Building and deploying are only the first step of building an AI chatbot – after deployment, you’re ready to monitor your project with chatbot analytics.
Any real chatbot or AI agent project requires the tracking of metrics to observe and improve its performance.
With several years of experience deploying enterprise chatbots, our team is well-versed in monitoring and reiterating on successful chatbot deployments. Not making the most of your chatbot’s analytics is one of the common mistakes companies make when deploying a chatbot.
Here’s a quick guide to get you started with chatbot analytics.
What are chatbot analytics?
Chatbot analytics are data and metrics that track how users interact with a chatbot. They help measure performance, identify issues, and optimize the bot’s effectiveness over time.
They involve tracking, measuring, and analyzing the performance and interactions of a chatbot through chosen metrics.
These analytics provide insights into how users engage with a chatbot, its effectiveness, and its overall impact on business goals.
Why should I measure chatbot analytics?
No matter the project, you need to measure your AI chatbot’s analytics.
An AI chatbot project has 3 stages: build, deploy, and monitor. The backbone of the monitor stage is measuring meaningful chatbot analytics and reiterating on your chatbot.
Proper monitoring is essential to a successful chatbot deployment — tracking analytics allows you to know which areas your bot has room for improvement and where it’s delivering the most ROI.
How to Measure Chatbot Performance: Step-by-Step
1. Define the chatbot’s goals
Start by identifying your chatbot’s purpose. What specific outcomes do you want? A customer support chatbot and a lead generation chatbot will have wildly different goals than an HR chatbot.
Common goals include better customer support, AI-enhanced lead generation, sales support, or increasing user engagement.
2. Tie goals to KPIs
Then you can select KPIs that reflect your goals:
If your goal is better customer support, your KPIs could be a resolution time of under 2 minutes, a ticket deflection rate of at least 40%, and a customer satisfaction score of 85%+.
If your goal is lead generation, your KPIs might be generating 50 qualified leads per week, or a lead conversion rate of 20%.
3. Monitor metrics that align with your KPIs
Next, you can identify which specific metrics inform your KPIs.
For example, metrics about user engagement will be tied to:
- The number of return users
- Whether they engage with the chatbot’s product recommendations
- How many overall website visitors are using the chatbot
4. Tie metrics to monetary values
To understand the bottom line of your chatbot investment, you need to quantify its impact.
For example:
- If the chatbot reduces support tickets, calculate how much you’re saving in labor costs by resolving queries automatically
- If increasing lead generation is a goal, calculate the average revenue per lead and multiply it by the number of leads the bot generates
This step is a key part of calculating a chatbot’s ROI.
5. Reiterate and improve
Monitoring chatbot analytics is an ongoing and evolving process.
Review your chatbot’s performance regularly. Analyze the data to identify patterns, such as high drop-off points, common errors, or inefficient resolution paths.
As your chatbot evolves – with new features, or an expansion of use cases – you’ll need to adapt and expand the metrics you track, alongside your KPIs.
9 Chatbot Metrics to Track
1. Number of interactions
One of the most important metrics is the most basic: are people using your chatbot?
If not, your team needs to signpost better, or make the chatbot a more necessary step of the process (i.e. employees can only schedule vacation days through the chatbot, instead of giving them the option to schedule through an HR representative or the chatbot).
2. Average chat duration (both length of time and number of messages exchanged)
The ideal chatbot interaction is efficient and helpful. If interactions are taking too long, try to identify and reduce bottlenecks.
3. Number of flows initiated
Does your chatbot identify and solve the problem immediately, or does it cycle through multiple flows to find a fix?
4. Number of repeat flows
If your chatbot repeats the same flows, it’s a sign of inefficiency. It may be because your chatbot doesn't properly recognize the user's need from the get-go.
5. Chatbot containment rate
The chatbot containment rate refers to how many users interact with your chatbot and complete the interaction without needing to interact with a human.
A successful chatbot can see a containment rate of ~65%, as there will always be interactions that need human assistance.
6. Number of repeat users
If your chatbot is useful, you should see return users.
7. Number of active users per time period
Knowing what times users interact with your chatbot can help with decisions about shift scheduling for live agents.
8. CSAT (customer satisfaction score)
Direct feedback is an easy way to measure the effectiveness of your chatbot.
9. Average response time
If your chatbot has the goal of reducing wait times for your customers or leads, make sure to track how long customers need to wait to speak with a human agent.
If your chatbot is doing its job, it should significantly reduce the waiting time.
How to use advanced chatbot analytics
The best chatbot platforms will allow you and your team to set up custom metrics to track chatbot analytics.
Custom analytics requires identifying high-value actions and instructing your chatbot to track them.
For example, Botpress allows subscription users to track any event they add a ‘Track Event’ card to.
These kinds of advanced analytics allow users to track hyper-specific events. For example:
- How often a bot isn’t able to answer a question using its Knowledge Base
- How often users interrupt a bot during an interaction
- How often an e-commerce chatbot fails to make a payment
- How often users abandon a chatbot, specific to certain times or flows
- How often users engage with the products a chatbot recommends
- How often a chatbot upsells or cross-sells a product or service
Advanced analytics allows your team to identify room for improvement with pinpoint accuracy.
By understanding how users engage with every part of your chatbot’s flow, you can endlessly optimize the process for better and better results.
What to look for in an analytics dashboard
There are plenty of options for chatbot anlyatics platforms. Most chatbot platforms will come with their own analytics dashboards, but you can enhance these with analytics add-ons. These are also useful for open source chatbot analytics.
When looking for high-level chatbot analytics platforms, here are a few features to look out for:
Real-time monitoring
A key feature of an advanced chatbot analytics platform is the ability to track performance in real time. Not only does it allow your team to see the latest data, but they can respond quickly to issues or anomalies.
For example, you can set up real-time alerts for problems, like an unusual or a decreased containment rate.
Integration with business systems
The ability to seamlessly export your chatbot’s data into data visualization and BI tools – like Tableu or Google Analytics – allows you to easily share insights across a team (without the need for everyone to log into your chatbot platform).
Customizable metrics
Customizable metrics – or ‘advanced analytics’ – will allow your team to hone in on specific parts of your chatbot’s flow.
Deploy a chatbot next month
With years of experience deploying AI chatbots and AI agents, Botpress is the leading chatbot platform for enterprise and solo builders alike.
The endlessly extensible and customizable platform allows bot builders to construct chatbots for any use case, across any industry.
Deploy seamlessly on a variety of channels and platforms with a pre-built library of integrations.
Learn how to build an advanced chatbot with an extensive educational library and an active Discord community of 20,000+ bot builders.
Start building today. It’s free.
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