Customer Service KPI Metrics: Everything You Need to Know in 2023, Explained

Written by Can Ozdoruk  on   Aug 31, 2022

Key Performance Indicators (KPIs) are a special set of metrics that help determine whether business is going in the right or wrong direction. At first glance, it seems that customer service KPI scores take a backseat to traditional business KPIs, like profits, costs and regional sales. However, key customer support metrics may paint a more complete picture of success for the long-term viability of a business. 

Jeff Bezos has gone on the record to say that customer obsession is “the first and by far the most important” key to building a successful business. Amazon isn’t alone in this belief: Netflix is one of many other Fortune 500 businesses that believe happy customers are the foundational element of a successful business. So what customer service KPI metrics are worth obsessing over? 

This post will highlight everything you need to know about customer service KPIs, including: 

  • Why are customer service metrics important?
  • What are the most important customer service KPI metrics for teams? 
  • What is a customer service KPI dashboard?
  • Why are top customer service KPIs hard to achieve?
  • How do you improve customer service KPIs? 

Why are customer service KPI metrics important?

Customer service teams have their work cut out for them: the need to optimize margins and cut costs, all while providing a higher level of care that meets the modern customers’ expectations for immediate, personal and effortless support.

It’s now widely understood that for support teams, the stakes have never been higher. People are increasingly making their buying decisions based on the support they receive. Customers will stop doing business with a company after one poor customer support experience. Because of the impact support can have on a company’s bottom and top lines, it’s critical that support leaders are tracking the performance of their support agents, understanding areas for improvement and what’s working, as well as celebrating exceptional performance. To do that, there are specific customer service key performance indicators that need to be monitored on an ongoing basis in order to adjust processes or optimize agent training. 

When you’re tracking the right KPIs, you get an undoctored, objective view of your team’s performance, which increasingly, has an impact on a company’s bottom line. Let’s take a look at what those are now. 

What are the most important KPIs for customer service teams? 

It’s more important than ever for customer service teams to understand how you are performing against your customers’ expectations. There are core KPIs that every customer support team needs to be tracking closely. Some are based on very tangible metrics like resolution time, while others look at your customers’ feelings towards your company and how they perceived an interaction. 

Here are the 15 most important Customer Service KPI Metrics:

1. Customer Satisfaction Score (CSAT)

CSAT score is the most popular and straightforward way to measure customer satisfaction. It’s a metric that measures sentiment towards your product, service or a specific interaction. To measure CSAT, you’ll ask a simple question, such as: On a scale of 1-5, how satisfied are you with your recent purchase/support interaction/service? You’ll want to carefully review the interactions for people who responded with low scores to analyze what went wrong to update procedures and responses or conduct additional agent training. 

 Read how we increased WestJet’s CSAT by 24% with AI here 

2. Customer Effort Score (CES)

Minimizing disruption in a person’s life and requiring minimal effort on their part are the cornerstones of good customer service. CES measures how much effort your customer had to put in to resolving a particular issue or answering a specific question. CES depends on a myriad of factors including time spent, total back-and-forth interactions, and the number of times a person has to reach out.

To determine CES, you’ll ask your customers, On a scale from “Very Easy” to “Very Difficult”, how was your experience? If you find that you have a low CES score, identify how to remove obstacles and friction. 

3. Employee Satisfaction Score (ESAT)

Customer service has one of the highest attrition rates of any industry. Measuring employee satisfaction with their job, processes and team can alert you to any issues or attrition risks, and as a result retain your agents (and keep recruiting, training and onboarding costs at bay). Take frequent employee surveys, have 1-on-1 check-ins and encourage open communication to understand your employee satisfaction. 

4. Total Tickets and Tickets Per Customer

The most straightforward KPI for customer service teams is tallying the total number of customers submitting support tickets. In addition to tracking the top-line figure, you’ll want to analyze to identify how volume fluctuates based on times of day, day of the week, or based on seasons. 

You can’t just take the number at face value, though. You need to understand if you are getting more service requests because your product/service is broken or because you are getting more customers. Tracking tickets per customer can help inform resource allocation through the lens of long-term vs. short-term needs.

5. Volume by Channel

Track where your customers are reaching out from in order to optimize staffing and prioritize channels that would benefit most from technologies like automation.

For example, companies generally have been de-prioritizing customer support email as a support channel in favor of social messaging and live chat. In a recent study, we found that customers prefer email support over all other digital channels. By tracking ticket volume per channel, you prioritize and shift resources to where your customers are. 

6. First Response Time (FRT)

Also referred to as First Reply Time, FRT measures how long it takes a company to provide an initial response to a ticket. Valuing a person’s time is the most important thing a company can do with regards to customer service according to 73% of consumers, so decreasing the time it takes to at least acknowledge a person’s request is critical to a person’s overall satisfaction. Across the board, first response time needs to be a key area of focus. Research has found that the average first response time is 12h 10m, but 75% of customers expect it within 5 minutes. It’s important to note that confirmation emails with generic auto-responders skew first response time metrics. If your company uses autoresponders, you may need to define a new KPI that measures “first impactful response time.”

75% of customers expect a response in 5 minutes. The average is 12h 10 min.

7. Average Handle Time (AHT)

Getting back to your customers quickly is one thing, but how long it takes for you to actually resolve an issue is even more important. To calculate AHT, add up the tidal time it takes to close a ticket, from the time your customer initially reached out, hold / wait time, back-and-forth interaction and subsequent tasks, and post-interaction system updates. 

You can minimize AHT by decreasing time your customers are waiting and optimizing each back-and-forth interaction. Using hybrid support models, like human+AI vs. purely human agents can significantly help reduce wasted seconds.

8. First Contact Resolution

You don’t want your customers to have to reach out to you multiple times to resolve a single issue. That’s the recipe for frustration that directly impacts retention. That’s why measuring first contact resolution, or whether or not you resolved an issue in a single chat session, phone call or email response, is a good indicator of how your team is performing. If your customer needs to reach back out or be escalated to another source for support, it does not count as first contact resolution.

To measure First Contact Resolution, ask your agents to check a box or confirm within the agent desk if an issue was resolved at the end of an interaction (you’ll want to audit this on a regular basis), or follow up with your customer and ask if their issue was resolved.

This is a better resolution time measurement than average resolution time (ART). While first contact resolution results in a solution being provided in the initial outreach, average resolution time measures the amount of time it takes to completely close a case. If you are in a service industry where issues escalate or move to other departments, measuring ART takes the true view of your performance out of your hands. 

9. Cost Per Resolution

Let this sink in: 265 billion customer support requests are made every year, costing $1.3 trillion1. Understanding how much it costs to solve a single ticket is critical not only to operating costs and staffing but also serves as a great way to measure the effectiveness and ROI of adopting tools like AI.

