This is the expectation across industries, including the insurance industry. Unfortunately, an IBM report shows that 60% of insurers lack a customer experience (CX) strategy. This may be one reason why 42% of customers don’t fully trust their insurer. 

Without a solid CX plan and the tools to execute it, you’ll fall behind the competition. This is especially crucial to note because 40% of insurers are already putting some semblance of a CX strategy into motion. 

In this guide, we’ll describe some customer experience trends du jour as well as how you can use them—and AI—to exceed customer expectations as an insurance company. 

The top customer experience challenges in the insurance industry

Why does the insurance industry consistently struggle to deliver positive customer experiences? A big part of this problem has to do with complacency. In the past, insurers didn’t need their customers to like them because their services were a necessity. 

In order to beat competitors, all insurance companies needed to do was offer the best rates and packages. However, the situation today is quite different: Consumers want great customer experiences with every single company they do business with. 

Even insurance executives are taking notice, with 95% stating the claims experience is key to maintaining customer loyalty. 

Unfortunately, this area and several others need work across the board, with many of today’s insurance companies merely playing catch-up.

Here’s a look at the leading challenges the insurance industry faces with CX:

• Understanding the customer’s needs and desires
• Developing an omnichannel sales process for a cohesive experience
• Using IoT to improve customer personalization
• Building apps and websites that cater to customers’ needs
• Improving the customer experience as demands change

If these are common problems that you face within your organization, be sure to read through to the following CX trends in insurance technology. 

Digital insurance customer experience trends in 2022

Insurance providers are taking the customer experience seriously in 2022. In the same IBM report mentioned above, researchers reveal that 90% of insurers have a Chief CX or Chief Customer Officer (CCO) to drive customer success. 

This is a smart move because the IBM survey shows 60% of consumers want their insurers to understand them well. To achieve this, insurance companies must gather the right intel to learn the needs and desires of customers. 

Given that customers rarely engage with their insurers, it’s critical to make every call and interaction a great one. One hiccup can lead to a lost customer for life!

In that case, what can you do to prevent subpar customer interactions? Below are some of what others in the insurance industry are doing to improve their CX. 

1. Focusing on omnichannel.

Digital transformation allows customers to engage with insurers everywhere—on social media, over email, on the phone, and via online chat. Some even have apps to file claims or speak to representatives. 

One report shows 45% of customers use three or more channels to get support from their insurance provider. Based on this fact, it’s critical to have an omnichannel strategy in place.

But before you build one, it’s vital to learn about your customers. With a mix of artificial intelligence and human representatives, you can gather information from customers to improve their omnichannel experiences. 

For example, you can use a tool that records and transcribes customers’ calls; afterward, the tool analyzes communications to identify keywords and trends that signal issues in the omnichannel experience. 

Maybe you’ll learn that customers prefer to use specific channels, or that your social media isn’t connected to your CRM. This would mean that the omnichannel experience is broken. 

Use the data you gather to map out customer journeys, then ensure your omnichannel strategy is accepted across departments.

2. Simple purchasing experiences.

Being onboarded into a clunky, disorganized, or complex process isn’t an ideal customer experience. Unfortunately it’s the all-too-common reality for insurers who rely on paperwork or digital forms to educate customers about their policies. 

What insurers are left with are dissatisfied and confused customers. According to the 2021 Global Insurance Outlook report out of EY, 63% of customers don’t understand the parameters of their life insurance coverage. Another 50% aren’t confident they’ll get the benefits included in their coverage. 

What this demonstrates is poor communication. One can pick up on these issues if one takes the time to interview customers throughout and following the onboarding process. 

Have representatives record these interviews to gain insights into the frustrations created during and after the purchase. Then, use these insights to train sales and support teams to provide better answers for asked and anticipated questions. 

Or, better yet, build a knowledge base that answers concerns customers have so they have a self-service resource. 

3. Enhancing the claims process.

The claims process is the most important part of the customer’s journey. If you want to gain loyal customers, perfecting this process is key. 

The best way to do this is simply to listen to your customers. Interview them about their claims experience and record claims calls. Both will unveil details that shine a light on overseen issues. 

For example, maybe there’s not enough empathy or personalization. People want to feel their insurer understands what they’re going through, particularly after a tragedy. 

With an AI-powered transcription tool, you can record claims calls to identify the sentiment of representatives. If you see they’re not showing empathy, then train them to do so. 

When a customer calls to file a claim, odds are they’re emotionally charged and sporadic. So, it’s critical that your support team is capable of being empathetic. Use recorded calls to identify specific problems to use as examples of what not to do in the future. 

Then, you can build a training program to ensure all future representatives understand how to manage high-emotion claims calls. 

4. Creating personalized customer experiences.

Your customers don’t just want you to understand them, they want you to know them well–or at least, that’s what 64% of consumers said in the IBM survey. 

How do you achieve this? By collecting and analyzing customer data and using it to build a personalized experience. 

Conversation AI provides real-time insights (e.g., sentiment, topics, follow-ups) so that sales reps can instantly leverage them to enhance the customer experience. 

