Chatbot Analytics: 15 Core Metrics to Track

Bhavna Gupta

February 12, 20248 min read

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Cliched it may sound, but it’s true - You can’t improve what you don’t measure.

Chatbot analytics allows brands to measure – and eventually improve – their chatbot performance, giving real business benefits in the form of improved CSAT and cost efficiency.

If you’re drowning in the flood of conversational data and can’t figure out what metrics matter, this article is for you. We are going to identify critical chatbot analytics for you and arm you with their definitions and optimization tips to get you started. Let’s get started.  

Table of Contents

What are chatbot analytics, really?

Chatbot analytics involves the collection, analysis and interpretation of data generated by interactions between users and chatbots. These analytics provide insights into various aspects of chatbot performance, user behavior and the overall effectiveness of the chatbot implementation.

Chatbot analytics enable organizations to make data-driven decisions, optimize the chatbot's performance and enhance the user experience. By tracking these metrics, businesses can identify trends, uncover areas for improvement and ensure that the chatbot aligns with organizational goals and user expectations. 

While there are tons of chatbot metrics one can measure, we will restrict to business-critical analytics that impact the user experience and influence your bottom line. Here we go. 

15 Important chatbot metrics to track

 Chatbot analytics can be grouped into four main buckets: 

I. User metrics

Topping chatbot analytics are user metrics that quantify the user base of your chatbot, including the total number of users, active users, engaged users, new users as well as user sentiment. Let’s discuss each of these chatbot metrics one by one.

Total Users

As the name suggests, the total users metric shows the total number of users – active or passive – who installed or used your chatbot within a fixed period. For chatbot vendors, this metric is vital since it quantifies: 

  • The total user data exposed to your chatbot 

  • The total market size for your chatbot or product 

 Active Users

Like the “Seen” metric on social media, Active Users define the number of responses your bot gets within a predefined timeframe. These users are potential targets for your business as they take a keen interest in your bot messaging and respond promptly.

So what?

This chatbot analytics metric denotes the footprint of your promotional campaigns. Brands can plan campaigns more strategically if they know how many eyeballs they will grab.

Engaged Users

Engaged Users is a chatbot metric that computes the number of conversations that go beyond the initial welcome message. These conversations are two-way, elongated and replete with customer engagement.

By comparing engaged users with total users, brands get a good sense of their chatbot’s utility and engagement value. They also get a wealth of super-engaged leads and prospects who can be nurtured into paying customers with tailored content marketing.

What else?

Analyzing the personas of these engaged users can help with customer profiling, giving marketers and sales reps a blueprint of people to chase.  Unique Users Unique users are the number of net-new users who interact with a chatbot. It differs from total users since the latter takes into account all the conversations a bot has, even if this means counting multiple conversations by the same user during the customer journey.

By tracing this metric and comparing it with total users, marketers can identify the users who interact with the bot more than once – a signal of deep customer engagement. 

User Sentiment

User Sentiment indicates the user’s emotional index during the chatbot conversation. Sentiment analysis can categorize the sentiment as positive, negative or neutral, which helps the brand get deep insights into the quality experience their bot is delivering. Polarity can be added by distinguishing degrees of sentiment, such as very negative, borderline negative, extreme positive etc.

AI-powered sentiment analysis by Sprinklr Service for Chatbot analytics

Additionally, sophisticated AI-powered sentiment analysis of conversations can yield granular insights, including: 

  • Intent: Indicates the customer’s perspective towards your brand/product/bot and the intent with which they initiated the conversation (purchase, research, troubleshooting etc.) 

  • Emotion: Highlights emotions at different points in the conversation – such as happiness, frustration, satisfaction, etc – using a technique called “lexicon”. 

  • Aspect: Shows the customer’s sentiment toward specific aspects of your brand, which could be product design, user interface, content and more. It helps with root cause analysis behind churn, passivity and disengagement.  