To calculate cost per resolution, take your total monthly operating expense (salaries, technology, training, licenses, overhead, office supplies, etc.) and divide it by the total number of tickets. If you have a high cost per ticket, or notice that it is increasing, you’ll need to look for ways to bring efficiency to your group. This could come in the form of new training and employee performance review, a need to review systems used like agent desk platforms or the need to adopt new technologies.

10. Top Topics

In addition to tracking the number of tickets, analyzing the topics and reasons why people are reaching provides opportunities to carefully review processes, responses and policies to ensure a positive customer experience. You’ll also be able to identify opportunities to proactively communicate throughout the customer journey and create ways to surprise customers and catch them before a problem becomes a pain point. 

For instance, if there is a high volume of troubleshooting questions for a particular product after three months, your company could proactively provide steps on how to keep a product working as expected. 

Learn more about Intercom vs. Zendesk if you are looking for a new solution to give your customers a more personalized experience.

11. Consistent Resolutions

Think about whenever you’ve visited In-N-Out. You know you’re going to get great service and your meal is going to taste the same as every time before. Like with their burgers, people also expect consistency when they reach out to a company – no matter the channel, the agent on the other end or time of day.

In fact, In our own consumer research, we found that consistency in a company’s service and experience is one of the most important factors in creating satisfied customers. Striving to provide consistent resolutions is something that is becoming increasingly critical – especially as people are more than eager to loudly share their negative experiences.

To measure consistency, use AI to analyze how agents respond to different people reaching out with the same query and flag discrepancies.

12. Net Promoter Score (NPS) As A Customer Service KPI

Net Promoter Score (NPS) measures loyalty and the probability that someone will recommend your company to other people. NPS looks at overall, long-term brand perception, and is measured by asking a simple question: On a scale of 1-10, how likely are you to recommend [company] to a friend/colleague? 

NPS can be an indicator of growth potential for a company because peer recommendations carry so much weight in our society that is social media-obsessed.

13. Customer Retention

You should track the retention rate of your customers who reached out with an issue. Did they come back and buy from you again? Did you manage their issue well enough for it to not rupture your relationship with the customer? This will require integrating into your CRM platform, and making sure all systems (agent desk, eCommerce, etc.) are feeding data in and out of your CRM for a 360-degree customer view.

This is also a key performance indicator for determining overall customer loyalty to your brand, so the implications of good customer retention go beyond repeat purchases.

14. Employee Turnover Rate (ETR)

Employee Turnover Rate is the percentage of employees who leave a company within a certain amount of time. If you run a large support team, make sure you have a close pulse on your ETR so you can address issues head-on. The cost of replacing employees (recruiting, training and onboarding) is huge and any time you have a new agent, there is potential for inconsistency and other metrics to slide. 

15. Top Performing Agents

You’ll want to track and recognize your agents who have the lowest average handle time, highest first contact resolution, solve a large volume of tickets, deliver high CSAT and more. Ensure your agent desk platform allows you to drill down to specific agent performance, including both human and AI-powered virtual agents.

What is a Customer Service KPI Dashboard?

A customer service KPI dashboard is a place where managers can access data in real-time – whether it’s CSAT, resolution time or effort score. Data is presented in graphs or charts and is continuously updated, enabling leaders to understand exactly how their team is performing. Within a dashboard, you can examine how your team is performing over time. And if you make new hires, change policies or procedures, or adopt technology like AI, you can easily see how performance is affected. 

You can create your own dashboard, or access out-of-the-box data platforms from agent desk software like Salesforce, Zendesk, Gladly or Freshdesk, among other customer experience management platforms. 

Why are Top Customer Service KPI Metrics Hard to Achieve? 

Managing a customer support function is harder than ever. There are more channels to support, higher volume and stretched – and stressed – agents. Here are the top 6 challenges impacting support team KPIs:

1. Elevated Customer Expectations

Meeting modern customer expectations is getting harder to do; people expect quick, convenient high-quality resolutions on their terms. People expect more, and although many companies have been trying to improve their support, whether it’s live chat or online wikis and customer self service options, more than 50% of U.S. consumers have not seen any improvement in customer service over the last 12 months. Twenty-three percent have reported that customer service has grown slightly or significantly worse.

2. Conversation Juggling

With the pressure to resolve tickets quicker, agents on digital channels like live chat and social messaging are often carrying on multiple conversations at the same time. This opens the door for distraction and mistakes.

3. Information Silos

In order to fully resolve tickets with personalization and context, agents often have to access information from various back-end systems of record – whether it’s CRM, order management systems, booking systems, knowledge base platforms, or logistics systems. This creates more work for agents that results in wait time and longer resolution times. 

4. Angry Customers

An article in the Los Angeles Times has referred to customer service agents as the “punching bag” on the front lines2.. The article focuses on the airline industry, but I would argue that agents across all industries deal with difficult customers daily. According to the publication, “Agents are subjected to verbal abuse almost daily. It’s a thankless job requiring patience and thick skin.” Agents often bear the brunt when something goes wrong – whether it’s a missing ingredient in a meal-kit, a lost bag, or lost package. 

5. Unavoidable Crises and Ticket Surges

When the COVID-19 pandemic crept across the world, customer service teams were dealing with a surge in volume, evolving policies and new remote work environments. Many companies stopped measuring customer satisfaction during this time as they were simply trying to get back to customers, which often took days. When a crisis hits, it’s hard to maintain the same level of service previously provided, especially when using a human-only team that is affected by new work environments, external pressure and stress, and can’t scale output as volume surges. 

6. Not Measuring the Right Customer Service KPI Goals

While this might sound very basic, you need to have the right systems in place to actually measure the business-critical KPIs before you can look to improve them. If you use multiple engagement platforms, make sure all of the data is analyzed together to provide a true picture of how your support engine is performing.

How Can You Improve Your Customer Service KPI Metrics?

So how can companies actually deliver against customer expectations and turn support into a business driver? There are 3 specific ways that companies can improve their customer service KPIs: 

1. Hire More Human Agents

To decrease resolution time and first response rate, companies can simply hire more agents. However, hiring an army of new agents to work around-the-clock and man all of the traditional and emerging support channels is cost prohibitive for most companies. The average salary for customer service agents is $35,437 in the U.S. [source]. You must also consider costs for human agent desk platforms, overhead costs, paid time off, sick days and more.

2. Outsource Customer Service

Many companies hire outside teams to manage customer service, but while outsourcing your customer support operations is a popular choice to save on costs, you’ll need to keep a close eye on consistency, agent training and CSAT.

