Learn with Engati analytics, from where it began to where it goes
We have all heard how chatbots are going to change the internet as we know it. Many of us want to build great conversational bots that will engage in a way never before. But there is one big problem, the bots are very new, no one surely knows how to design a great conversational flow which will work perfectly. That means we need a lot of iterations and experimentation to nail it, bringing us to the question, “How do you know what is working and what is not? How do you measure it? Are there any analytics tools?"
At Engati, we have tried to arm you with a bunch of very useful metrics. We help you improve the human-bot interaction by measuring customer engagement and retention, observing the conversation, identifying bottlenecks and identifying and improving the content quality. I will try to explain briefly what these metrics areand how they can help you.
The Analytics Dashboard
The Analytics Dashboard provides the bot admins with a quick overview of how the bot is performing across the various platforms configured. The fundamental unit we use is called 'interactions', which is single to and fro communication between the bot and user. There is another metric that is used often is 'conversations,' a metric that's very similar to the idea of 'session' but for bots.
The analytics dashboard shows you 4 different metrics -
- The first analytics metric is straightforward, it gives you the total number of chatbot conversations.
- Average Conversations per user is going to give you an idea of how successful your content is, in bringing back the user to the bot. It's a very crude measure of retention.
- The next two analytics metrics will give a measure of how engaging your bot content is. 'Average conversation length' and 'the number of messages per conversation' tells you for how long an average conversation in a bot goes on and how many interactions the end-user has had with it.
- With good engaging content, your bot's conversation should last longer and more messages will be exchanged through analytics.
The two infographics are focused on new users coming in and the active users on the bot. There are filters like weekly, monthly, and daily. Analytics gives a clear picture of the user acquisition and user retention trends over the past month, during which day of the month and the week are we getting more users and active users. With analytics, we can also track the dip or increase in user acquisition and active users and correlate them well with the changes made in the bot in and around that period.
The individual user screen comes when you click on a specific user on the users tab. It helps you drill down to a specific user and do a one to one chat when needed. The screen provides you some basic user specific details, which might come in handy during a conversation with the user, and also a very detailed conversation history of the user. With analytics, the conversation history helps you to catch up quickly with the context of the conversation that the user had with your bot. It will also help you identify bottlenecks in your bot content which are probably driving users away or causing a negative experience for end users.
Engage and Retention
The 'Engage' tab provides a nice overview of what your users are doing on your bot. What are they clicking and what are they searching for? It tells you which of your conversation tracks /paths are popular among the users and taken mostly. Using these analytics stats you can figure out the bottlenecks in your conversation flow where things are fizzling out.
The cohort graph on the 'retention' tab provides insights on retention of your users, what percentage of people are revisiting the bot within a fortnight of the first visit? Using it you can make inferences on user behaviour and find correlations with changes in the bot's content and make improvements accordingly.
This tab is there to help you train your bot based on the feedback received from your customers. Your faq bot won't have all the information from the word 'go.' However, you can always come here to feed the correct information. Using train in analytics, you can add missed queries and help the bot AI to find better answers in the future. We also provide you with handy filters and text search.
Customer support and Live chat dashboard
Our live chat module allows you to incorporate the human touch while engaging with customers. It’s the perfect blend of automation and humanity.
It allows you to answer the most complicated questions in real-time with smooth and seamless transitions between bots and agents. It even allows you to intelligently route conversations among agents, depending on their skills and expertise.
Read more about live chat.
All the analytics metrics and data on these two screens will make life easy for your support agents. On screen you get the data that triggered the customer support request. And once it takes up the request, you will be redirected to the chat screen. It provides the same information you get in the analytics user details screen. This will help you have a meaningful conversation and resolve the issue. The Customer Support Analytics Dashboard also provides a quick overview of many things. For example- the requests received, pending, handled and missed by the Customer agents.
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