives you the total number of conversations a chatbot has with your users. Although it is a broad indicator, it does give you a brief idea of your chatbot’s reach and whether users are engaging as intended.
Total Number of Conversations= ∑ (Individual Chat Sessions)
Here’s why you should track total number of conversations:
The total number of conversations is a baseline for measuring chatbot performance. A low number of interactions indicates that users may not be aware of your chatbot due to poor positioning on your website or app. It may also indicate that the chatbot has usability issues or technical problems like slow loading time. Conversely, a higher number of conversations shows that users find the chatbot easily accessible.
For instance, a healthcare organization deploys a chatbot to answer patient queries and help with appointment bookings. They notice a sharp incline in chat sessions around the flu season, indicating high demand for vaccination bookings, flu information, and prevention tips during this time.
In response, the healthcare provider updates the chatbot with information that specifically addresses flu symptoms. The provider also equips the chatbot with a symptom checker to improve patient support during flu season. They also add a few staff to man the chatbots so they can step in and offer human assistance whenever necessary.
4. Human takeover rate
The human takeover rate essentially measures the number of chatbot conversations that are escalated to a human support staff. It is calculated by dividing the number of conversations where an agent took over by the total number of conversations.
Human Takeover Rate = Number of Conversation Escalated To Human Agents
—————————————————— x 100
Total Number Of Conversation
Here’s why you should track human takeover rate:
If you notice a high volume of chatbot to human transfers, it could be because the chatbot’s natural language processing capabilities are not functioning properly. This means the chatbot is not able to understand the user’s query. A high volume of transfers initiated by users could also indicate that the chatbot is ineffective in delivering satisfactory resolutions. But on the other hand, if the human takeover rate is on the lower end – with transfers being initiated only for complex queries – it indicates that the chatbot is functioning well.
For instance, say an ecommerce company uses a chatbot to handle enquiries related to payment, billing, and refunds. On measuring the human takeover rate, they find that 40% of all queries are escalated to agents. Further analysis proved that most of the escalations happened because the chatbot was not able to address queries around payment options.
The company decided to integrate the chatbot with their knowledge base created for addressing FAQs around payments. They also trained the chatbot to recognize keywords related to billing and payment related queries and route users to the relevant sections in the knowledge base. Not only did this reduce the burden on human support staff but it also streamlined the purchasing process for customers
5. Retention rate
This is a measure of the number of users who return to engage with the chatbot after an initial interaction. It could be an indicator of your chatbot’s effectiveness in engaging customers and delivering helpful responses. Retention rate is calculated by dividing the number of returning users by the number of unique users measured over a specific period.
Retention Rate = Number of Returning Users
———————————- x 100
Total Number of Unique Users
Here’s why you should measure the retention rate:
The retention rate is a reflection of your users’ satisfaction with the chatbot. A high retention rate shows users are deriving long-term value out of your chatbot and they are likely satisfied with their interactions. On the other hand, a low retention rate may indicate a problem with the chatbot’s functionality. The chatbot may not be providing relevant responses or it might not be offering a smooth user experience. It signals a need for businesses to analyze why users don’t return and optimize engagement strategies.
For instance, a financial services company implements a chatbot on their website to help customers with queries related to budgeting and transactions. While assessing the chatbot’s effectiveness the company found that only 20% of users return to use the chatbot a second time. They analyzed the chat logs and user feedback and found that the chatbot offered generic finance advice whereas users preferred a more personalized approach for their budgeting needs.
The company used this feedback to improve their chatbot workflow by adding personalized budgeting tips based on the users’ spending patterns. They even added a functionality for users to set reminders about their upcoming credit card bills so that they don’t fall behind on their payments. A few months later the chatbot’s retention rate increased to 50% indicating that a higher number of users were finding the bot to be useful.
6. Response time
Response time is one of the most important chatbot metrics you need to track. As the term suggests, it tells you how long it takes for a chatbot to respond to your user’s query. It helps you understand your chatbot’s efficiency and how it contributes to the user’s experience. You can calculate response time by adding the time taken by the chatbot to send out a response to every message and dividing it by the total number of responses.
Average Response Time = Total Time Taken to Respond to All Messages
———————————————–
Total Number of Response
Here’s why you should track the average response time:
Tracking the average response time gives you an idea of how efficiently your chatbot is handling customer interactions. A shorter average response time indicates that users are receiving quick replies from your chatbot and they are more likely to return to the bot for further interactions.
For instance, a retail company deployed a chatbot on their website to manage common customer queries. However, they notice an increase in customer complaints about delays soon after they’ve implemented the chatbot. On checking the average response time, they notice that the chatbot takes over 30 seconds to respond to customer queries. They refine the chatbot’s infrastructure and update it with response templates so customers receive timely assistance. Within a few weeks, the company notices that response times have decreased to 5 seconds. There’s also a gradual decline in customer complaints
7. Fallback rate
This metric is a measure of the number of conversations in which the chatbot fails to understand a customer’s query. In cases where a chatbot fails to understand the query, it delivers fallback responses like ‘I’m sorry I did not understand that..’ or “I’m sorry, I didn’t quite catch that. Could you rephrase your question?”.