3. Bring AI Into The Organization In The Form Of Virtual Agents

Companies are starting to bring AI into their workforce to automate and augment support. The companies that leverage AI-powered virtual assistants are seeing upticks in customer satisfaction and other KPIs.

Are customer satisfaction surveys still relevant? Click to learn more.

Adopting AI can help improve customer service KPIs in two core ways: 

  1. Automate resolutions to repeatable issues:
    AI can respond instantaneously to high-volume, simple queries like order status and return requests. 
  2. Augment human agent work: AI can help agents work faster by gathering data from a customer prior to handoff, or pulling information from other business systems like your CMS and eCommerce platforms. The AI can package up all relevant data to pass along to an agent who can quickly review, make a decision and communicate with the customer.

Give your customers instant answers to up to 85% of customer service issues with our Zoho chatbot.

How Do You Measure AI-powered Virtual Agents Against Your Customer Service KPI Goals For Human Agents?

Seventy-seven percent of executives have already implemented conversational bots for after-sales and customer service3. With more companies turning to AI, it’s important to understand the relevant KPIs for virtual agents.

Whether you’re tracking the performance of human or AI-powered virtual agents, you need to look at the same key metrics. Yes, that’s right. measure AI like you measure your human employees. Track the impact human and virtual agents are having on CSAT, retention rates, how well they collaborate with teammates, how successful they are at cross-selling, and the underlying metrics related to efficiency in closing tickets. This is a new way of measuring the impact of an automated customer service technology platform. Virtual agents are performing human work and need to be measured in the same way. 

Need help improving your overall customer service KPI? Discover how other companies improved their most business-critical customer support KPIs.



Is Conversational Ticketing Right for Your Business?

Written by Amy Wallace  on   Aug 24, 2022

For support teams and CX leaders today, conversational ticketing may be the way forward, and an excellent method for streamlining the support process. Why, you may ask?

Customer service is often viewed as a hassle – a necessary chore to check off the list when something goes wrong. For instance, our State of Customer Service Benchmark Report revealed that 53% of respondents reported that telecommunications companies are the most dreaded customer service calls to make. This is why more emphasis on the conversational side of support is welcome. Conversational ticketing makes it easy for people to get the support they need as they carry out their day-to-day activities. In this post, we’ll cover all you need to know about conversational ticketing, including:

  1. What is conversational ticketing?
  2. How does conversational ticketing differ from conventional support?
  3. What are some pros and cons of conversational ticketing?
  4. What role do chatbots play in this process?

What is conversational ticketing?

Conversational ticketing, also known as conversational support, refers to support provided in real-time to a user by support agents or self-service bots. By treating conversations as tickets, this streamlined system helps support teams easily view and resolve all incoming requests within these platforms. What’s more, all of this is completed within collaboration platforms such as Slack and Microsoft Teams.

Conversational ticketing is becoming more common. Today, more than 65 of the total Fortune 100 firms are paying Slack for business communication, while the number of Slack users is growing by 12 million each day. In 2021, average Slack users invested a total of 10 hours on the platform per week! Meanwhile, in 2022, Microsoft Teams reached 270 million users, up from the 145 million it reported in 2021. With their global growth and prominence, providing support within these conversational platforms that are widely used in today’s remote and distributed workforce seems like the logical path forward.

How does conversational ticketing differ from conventional support?

What makes conversational ticketing systems truly unique is their reliance on dialogue – a dialogue between a customer and a support agent (human, virtual, or a combination of both). It eliminates the delays that are common in more conventional support interactions, such as waiting for hours to speak to an agent, or emailing a support line and then receiving a response the following day.

What are some pros and cons of conversational ticketing?

The Pros:

A common ground – conversational ticketing meets users on the channels where they are, on the platforms/apps they are familiar with and frequent on a daily basis. As support is delivered directly within the app, this eliminates the time as well as loss of context involved in switching apps.

Support, at speed – allowing for back-and-forth exchanges to occur in real-time, it dramatically speeds up time to resolution, so issues are submitted and resolved quickly.

The best of both worlds – it offers support with a combination of both automation and human support. By leveraging the power of AI-powered virtual assistants, which can handle many repeatable queries, human agents are free to tackle more complex tasks.

Adding a touch of humanity to the support process – its conversational element makes receiving support as simple as chatting about troubleshooting issues with a coworker.

Affordable – through ticket deflection, conversational ticketing allows teams to decrease their IT support volume, and reduce operational costs of support.

The Cons:

Keeping up with a steady flow of requests can be difficult – with a greater number of support requests entering ticket queues, there is the possibility that some may fall through the cracks. For instance, there may be times when there is a software glitch or systems upgrade, and users are encouraged to reset their passwords. In such cases, support teams should expect an influx in tickets!

Tracking of important metrics is limited – while metrics are key to gauge success, platforms such as Slack and Microsoft Teams were simply not designed to open and close tickets and track time to resolution.

Accounting for other self-service options – nearly half of employees want to take a DIY approach to their IT fixes. This is why integrating such resources within the support platform is key, to give users the option to follow this resolution path – more on that later!

When two worlds collide: conversational ticketing meets AI

For technology, integrations are powerful – integrations with other solutions (such as Netomi!) that can enhance the power of each. Automation takes conversational ticketing to a whole other level – making the entire process effortless for both support teams and the customers they serve.

  1. Used in parallel with knowledge base tools, AI-powered virtual assistants allow companies to harness the extensive information contained within their knowledge bases to accurately provide customers with the exact information that they need, and when they need it.
  2. Additionally, taking a more structured and streamlined approach, AI can help with the ticket triaging process to identify and categorize recurring service requests, ensuring tickets are resolved faster and transferred into the right hands for review. AI agents can first gather information and contextual data from back-end systems such as order management or CRM platforms prior to passing the ticket to a human agent, so that they will have the necessary information to make an informed decision.
  3. AI swiftly handles common and repeatable queries, such as password resets and basic troubleshooting issues, allowing human agents to focus on tasks that carry greater complexity, or those that require a personal touch. The result? Significant boosts in the capacity of support teams.

Due to their pure convenience, customers today are favoring options that allow them to easily chat with support teams to quickly receive the support they need. In a shift towards conversational customer service, live chat is on the rise. In 2021, customer inquiries over live chat channels jumped by 36%, representing the highest increase of any other communication channel. In order to successfully bridge the gap between support teams and their end-users, there is a need for an integrated solution, one that offers real-time support within platforms where users spend their time. It is time to remove the hassle often associated with customer service, and make it more conversational.

