Chatbot Analytics: 5 Essentials to Track to Guarantee Chatbot Success


Share on LinkedIn

So, you’ve invested in automating your customer service with a chatbot. You know that chatbots have become an increasingly popular way for businesses to improve their customer service and you know your customers want to engage with simple and efficient customer support.

However, it’s not enough to simply set up your chatbot and hope all goes well with your customer touchpoints. It’s important to test your conversational experiences to learn what’s working, or what isn’t.

Whether you provide Software as a Service (Saas) or eCommerce, you should use analytics to track data regarding the performance of your chatbot. Plus, retrieving this data alone is not much use. You should also know how to interpret it to provide valuable insights that can then be used to improve the functionality of your chatbot and, consequently your customer’s experiences.

Why Should We Use Chatbot Analytics?

Chatbot analytics are an ongoing evaluation of your chatbot’s effectiveness. Key performance indicators (KPIs) should be regularly collected and evaluated to provide actionable insights into whether your chatbot is helping you achieve your objectives. That data can then be interpreted to help you measure, adapt, and improve the following:


It’s important to continually train and improve the functionality of your chatbot. You can only do this by using analytics to determine if your chatbot is assisting visitors rather than disappointing them.

Natural language understanding (NLU) for chatbots helps them to recognize words, but you need to program the chatbot so it can answer the most frequently asked questions for your business, and this will vary from industry to industry.

For example, if you offer guidance on eCommerce pricing strategy, your customers will have very different questions compared to the customers of companies that sell disposable paper products.

You need to know what the frequently asked questions are and monitor if they change so you can adapt your chatbot to ensure it offers relevant answers.

If you are a software company that modernizes a legacy banking system, you need to ensure that your chatbot provides accurate information for a precision-driven audience. Out-of-date or incorrect information will only frustrate your users and damage your brand.

Customer Service

You set up your chatbot to improve customer experience. But without analytics, how would you know if your customers are using the bot, engaging with the bot, or if the bot helps them resolve their problems?

If customers are dropping out of a conversation with the bot or choosing to speak to a human instead, then your bot is not providing the level of service they require.

Customer Satisfaction

You cannot determine whether your chatbot is effective unless you can measure its impact on customer satisfaction.

Customers should feel that your bot adds value to your service. Customer experience KPIs will help you determine whether you need to improve your chatbot’s performance.

Free to use image from Pexels

Return on Investment (RoI)

Including a chatbot as part of your customer service strategy requires investment. New companies that invest in sales enablement tools, integrate the bot, train the bot and employ someone to oversee its management are taking a significant risk. Regardless of your company’s size, you need to establish whether the investment was worth it.

Chatbot analytics enable you to measure lead generation, the number of issues resolved and the handling costs per issue, then compare performance with alternate channels.

If you design stock monitoring software, and you offer chatbot and web chat customer service options, you could use analytics to establish the estimated times it took to handle any queries in each channel and calculate annual handling costs, then compare the performance and value of each.

5 Essential Chatbot Analytics and How to Use Them

Chatbot analytics are important for measuring performance and provide valuable insights which can help inform planning.

As mentioned above, getting the data is easy, but you also need to be able to interpret and use it effectively or it’s useless.

Here are 5 chatbot analytics and how to use them to help you evaluate performance and apply your findings to achieve your objectives.

1. User Numbers

In simple terms, this KPI helps you to track the number of users who interacted with your chatbot. You can also break this down to help you determine total users, engaged users, and new users.

Engaged users are users who repeatedly interact with your chatbot. For example, users of Discord bots who continually return to listen to music in the background while they work.

How to use it:

Knowing the total number of users who interact with your bot, especially those who regularly engage in conversations to help resolve their issues, will help you determine any RoI.

If you see an increase in users then your chatbot is doing well, and you could potentially invest in enhancing its capabilities to handle even more issues.

If there’s a decrease, then your bot is not engaging your users effectively and you may need to adjust your strategy.

A high number of new users that don’t engage or return could mean that users are unsatisfied with the bot’s performance. Alternatively, a high number of returning users, but not so many new users could mean that you need to promote the use of your bot or improve visibility.

2. Missed/Failed Engagement

Your bounce rate is a significant KPI and indicates the number of users who may initially engage with the bot and then stop. If it’s high, you have a problem.

How to use it:

High bounce rates are usually associated with poor functionality. Perhaps your bot has not been programmed to answer that question, or it may be a new question that has not been asked before.

Continuous monitoring of the conversations with your chatbot can inform the training of your bot and keep it up to date with the information it needs to resolve a customer’s issue. 

Regular checks and cleanouts will help you to optimize performance, ensuring your bot can provide the latest information on the relevant issues and thereby reduce the bounce rate.

3. Interaction Rate / Chat Volume

Long conversations mean high customer engagement. It’s important to track the number of messages exchanged between a user and a chatbot during an average interaction.

How to use it:

High interaction rates mean users are actively engaging with your bot, so it’s fulfilling its purpose effectively.

If the interaction rate is low then you may have a technical problem like a glitch, and you should speak to IT support or your chatbot provider.

If not, then re-examine the chat script. You should design conversations that are helpful as well as conversational.

Poor visibility can lead to low interactions. If you offer a chatbot for your latest QA test plan it’s a good idea for your chatbot to pop up on your website and introduce itself to your visitors. If visitors don’t know the bot exists, they won’t use it.

Free to use image from Pixabay

4. Goal Completion Rate

It is only possible to set up a chatbot if you establish the goals you wish it to achieve beforehand.

Your goals will be specific to your industry. For example, a SaaS company that provides a remote work tech stack will likely have a chatbot objective to increase the number of free-trial sign-ups. On the other hand, banking chatbots may be used to promote personal loan applications.

How to use it:

If your goal conversation rate is low, your chatbot is still working.

Review the script to make conversations smoother. Is your bot throwing too much information at the customer? This can be confusing and will not achieve the end goal, be it sales or newsletter sign-ups.

Perhaps you need to re-evaluate your goals. Maybe you provide services to new startups, and you want to promote a feature about how to start a business call center on your website. There’s no point in measuring newsletter sign-ups when you want to measure visits to that webpage.

5. Human Takeover Rate

This KPI tracks how many times users opt-out from conversing with the chatbot and request to be transferred to a human.

It could be because they don’t like chatbots, the chatbot has misunderstood their request, or the bot simply cannot provide them with the level of detail they require to resolve their issue.

How to use it:

High human takeover rates may simply mean your customer base prefers human interactions and the automation of your customer service may not be worth the investment. In this case, you could consider scaling down your bot interaction to basic user questions before issues are handed over to the human operative.

The other option may again be down to the bot script. Users may ask to transfer to a human because the bot’s scope of answers is just not wide enough. Continuous user testing and bug testing are important to help establish performance so scripts can be adapted and any bugs eradicated that may have a detrimental effect on the flow of conversations and, therefore bot engagement.

Free to use image from Pexels

A quick recap…

Setting up a chatbot to help automate your customer service strategy is an increasingly common approach to help meet customers’ demands for convenient and simple customer orientation.

However, you cannot establish if your chatbot is meeting the needs of your customers, or your company objectives, without analytics.

Depending on your industry, you must set clear objectives for your chatbot and choose the right KPIs that help you measure its effectiveness. Only then can you identify any issues that may require reevaluation or improvement to help inform your chatbot strategy and ensure customer satisfaction. 


Please enter your comment!
Please enter your name here