💡Pro Tip: AI-powered customer support solutions like Sprinklr Service offer an Agent Assist feature with predictive CSAT. It tracks customer sentiment and satisfaction during the conversation (live) instead of after. This way, agents have a finger on the customer’s pulse all the time, enabling them to control the conversation adeptly.

Chatbot analytics like CSAT trend and score in Sprinklr Service

II. Message Metrics

Message-level metrics yield insights into the kind of messages that click with your audience and the ones that don’t. Before we delve into metrics, let’s talk about the different categories of chatbot messages: 

  • Conversation starter messages are the ones initiated by the bot. Over time, they should reduce in number as customer engagement grows. 

  • Bot messages are the total number of messages by the bot in one session. While a bigger message number is a good sign of engagement, message accuracy is more important. 

  • Missed messages are the messages the bot couldn’t fathom and respond to. Missed messages are common in conversations in regional languages and idiomatic phrases.  

  • In messages are those initiated by the customer or user. If this number is dismally low, reconsider investing in chatbot technology. A social media page should be sufficient to cater to low-volume queries. 

With message categories sorted, let’s discuss the main message metrics within chatbot analytics:

Total Conversations

As the name suggests, this is the total conversations between bots and users held in a single day. This metric is indicative of your bot’s reach and engagement.

New Conversations

New conversations encompass conversations with brand new users in a day or the same user on a new topic or issue.

III. Bot Metrics

Now, we come to bot metrics that specifically evaluate a bot’s usability, retention, seamlessness and performance – overall and goal-specific.

Retention Rate

Retention rate is the percentage of users who return to your chatbot in a predefined window. Bear in mind that users need to spend good time interacting with the bot in order to divulge usable and substantial insights. If they bounce too fast, the bot won’t capture analytics that fuels its AI learning model. To optimize the bot’s retention rate: 

  • Offer enticing chat-to-discount deals or other promotional campaigns. 

  • Build a quality bot that exceeds user expectations in terms of user-friendliness and response accuracy. 

Goal Completion

Goal completion rate or GCR is the percentage of conversations that attained their intended purpose. For example, for an e-commerce chatbot, the goal could be to provide users with the product catalog. So, each time the bot is able to process the user query and display the product menu, it will count as a successful engagement. 

One way to boost this chatbot analytics is to design your chatbot intentionally. Detect your audience’s top contact drivers using conversational AI or contact center AI. Then, model your bot intents on these contact drivers, driving success and engagement.

Top contact drivers data from chatbot analytics by Sprinklr Service

Goal Completion Time

Goal completion time is the time taken by the bot to attain its goal. The quicker the goal attainment, the higher the bot’s efficacy. Another interpretation of this metric is in terms of customer effort score (CES). If a bot takes just a few taps to give desired information to a user, goal completion time dips while customer satisfaction peaks.

Human Takeover Rate

Oftentimes, bots transfer cases to human agents for deeper issue resolution or lead generation. The human takeover rate is the percentage of transferred cases during a duration. This chatbot analytics is important for many reasons: 

  • If takeovers are too many and user-initiated, it’s indicative of inefficiency on your bot’s part to understand user input or deliver satisfactory responses. 

  • Bot-initiated handoffs of complex queries signify that the bot algorithm is robust, catering to 86% of customers who want the option of human help during chatbot conversations.  

Fallback Rate

Fallback rate is the percentage of interactions when the chatbot was unable to understand the user query, and resolution happened with human intervention. While some chatbot failures are inevitable, an abnormally high fallback rate needs immediate attention.

To calculate this chatbot analytics, use the formula:


Implement these hacks to reduce your fallback rate: 

  • Expand your training data to cover diverse user queries.   

  • Implement progressive disclosures to manage user expectations. 

  • Clearly communicate bot limitations right at the outset. 

  • Conduct rigorous A/B testing with varied customer service scenarios

Self-Serve Rate

The self-serve rate is the percentage of issues that are solved by the chatbot independently and completely, without needing repeat contact or escalation to human agents. It denotes the efficiency and effectiveness of your chatbot and overall customer self-service. If your self-serve rate is dismally low, there are ways to grow it by: 

  • Updating your bot’s content regularly 

  • Improving the chatbot’s user interface  

  • Designing clear pathways or menus for users 

  • Placing your bot prominently on customer-facing touchpoints 

Need inspiration? Check out these 15 ground-breaking chatbot examples.