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The Rise of the Machine Customer: The Next Emerging Market for CX

Written by Mani Makkar  on   Aug 18, 2022

Staying abreast of all things CX is something we take seriously at Netomi, and the rise of the machine customer is a phenomenon that is shaping the future. This sounds futuristic, bringing to mind images of assembly lines and TV shows such as Westworld. To clarify, the customers themselves are not machines, rather, machine customers, such as virtual personal assistants or smart products, will perform customer service activities on behalf of their human customers for lower customer effort.

To highlight the growing prominence, and power, of machines doing much of the heavy lifting: today, there are more than 7 billion smart devices worldwide, while, in the U.S. alone, Amazon’s Alexa has gained more than 80,000 skills.

Let’s look at some examples of truly proactive customer support, delivered by machines.

HP Instant Ink monitors ink and toner levels, then orders cartridges from HP before the printer runs out of ink. Ink cartridges are proactively shipped before the customer runs out. Effortlessly, the customer is not involved in the entire transaction.

Google Duplex makes calls on behalf of customers in a natural-sounding human voice, the technology is directed towards completing “real world” specific tasks over the phone, such as scheduling appointments. For instance, if a customer asks ‘Hey Google, can you book a haircut appointment,’ Google will phone the salon, speak to the agent and book the appointment.

The Evolution of the Machine Customer

There are three phases in the evolution of the machine customer:

With the bound customer, there is a clearly defined set of rules that exist before the machine customer begins to make any decisions. In this case, the customer is taking the lead, such as asking a machine to order more milk when they run out, on their behalf. This is happening today (consider all of the grocery lists created with Alexa and Siri’s assistance)!

The adaptable customer will make optimized selections among competing products, based on rules. In this phase, there is a shared lead between humans and machines, that is, neither takes the decision unilaterally. For instance, after gathering details about a customer’s financial situation and future goals through an online survey, robo-advisors such as Betterment use this data to offer the customer advice and automatically invest for them. Gartner is predicting this phase to become a reality by 2026.

The autonomous customer infers customer needs, based on rules, context and preference, even when they haven’t been explicitly stated by the customer. In this case, the machine itself has its own needs. Taking concepts from Maslow’s hierarchy of needs and applying it to machines, the machine’s self-actualization needs, which sit at the top of the pyramid hierarchy, relate to the amount of positive change it can bring to the life of the customer. For instance, this could involve informing them that they should adopt healthier eating habits so as to lower their risk for obesity. In this case, the ‘thing’ (machine) both leads the interaction and execution. This is the level of autonomous customers that companies are trying to manufacture, and, by 2025, Gartner predicts that 37% of customers will try using a virtual assistant to interact with customer service.

What Does the Rise of the Machine Customer Mean for CX?

In this new machine customer era, business models, along with customer experience itself, must be rethought. Whereas previously, customers would phone, email, or send a message to support teams, the machine customer is now preemptively or proactively doing this task, in what becomes an effortless experience for the customer. Imagine, as a human, not taking any action at all, or even not knowing about an issue before it is seamlessly resolved?

Original source: Gartner

However, there is a distance that will come between CX teams and customers, and one that results in a conundrum for CX leaders – at which point in the journey will they need to loop in human customers? While a human touch is essential for CX teams, with the presence of machine customers this will become increasingly difficult to maintain, making room for a potential disconnect to occur. That is, you can ‘wine and dine’ your way to loyalty with a human customer, but with a machine, this is not so easy to do. There are situations in which the human customer will be impacted and they will need to be involved, for instance, due to manufacturing delays, their car will not be ready for several days. This is where the concept of having a ‘human-in-the-loop’ becomes key – ensure that the human customer is always kept in the loop. Rather than having a human-centric CX, which relies on developing a customer understanding through voice of the customer insights, there is now the voice of the human plus the ‘thing’ to account for.

As CX leaders, we have to consider:

  • Which tasks will machine customers actually perform? This could encompass, for instance, waiting in queues, repurchasing items, making appointments, and disputing charges. Do customers want to re-order paper towels each time they are running low, or have a machine automatically do this?
  • How do we recognize when a machine customer is contacting us, versus a human customer? We might not be able to tell the difference.
  • With requests coming through various channels, such as chat and email, is our organization equipped to handle these machine customers (bot-to-bot interactions)? As many of us design and construct our AI solutions with human users in mind, how will these machine customers interact with our chatbots and navigate these experiences?

Summing it Up

It is a whole new, machine-driven world out there. The rise of the machine customer is shaping the CX landscape, and, for CX leaders, a new mindset is required when crafting customer experiences.

The 16 Best AI Chatbots for Business in 2023 and Beyond [Review and Key Features]

Written by Dylan Max  on   Aug 17, 2022

Chatbots are used by 1.4 billion people today. Companies are launching their best AI chatbots to carry on 1:1 conversations with customers and employees. AI powered chatbots are also capable of automating various tasks, including sales and marketing, customer service, and administrative and operational tasks.

As the demand for chatbot software has skyrocketed, the marketplace of companies that provide chatbot technology has become harder to navigate as competition increases with many companies promising to do the same thing. However, not all AI chatbots are the same. To help companies of all sizes find the best of the best, we’ve rounded up the best 16 AI chatbots for specific business use cases. We’ll also cover the 5 best chatbot examples in the real world, but more on that later.

In this post, we’ll dive into everything you need to know about AI chatbot solutions, including:

Before we jump into the 16 best AI chatbots, it’s important to differentiate between AI chatbots and rules-based bots. The first-generation bots that many companies adopted were very rigid and provided poor user experiences. Rules-based chatbots are limited to very basic scenarios.

What is an AI chatbot?

AI-powered chatbots provide a more human-like experience, are capable of carrying on natural conversation, and continuously improve over time. While basic chatbot technology moves the conversation forward via bot-prompted keywords or UX features like Facebook Messenger’s suggested responses, AI-powered chatbots use natural language processing and leverage semantics to understand the context of what a person is saying.

The most powerful AI chatbots have the most sophisticated artificial intelligence software built. So what does a powerful customer service chatbot look like? Here’s an example of what a powerful AI chatbot might look like if you could see it.

Though you won’t see a purple glowing orb literally tracking and shooting down tickets, smart backend software can solve hundreds of tickets or tasks per second. On the other hand, agents who work with low-quality AI chatbots (or none at all) might be stuck doing the manual work like this:

Without further ado, let’s take a look at the best AI chatbots for 2023 and beyond.