It is calculated by dividing the number of such fallback responses by the total number of interactions over a period.
Fallback Rate = Number of Fallback Response
——————————- x 100
Total No of Users Interactions
Here’s why you should measure the fallback rate:
Monitoring the fallback rate helps you identify gaps in the chatbot’s algorithm and improve the bot’s capabilities. If a chatbot has a higher fallback rate, it could mean that the bot needs more training. It could also indicate that your conversation flow needs to be improved.
For instance, a retail company realized that their website chatbot showed a high fallback rate when customers raised queries on specific products. This included information on product availability, specifications, and price. To combat this, the retail company decided to integrate the chatbot to its product information management (PIM) system and made improvements to the bot’s NLP algorithm to understand customer queries better.
Through these improvements, the chatbot was able to understand product inquiries better and displayed relevant information that was directly sourced from the PIM system. This reduced the fallback rate and ensured the chatbot could handle more queries without escalating to a human agent.
8. ROI period
The ROI period is a measure of how long it takes for a chatbot to drive financial returns to a company. This ROI can come in the form of cost savings or increased revenue. The ROI period is calculated by dividing the investment in the chatbot by the net return of the chatbot over a month or a year.
ROI Period = Total Investment in Chatbot
———————————————
Monthly ( or Annual ) Net Return from Chatbot
Here’s why you should measure the ROI period of a chatbot:
Measuring the ROI period is important to ensuring that the chatbot brings in tangible benefits after its implementation. It gives you insights into how effectively your chatbot is complementing your customer service function and whether implementing the bot was a good financial investment. A higher ROI period may indicate that you need to make some adjustments to the chatbot
For instance, a financial services company implemented a chatbot to handle FAQs and reduce the burden on support agents. By calculating the ROI period, the company realized they could recover their $80,000 investment within six months, primarily through labor cost savings and increased efficiency in handling support inquiries. Tracking the ROI period allowed them to quantify the chatbot’s impact and see the value it brought in reducing workload for human agents, enabling them to focus on more complex issues.
9. User sentiment
User sentiment refers to the emotions or underlying tone present in customer or prospect messages when they interact with a chatbot. It can be tracked by integrating your chatbot to sentiment analysis tools that use NLP to identify emotions in customer responses. These tools track certain keywords, emojis, and the context of interactions to gauge whether the customer sentiment is positive, negative, or neutral.
You can track user sentiment using:
- Sentiment analysis tools
- Taking customer feedback
- Tracking emotion-based keywords in conversations
Here’s why you should track user sentiment:
Understanding user sentiments is key to monitoring how customers feel during their interactions with the chatbot. Negative sentiments can indicate customer frustration and it may be because the chatbot does not respond accurately to queries. You can set up an automation where if a chatbot detects a negative customer sentiment, it triggers a human takeover so that the issue at hand is resolved without further escalations. This also reduces the risk of customer frustration.
For instance, a telecommunications company used sentiment analysis and found that users expressed frustration when the chatbot handled complex billing inquiries. By tracking these negative sentiment trends, the company identified a gap in the chatbot’s ability to provide detailed billing explanations. In response, they enhanced the bot with more comprehensive billing responses and implemented a protocol for quicker escalation to human agents when the sentiment turned negative. This improvement led to a noticeable drop in customer complaints and increased user satisfaction.
10. Response accuracy
Response accuracy is a measure of how frequently a chatbot is able to deliver accurate and effective responses to customer queries. You can calculate response accuracy by dividing the number of accurate responses by the total number of responses.
Response Accuracy = Number of Accurate Response
————————————– x 100
Total Number of Response
Here’s why you should track response accuracy:
When a chatbot responds accurately, it reduces the need for human intervention. It also helps businesses identify specific areas where the chatbot needs further training or adjustments, enabling continuous improvement over time. Monitoring and improving response accuracy helps ensure the chatbot serves as a reliable customer service tool.
For instance, a healthcare company implemented a chatbot to assist with patient inquiries, including appointment scheduling and medication information. Initially, the chatbot had a response accuracy of around 60%. By tracking and improving response accuracy—focusing on frequently asked medical and scheduling questions—the company increased the chatbot’s accuracy to 92%. This enhancement led to higher user satisfaction, as patients received correct information more consistently, reducing the need for human follow-ups and allowing the support team to focus on more complex issues.
Maximizing Chatbot Success with Essential Analytics
Chatbot technology has advanced a lot in recent years. But the buck doesn’t stop at just investing in a bot and setting it up. That’s not going to magically elevate your customer support quality.
There’s a lot of tweaking and tinkering you need to do so that the chatbot works the way you want it to work. And in order to know what to change, you need to track chatbot performance diligently.
That’s where the analytics comes in. It gives you visibility into how you can make your bot more effective. By ‘more effective’, we mean a bot that can offer accurate responses, knows when to escalate queries, and in general, provides a good user experience.
Tracking metrics like response accuracy, user sentiment, fallback rate and goal completion rate are so important. They tell you where the gaps lie in your bot workflows and how you can bridge those gaps, so that you can continuously improve the quality of support you deliver.