IV. Commercial Metrics

It’s essential to measure a chatbot’s commercial impact on business revenue (real and projected) in order to make more informed decisions that give you the best bang for your buck. Here are the two most prevalent chatbot analytics from a commercial perspective:

ROI Period

This is the time period it takes to recoup a business's investment into implementing and maintaining a chatbot by getting returns in the form of cost savings, CSAT growth and increased agent efficiency.

ROI period is calculated by this formula:


A short ROI period indicates quick investment recovery, which is desirable for growing businesses.

Leads Generated

This is the number of prospects or leads who have yielded contact information during or after a chatbot interaction or have expressed interest in a way that indicates potential sales opportunities.

Leads could possibly translate into a sales and marketing pipeline for your business, raising your bottom line and revenue.

📋How Mobily improved first response time by 99.6% with a cross-channel chatbot

Mobily is a UAE-centered telecommunication giant that was struggling to cater to the sudden influx of customer queries across its 20 social and messaging channels. With bandwidth limitations, human customer support teams could handle only a fraction of the inpouring requests, leading to elongated wait times and frustration for customers. 

The solution? 

Mobily moved their offline interactions to modern digital channels, specifically Twitter, Facebook, and WhatsApp, using Sprinklr’s conversational AI chatbots that could juggle multiple customers and serve quick, contextual answers to routine queries, eventually increasing the first response time by a whopping 99.6%. 

“Before we enabled the chatbot as part of our ecosystem, our average first-response time was 20 minutes. After we enabled the chatbot for all these channels, the response time became six seconds.” 

-Mubarak Alharbi, Mobily

The chatbot helped Mobily shorten agent response time, allowing customers to choose self-service options from the chat by suggesting relevant actions like: 

  • Paying a bill 

  • Inquiring about an account balance 

  • Changing their service package 

For more complex inquiries, customers are routed to a live agent, who can see the history of the conversation between the customer and the chatbot before they get started. This enables a seamless customer experience, whether they are assisted by the chatbot or a live agent.

Read Mobily's Full Story

How to select the right chatbot metrics

There is a wide variety of chatbot metrics, but not all deserve to be measured by all businesses. Consider these five steps to narrow down your metric selection: 

  • Consider your business objectives: What do you aspire to achieve with your chatbot – customer satisfaction, cost saving, operational efficiency or something else? 

  • Factor in your stakeholder expectations: Discuss metrics with customer support teams, broad user groups, and executive leaders to strike a balance between their aspirations. 

  • Cover your customer journey: Pick metrics that are pivotal in accelerating your customer’s journey. 

  • Prioritize user experience metrics: This is a blanket rule regardless of the above factors. Ensure you don’t leave out customer experience metrics like user sentiment, user retention etc. 

  • Avoid vanity metrics: Metrics like message volume may not provide meaningful, actionable insights, so avoid tracking them. 

Additionally, avoid information overload by tracking too many chatbot metrics in one go. Focus on a few key analytics that align with your overarching organizational and customer support goals, prioritizing quality over quantity at all times.

Grow your self-serve rate by 150% with Sprinklr’s chatbot analytics

Monitoring your chatbot analytics is a surefire way to keep your conversational AI solutions bankable, efficient and viable. Fortunately, customer service solutions like Sprinklr Service take the pain out of performance measurement, monitoring and reporting by: 

  • Conducting in-platform chatbot testing of 100% customer scenarios 

  • Tracking mission-critical chatbot analytics like containment rate 

  • Automatically finding emerging bot issues using unsupervised learning 

Sprinklr chatbots power customer self-service for 90% of enterprise and mid-market brands. Plus, the out-of-the-box Sprinklr Service Self-Serve module gets your chatbot up and running in a matter of days. Care to give it a try? It’s free for 30 days.

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