Best AI Chatbots for 2023

RankAI ChatbotRating (Out of 5 Stars)
4.Microsoft Bot Framework4.6
5.Zendesk Answer Bot4.6
8.Alexa for Business4.4
10.Salesforce Einstein4.4
11.Dasha AI4.3

  1. Best AI Chatbot for Customer Service: Netomi
  2. What makes Netomi one of the best AI chatbots of 2023?

    Netomi’s AI platform helps companies automatically resolve customer service tickets on email, chat, messaging and voice. It has the highest accuracy of any customer service chatbot due to its advanced Natural Language Understanding (NLU) engine. It can automatically resolve over 70% of customer queries without human intervention and focuses holistically on AI customer experience. Netomi is incredibly easy to adopt and has out-of-the-box integrations with all of the leading agent desk platforms. The company works with companies providing diverse products and services across a variety of industries, including WestJet, Brex, Zinus, Singtel, Circles Life, WB Games and HP.

    Key features

    • Natural Language Understanding for human-like conversation
    • Reinforcement learning and ongoing optimization
    • Sentiment analysis for contextual next best action
    • Back-end systems integrations (CRM, OMS, etc.) for meaningful and personalized resolutions
    • Human escalation via agent desk integration (Zendesk, Freshworks, Salesforce, Khoros, Zoho, Sprinklr, Shopify)
    • Knowledge-base integration
    • Omni-channel (email, chat, voice, social)
    • Analytics and real-time reporting
    • Multi-lingual
    • Dedicated customer success team

  3. Best AI Chatbot for IT, HR and Business Ops: atSpoke
  4. What makes atSpoke one of the best AI chatbots of 2023?

    atSpoke makes it easy for employees to get the knowledge they need. It’s an internal ticketing system that has built-in helpdesk AI. It allows internal teams (IT help desk, HR and other business operations teams) to enjoy 5x faster resolutions by immediately answering 40% of requests automatically. The AI responds to a range of employee questions by surfacing knowledge base content.  Employees can get updates directly within the channels they are using every day, including Slack, Google Drive, Confluence and Microsoft Teams.

    Key features 

    • Multi-channel (chat, email, and SMS customer service)
    • Alerts / Escalation
    • Automated Routing
    • Knowledge Base Management
    • Reporting/Analytics
    • Workflow Configuration

  5. Best AI Chatbot for WordPress: WP-Chatbot
  6. What makes WP-Chatbot one of the best AI chatbots of 2023?

    WP-Chatbot is the most popular chatbot in the WordPress ecosystem, giving tens of thousands of websites live chat and Web chat capabilities. WP-Chatbot integrates with a Facebook Business page and powers live and automated interactions on a WordPress site via a native Messenger chat widget. There’s an easy one-click installation process. It is one of the fastest ways to add live chat to a WordPress site. Users have a single inbox for all messages – whether taking place on Messenger or on webchat – which provides a really efficient way to manage cross-platform customer interactions.

    Key features:

    • One-click-install for WordPress
    • Customization
    • Custom greeting
    • Facebook page branding
    • Single inbox for all incoming messages
    • Integration with your Facebook Business Page
    • Compatible with all versions of WordPress

  7. Best Open Source AI Chatbot: Microsoft Bot Framework
  8. What makes Microsoft Bot Framework one of the best AI chatbots of 2023?

    The Microsoft Bot Framework is a comprehensive framework for building conversational AI experiences. The Bot Framework Composer is an open-source, visual authoring canvas for developers and multi-disciplinary teams to design and build conversational experiences with Language Understanding, QnA Maker and bot replies. The Microsoft Bot Framework allows users to use a comprehensive open-source SDK and tools to easily connect a bot to popular channels and devices.

    Key features:

    • AI and natural language
    • Open source SDK and tools to build, test, and connect bots to popular channels and devices
    • integrate with existing IT ecosystem
    • Omnichannel experience (website or apps, Microsoft Teams, Skype, Slack, Cortana, and Facebook Messenger)
    • Speech capabilities

  9. Best Agent Desk AI Chatbot: Zendesk Answer Bot
  10. What makes Zendesk Answer Bot one of the best AI chatbots of 2023?

    Zendesk Answer Bot works alongside your support team within Zendesk to answer incoming customer questions right away. The Answer Bot pulls relevant articles from your Zendesk Knowledge Base to provide customers with the information they need without delay. You can deploy additional technology on top of your Zendesk chatbot or you can let the Zendesk Answer Bot fly solo on your website chat, within mobile apps, or for internal teams on Slack.

    Key features:

    • Multilingual
    • Multi-channel (email, Web forms, chat, in-app)
    • Integrates with Zendesk Guide knowledge base
    • Integrates within Zendesk agent desk platform for seamless human hand-off
    • Deep learning

    Interested in learning more about Zendesk for customer service? Check out any of these helpful blog posts from our team:

    Looking for an AI platform that works beautifully with Zendesk? Let us show you how it works.

  11. Best AI Chatbot To Be Your Personal Assistant:
  12. What makes one of the best AI chatbots of 2023?

    Are you looking for ways to increase productivity and reduce time doing administrative tasks? is the best personal assistant chatbot that can schedule meetings and follow up to confirm times with attendees. Once you have an account, it’s as simple as CC on an email. It connects to your calendar and will coordinate with guests to find a time that works.

    Key features: 

    • Meeting Scheduler
    • Meeting Tracker
    • Auto-Responder
    • Google Calendar Integration
    • Calendar Integration
    • Office 365 Calendar Integration
    • User Access Control
    • Analytics & Reporting

  13. Best AI Chatbot for Developers:
  14. What makes CSML one of the best AI chatbots of 2023?

    CSML is the first open-source programming language and chatbot engine dedicated to developing powerful and interoperable chatbots. CSML helps developers build and deploy chatbots easily with its expressive syntax and its capacity to connect to any third party API. Used by thousands of chatbot developers, CSML Studio is the simplest way to get started with CSML, with everything included to start building chatbots directly inside your browser. A free playground is also available to let developers experiment with the language without signing up.

    Key features: 

    • Super easy syntax and conversation-oriented components
    • Short and long-term memory slots
    • Integrates out of the box with over 70 other apps (CRM, ticketing, databases, livechat, and more)
    • Chatbot activity analytics
    • Ready-to-use chatbot templates library

  15. Best AI Chatbot for Voice: Alexa for Business
  16. What makes Alexa for Business one of the best AI chatbots of 2023?

    Do you want to interact with the 83.1 million people who own a smart speaker? Amazon, which has captured 70% of this market, has the best AI chatbot software for voice assistants. With Alexa for Business, IT teams can create custom skills that can answer customer questions. The creation of custom skills is a trend that has exploded: Amazon grew from 130 skills to over 100,000 skills as of September 2019 in just over three years. Creating custom skills on Alexa allows your customers to ask questions, order or re-order products or services, or engage with other content spontaneously by simply speaking out loud. With Alexa for Business, teams can integrate with Salesforce, ServiceNow, or any other custom apps and services.

    Key features: 

    • Self-service APIs to help you create, manage, test and publish custom skills
    • Request APIs receive intents and directives from Alexa in your application logic
    • Respond to customers using text-to-speech, images, and streamed audio and video
    • Transactions and closed-loop commerce
    • SDKs for Node.js, Python and Java

  17. Best AI Chatbot for Sales: Drift
  18. What makes Drift one of the best AI chatbots of 2023?

    Drift B2B chatbots are implemented on websites to qualify leads without forms. Drift chatbots ask qualification questions and create leads in your CRM (Salesforce, HubSpot and Marketo). Once a lead is qualified, the chatbot can automatically book meetings for sales teams by connecting to calendars to pull availability. Drift also allows companies to identify the highest-valued and intelligently send personalized welcome messages to VIPs. If other questions arise during the conversation, Drift can integrate with some of the best knowledge base tools like Zendesk, Help Scout, HelpDocs and others to surface relevant information.  

    Key features:

    • A chat widget for mobile and desktop
    • Routing that directs leads and conversations to the correct person, group or team
    • SDKs for JavaScript Web, Android mobile, and iOS mobile and web
    • an API for building apps, customizing the chat widget or integrating with your platform
    • Out-of-the-box integrations, including: Slack, Office 365 Calendar, Salesforce and Market

  19. Best AI Chatbot for Salesforce Fanatics: Salesforce Einstein
  20. What makes Salesforce Einstein one of the best chatbots of 2023?

    Salesforce Einstein is an AI chatbot designed by one of the most successful companies ever to come out of Silicon Valley. Salesforce is first and foremost a CRM company, in fact, its stock symbol is CRM.

    Much of Salesforce’s success comes from the abundant software integrations that are either made by Salesforce themselves or by third-party companies. For example, Netomi has created a really powerful Salesforce chatbot, which integrates seamlessly into Salesforce’s platform. To have Einstein Bot at your fingertips, you need to buy into the overall Salesforce system and then pay $50/month as an add-on to Salesforce Service Cloud (which we also recognized as one of the 11 best help desk software options of the year).

    Key features:

    • Powerful Conversational AI
    • Capable of triage and routing to human agents when necessary
    • Seamless integration with other SalesForce products
    • Requires Salesforce Service Cloud

  21. Best AI Chatbot for Call Centers: Dasha AI
  22. What makes Dasha AI one of the best chatbots of 2023?

    Dasha is a conversational AI as a service platform. It provides developers with tools to create human-like, deeply conversational AI applications. The apps can be used for call center agent replacement, text chat or to add conversational voice interfaces to mobile apps or IOT devices. Dasha was named a Gartner Cool Vendor in Conversational AI 2020.

    No knowledge of AI or ML is required to build with Dasha, any developer with basic JavaScript knowledge will feel right at home.

    Key features:

    • Human-indistinguishable speech synthesis
    • Unlimited conversational depth
    • You own the Intellectual Property to all Dasha apps you build
    • The SDK integrates into your existing infrastructure seamlessly
    • Voice over GRPC means that you can add live AI conversations to your websites or mobile apps
    • Robust digressions and intents, stacked named entities
    • Ultra-high conversational concurrency
    • The SDK integrates into your existing infrastructure seamlessly

  23. Best AI Chatbot for User or Market Research: SurveySparrow
  24. What makes SurveySparrow one of the best AI chatbots of 2023?

    SurveySparrow is a software platform for conversational surveys and forms. The platform bundles customer satisfaction surveys (i.e., Net Promoter Score (NPS), Customer Satisfaction Score (CSAT) or Customer Effort Score (CES) and Employee Experience surveys  (i.e., Recruitment & Pre-hire, Employee 360 Assessment, Employee Check-in and Employee Exit Interviews) tools. The conversational UI deploys surveys in a chat-like experience. This approach increases survey completion rates by 40%. SurveySparrow comes with a range of out-of-the-box question types and templates. Surveys are embedded on websites or other software tools through integrations with Zapier, Slack, Intercom and Mailchimp.

    Key features: 

    • Customized conversational surveys
    • Subaccounts and multiple users
    • Multi-language surveys
    • Smart surveys using conditional logic branching
    • White label surveys
    • Visual workflows
    • Omni-channel (email, social media, web links, embedded options, scannable QR code and email)
    • Reports and analytics

  25. Best AI Chatbot for the Conversational Cloud: LivePerson
  26. What makes LivePerson one of the best chatbots of 2023?

    LivePerson offers AI-powered conversations which connect brands to consumers through multiple messaging channels. LivePerson works with companies providing diverse products and services across a variety of industries, including The Home Depot, IBM, and Vodafone. According to LivePerson’s website, its conversational AI software mostly addresses marketing and sales, followed by customer care to a lesser extent.

    Key features:

    • Integrates with most channels aside from email
    • Ability to pull time-based reports
    • Intuitive interface
    • Real-time analytics dashboard
    • Enterprise-focused pricing

  27. Best Build-Your-Own AI Chatbot for Messenger: ManyChat
  28. What makes ManyChat one of the best AI chatbots of 2023?

    Next year, 2.4B people will use Facebook Messenger. ManyChat is a great option if you’re looking for a quick way to launch a simple chatbot to sell products, book appointments, send order updates or share coupons on Facebook Messenger. It has industry-specific templates, or you can build your own with a drag-and-drop interface, which allows you to launch a bot within minutes without coding. You can easily connect to eCommerce tools, including Shopify, PayPal, Stripe, ActiveCampaign, Google Sheets, and 1,500+ additional apps through Zapier and Integromat. 

    Key features:

    • Basic reporting and analytics
    • Drag-and-drop interface to build bot
    • Integrations with Shopify, Google Sheets, MailChimp, HubSpot, ConvertKit, and Zapier
    • No coding required
    • Easy set up in minutes

    How are our customers building chatbots to rethink customer service?
    Learn how Nespresso, Tommy Hilfiger, and Westjet have turned support into a difference maker.

  29. Best AI Chatbot for Marketers: Intercom
  30. What makes Intercom one of the best chatbots of 2023?

    Intercom exploded onto the market in 2011, making it one of the first chatbots on the market. Intercom is traditionally known as an easy to use rules-based bot for business (with minimal AI). However, it has only been until recently that Intercom has released an AI chatbot. With that said, there are some strong cases to pick Intercom as a top performing software in the space including an extensive list of software integrations.

    Key features:

    • Intercom’s Business Messenger offers engaging customer support
    • Powerful integration set with over 250 out-of-the-box apps
    • Uses both business and customer data to personalize experiences
    • Seamless experience between desktop and mobile
    • Machine learning models can help answer up to 33% of inquiries automatically
    • No free tier with expensive higher tiers compared to other options

  31. Best AI Companion and Friend Chatbot: Replika
  32. What makes Replika one of the best AI chatbots of 2023?

    While this is not a business use case, it still warrants placement on this list for its coolness. Replika is an AI chatbot designed to become a “friend” who offers “no judgment, drama, or social anxiety.” It claims that users can “form an actual emotional connection, share a laugh or get real with an AI that’s so good it almost seems human.” Users can choose their 3D avatar and customize it, help it learn about the world, and develop its personality. You can decide if you want it to be a friend, virtual significant other or mentor.

    Key features:

    • Learns to imitate users
    • Messaging and voice recognition
    • iOS and Android apps
    • Customizable avatar

AI Chatbot Frequently Asked Questions


How Do AI Chatbots Work?

AI chatbots use Natural Language Processing (NLP) engines and machine learning to interpret user inputs. This involves extracting user entities and determining user intents. These NLP methods are used widely in the technology industry, including for machine translation, sentiment analysis, and user behavior analytics (UBA) in cybersecurity.

What is a Chatbot Platform?

A chatbot platform allows businesses to host multiple AI chatbots all in one place. Chatbot platforms are crucial when companies want to deploy chatbots across multiple communication channels like messenger, SMS, email, and directly on the website. Having all your chatbots organized in one place ensures maximum efficiency and learning opportunities as the AI inevitably gets more sophisticated.

Is Siri a Chatbot?

Siri is considered a basic chatbot. Even though Siri sounds smart at times, Sirilacks the natural language processing and human-like conversational ability of more advanced AI chatbots.

How Can Chatbots Help Save Me Money?

Chatbots can help save you money by automating routine tasks that humans would otherwise complete. Imagine that you owned a business where five different types of questions made up for over 50% of the total questions by volume. Without a chatbot, a customer service agent would have to answer each question one by one. On the other hand, a chatbot could answer an unlimited amount of the same customer service question type in an instant. This allows businesses to save their support agents’ time while maintaining a quality customer experience.

What Makes an AI Chatbot Powerful?

What separates a bad chatbot from the best chatbots is a bot’s ability to leverage four different categories of artificial intelligence. The best AI chatbots are extremely sophisticated in these 4 AI attributes:

  • Intent recognition—intent recognition involves a semantic understanding of text-based and AI chatbots that leverage general syntactical and semantic knowledge which they learn from a large corpus of language data and business-specific training samples. Knowledge learned by AI chatbots from large data sources helps for the expansion and transfer of vocabulary which helps to improve interpretations with fewer business-specific training samples.
  • Extraction of entities—information that relates to a specific object or concept. For example, dates, places, times, descriptions, names, items, or numbers. These bits of data are the building blocks from which inputs are interpreted and defined.
  • Dialogue management—Based on intent and entities, AI Chatbots use the next best action to trigger various actions required to capture appropriate details from users and business systems for meaningful resolution. AI chatbots learn user preferences in their long and short-term memory to take contextually relevant smart actions.
  • Expansion and transfer of vocabulary—algorithms can capture and refine vocabulary, including synonyms to improve interpretations. These refinements are tied to subsets of users to generate more natural responses and be passed to new bots.

The strongest chatbot platforms (listed earlier) allow for easy scalability and low manual effort. Now you know what makes the best AI chatbots so powerful.

The 5 Best AI Chatbot Examples in Real Life

As chatbots get smarter, the adoption rate by big brands and industry leaders grows exponentially. Now that we’ve taken a look at which companies make the best AI chatbot technology and how to define what makes one chatbot better than another, let’s explore real-life examples of companies that put those chatbots to work. Below are five of the most successful chatbot implementations and their results:

1. Best AI Chatbot Overall: WestJet’s Chatbot

Company Background

WestJet, the only 3-peat winner of TripAdvisor’s Best Airline in Canada, has incorporated a chatbot to help serve its millions of monthly website visitors. With its chatbot “Juliet,” users can book travel plans, ask questions and get resolutions to common customer service questions.

Chatbot Results

When WestJet’s bot first got started, it could automatically resolve about 30% of all customer service tickets. In less than two years, that number has jumped to over 87%. Not only is this the highest rate of automated ticket resolution ever recorded – making WestJet’s Juliet the most powerful chatbot in the world –  it also speaks to the sophistication of how true artificial intelligence can learn and get better over time. As a result, the WestJet customer service agents are able to work side-by-side with the AI bot and handle over 5X the normal load of customer support.

Interested in learning more about WestJet’s chatbot, Juliet, check out one of these resources below.

2. Best AI Chatbot for Telecom: Charter Spectrum’s Chatbot

Company Background

Charter Spectrum, a top cable and phone service provider in the U.S. has incorporated a chatbot into its customer service operations. Before launching its bot, Charter’s customer support agents were answering around 200k live chats per month, a large portion of these for common use cases including forgotten passwords or usernames.

Chatbot Results

After bringing the “Ask Spectrum” chatbot into its customer support team, Charter Spectrum was able to handle 83% of chat tickets without human intervention. This significantly lightened their customer service load and resulted in a 300% increase in ROI.

To download a copy of our Telecom Customer Service Benchmark report, visit here.

3. Best AI Chatbot for Ecommerce: Covergirl’s Chatbot

Company Background

Covergirl, a popular makeup brand, has taken a different approach. They are leveraging chatbots to engage with teens by providing product information and disseminating coupons. The Covergirl bot was designed to help the brand address the role that social media influencers play in young customer’s lives. Customers can interact with the bot to get product information and coupons for items.

Chatbot Results

As a result of their ecommerce chabot, Covergirl has seen social media engagement increase by a factor of 14. They have also experienced 91% positive sentiment ratings and a 51% click-through on coupons.

To download a copy of our retail Customer Service Benchmark report, visit here.

4. Best AI Chatbot for Travel: Amtrak’s Chatbot

Company Background

Amtrak, a nationwide rail provider in the United States, launched a travel chatbot to provide support to its 375k daily website visitors. With the Amtrak chatbot, users can book travel, ask common questions, and seek assistance modeled on the company’s best customer service representatives.

Chatbot Results

Currently, Amtrak’s bot is responding to around 5 million requests per year. This has led to a 25% increase in bookings and a 30% increase in revenue. Overall this has meant an 800% increase in ROI.

To learn how AI is completely transforming the travel experience, download this eBook.

5. Best AI Chatbot for Customer Experience: Johnson and Johnson’s Chatbot

Company Background

Johnson and Johnson owns a pregnancy and childcare resource called The BabyCentre UK. The company introduced a bot on Facebook Messenger to provide information and content to new or expecting moms. The bot uses targeting and personalization to deliver relevant information and to answer popular queries from users.

Chatbot Results

As a result of the bot, BabyCentre saw an 84% engagement rate and a 53% click-through rate. This represented a 1,428% increase over email communications despite using the same personalization methods.


The Best AI Chatbots can unlock incredible efficiency, but you need to select the right AI partner. The breadth of AI chatbots available today is incredible. The best business-specific AI chatbots are focused on a core use case – whether it’s customer service, surveys, administrative tasks or sales. With 53% of organizations expecting to use chatbots within 18 months, it’s crucial that teams adopt the right technology that prioritizes the end-user experience, exceeds goals, is easy to adopt and works seamlessly with other business systems. Therefore, as an increasing number of companies claim to have sophisticated AI platforms, not all AI chatbots are created equal.

Find out what your ROI will be if you build an AI chatbot. Try our free chatbot ROI calculator today.

Want your AI chatbot solution to be considered for this list? Email and tell us why! 

AI for CX 101: Conversational AI Metrics that Matter

Written by Shail Gupta  on   Aug 10, 2022

Metrics are critical in order to gauge the performance of both support teams and the technology solutions behind them in any project. In our customer case studies, we frequently talk about milestones such as our customers reaching deflection rates of X%, yet what do these terms really mean? As you progress with your conversational AI journey, which key customer experience metrics can you see improve with AI? In this second iteration of our ‘AI for CX 101’ series (Part 1 covers the initial steps in deploying an AI solution), we dive into the key AI-centered metrics to hone in on, to help you hit your CX and AI targets with precision.

The Top 6 Conversational AI Metrics that Matter

1. Deflection Rate

Deflection rate refers to the percentage of customer support requests that are resolved by AI, those that would otherwise be serviced by agents. This includes both:

Full deflection – In such cases, a customer receives a response from the AI, such as details on a hotel’s cancellation policies, thus obtaining a full resolution to their query (case, closed)!

Assisted deflections – Although the AI is able to identify the scope and intent of the customer query, depending on the use case, it may not be authorized to respond with a complete answer. This is an instance in which the AI and human agents work together, in ‘co-pilot’ mode. For instance, for an item that is being returned, the AI may not be authorized to cancel an order and issue a full refund to a customer. In this case, the customer may be asked additional questions, such as the date of purchase. Acting as an assistant who is conducting research prior to a presentation, the AI is gathering key pieces of data to arm the human support agent with to eventually take over. So, even though it may not be capable of fully deflecting the customer query, the AI is still significantly moving the needle, saving valuable agent time, and ensuring that the agent is armed with full context.

Why it matters:

The deflection rate metric has its benefits, as it provides an indication of the impact that your AI solution is having on your customer experience functions – both your support agents and ultimately, your customers.


CSAT score measures customers’ sentiment towards your product, service or a specific interaction. It is important to separate the CSAT into two parts, to give credit where credit is due: the CSAT score that is attributed to the AI should encompass the AI-only use cases that took place without human intervention, while, if a human agent handled the ticket, the score should be attributed to them. What constitutes a satisfactory CSAT score? “While 60% is a good starting base for CSAT ranges to begin, a score near 80% is the “holy grail,” Partho Nath, Netomi’s Head of Applied AI, noted.

Why it matters:

Key to customer retention, the CSAT score can provide insights into where and when your company is at risk of losing customers. If a customer provides a negative CSAT score because their query was not sufficiently addressed, this is an opportunity for businesses to refine their workflow to include additional customer use cases, and identify areas for improvement. Another consideration is the context of the issue itself. For instance, is a customer providing a negative score because they don’t agree with a certain business policy or an item arrived damaged, or are they basing their feedback on the support interaction?

3. Classification Rate

This refers to the volume of tickets understood by the AI – how many conversations, chats or emails did the AI understand and therefore could make an attempt at responding to?

An AI that is “well-behaved” (in the words of Partho) will not attempt to answer each and every query that comes its way, thus potentially sending the customer down the wrong path. Rather, it will first make a judgment call on whether the conversation is one in which it can confidently identify and map to one of the business workflows. Our Conversational AI Benchmarking Report revealed that Netomi has the highest accuracy in comparison to other AI platforms, meaning that the AI is responding accurately, thus causing less user frustration than if it provided a response that was incorrect or irrelevant. Netomi was also found to have the highest out-of-scope accuracy, meaning that it understands which topics it has not been trained on and follows the appropriate behavior (such as escalating a customer query to a human agent or directing them to another channel).

Why it matters:

The classification rate measures the total volume of tickets handled by your AI, and it indicates if there are additional topics or use cases that can be brought under the purview of the AI, those that it may not be authorized to currently handle. For instance, perhaps the AI has not been trained on issuing refunds, yet, as this is becoming a fairly common query asked by your customers, perhaps it is time to consider bringing this use case under its scope and focus.

4. Goal Completion Rate (GCR)

Oftentimes, users may exit a support conversation, perhaps if they do not have a specific detail of their query right in front of them, or if something else interferes with the task. Yet how many users successfully completed the journey, completing the goals you originally set for your chatbot to meet? For instance, for an airline, this could involve changing a flight, and for a retailer, finding an order status.

Why it matters:

To provide an indication of whether a customer ended their journey successfully, Netomi’s AI platform asks for feedback. If a customer remained until the end of the workflow, we can safely assume that they have been successfully helped. Focusing on this metric can provide some surface-level insights, which can warrant further digging: why didn’t the user complete their support journey? Were there any moments of friction along the way? How can these be removed?

While not directly related to the performance of the AI itself, these final two metrics pertain to the capacity of support teams, and most importantly, the time it takes for the resolution of customer queries.

5. Average Resolution Time (ART), also known as Average Handling Time (AHT) 

This is the average time it takes an agent to resolve a customer conversation. Here, you can identify potential areas for improvement and reduce resolution, such as making enhancements to your company’s knowledge base to make it easier for customers to utilize options for self-service, or removing any operational efficiencies that impact support agents performing their jobs. This also ties into smart conversational design, and reimagining the resolution paths for certain queries. By looking at the average resolution time per topic, you might be able to identify ways to streamline the journey. For instance, if a customer wishes to cancel a flight, they could be asked whether or not they purchased a refundable ticket, in order to cut down on the amount of text sent by the AI.

6. Average First Response Time (FRT) 

This refers to the amount of time that has elapsed between a customer raising a ticket and an agent first responding to it. How long does it take for a company to provide an initial response to a ticket? Research has found that the average first response time is 12h 10m, yet 75% of customers expect it within five minutes! By looking at the Average FRT, the customer service team can see how this decreases overall because the chatbot is adding value.

As AI has 24X7 availability, working around the clock outside of standard working hours, it significantly lowers the FRT, making that five-minute window quite feasible.