If your conversation data shows that the customer recently downgraded their vehicle to save on gas, and mentions on a support call about losing their job, you can offer them a discount for being eco-friendly. Or, you can offer to search for a better policy rate to prevent losing them as a customer.

Deliver a better customer experience with voice AI

Customers rely on insurance companies to bail them out of financial hardships. But doing the bare minimum (e.g., paying for expenses) isn’t enough. 

If you’re not proactively perfecting the customer experience, then you’ll fall behind the competition. 

Conversation AI has never been more important than now—especially with 36% of leaders stating insurance customers prefer the phone over other channels to buy policies.

With Symbl.ai, you can transcribe and analyze all communications, including phone, email, and online chat. Then, you can capture these insights and plug them into a hub or shared CRM for sales and support teams to easily view.

Symbl.ai is a context-aware conversation AI tool with monitoring dashboards that show the sentiment of representatives and customers in real time, so your teams can better manage their tone and create a better overall communication experience. 

The real-time analysis Symbl.ai offers makes it easier to solve problems right away to prevent customer dissatisfaction and churn. 

Curious to see it in action? Talk to an expert to better understand how Symbl.ai can amplify your customer experience today.

The post How to Improve Customer Experience in the Insurance Industry with Conversation Intelligence appeared first on Symbl.ai.

The problem isn’t about accepting CX as being necessary. It’s about finding ways to capture data that empower brands to measure performance and identify opportunities for improvement. Many of the tools on the market don’t offer a complete end-to-end look at the customer journey, making it next to impossible to pinpoint what needs addressing.

Throw on top of that the need for deep, granular CX insights in real time so that businesses can act in the moment whilst they have the customer’s attention, and you can clearly see the dilemma.

Thankfully, voice intelligence is evolving to enable these capabilities. In the sections below you will learn how conversation AI is the next frontier when it comes to enhancing the customer experience.

Gather customer feedback—and sentiment

When a customer is unhappy with your product or service, you don’t want to wait days to get back to them. That’s enough time for the customer to give up on your brand and sign up with a competitor.

Context-aware conversation intelligence can empower teams to flag conversations that show negative sentiment so that they can follow up to ensure problems are resolved.

If multiple customers are giving similar feedback, teams will see a trend in those topics and know which issues to prioritize.

Customer feedback is often nuanced, making it challenging to identify and address issues at scale. When one has a high-level overview of customer insights, it’s easier to spot these nuances and take action.

Cut down on customer research time and internal flow of information 

Traditional customer research requires hours of customer conversations, surveys, and focus groups to capture feedback. Throw on top of that the time needed to analyze these insights and you have weeks or even months of work to sift through.

AI-driven CX hastens this process by summarizing conversations, identifying sentiment, and flagging topics or keywords that reveal problems in your product or the overall experience of customer engagement.

With the in-depth information you gather with conversation intelligence, you get a better understanding of who your customers are and what their unique desires are. This enables you to identify features, updates, and other tweaks to make to your process or product to reduce churn.

Give actionable insights to customer-facing teams when it is most needed 

Sales and support can use customer insights from CX AI to improve performance. For instance, sales teams can analyze the most popular questions, and concerns prospects have during calls. Then arm themselves with answers and resources to give leads who bring them up on their call.

Customer support teams can also use conversation intelligence to identify common issues customers have. Then, these teams can develop a knowledge base that answers questions and displays DIY walk-throughs to reduce the number of calls as well as customer support tickets.

A Zendesk report shows 71% of leaders say their agents are essential to driving sales. So, by equipping them with actionable customer insights they’ll be better prepared to overcome objections and close more deals.

Use AI to inform your CX strategy

Since the pandemic began, 60% of consumers say their customer service expectations have increased. However, only 54% feel organizations treat CX as an afterthought. If you’re serious about business growth, then building better customer experiences is critical to winning over the competition.

Below are several ways you can implement CX AI insights to enhance your customer experience strategy:

• Create and optimize a better customer journey map using insights from various customer touch points
• Improve collaboration between sales, support, and product teams to ensure alignment in creating a holistic customer experience
• Enhance your product or service based on the feedback of customers
• Create better communication experiences using real-time analytics and AI assistance to track your tone, solve problems, and offer the right answers while taking calls from customers

How can your superpower CX insights with conversation intelligence? 

Conversation intelligence software collects customer data from conversations held on various platforms. For instance, it can capture insights from:

• Customer call recordings
• Customer chats (real-time or AI)
• Customer emails

The AI turns recordings into transcriptions, and then converts it—and text-based chats—into insights. The software analyzes the words to detect phrases that showcase happiness, discontent, and even confusion.

The software also creates summaries of important areas containing keywords. For example, it’ll organize your notes into sections based on sentiment that you can filter based on different moods or words.

“We deployed artificial intelligence tools to bridge the gap between practitioners and patients. As a consequence, we can give 24-hour service, assisting human therapists,” says Isaac Robertson, co-founder of Total Shape, in an interview.

Robertson isn’t a Symbl.ai customer, but he developed conversation AI chatbots to give patients a private and safe area to express their feelings. 

“The same artificial intelligence chatbot may help therapists take notes and summarize sessions. This has drastically transformed the transparency and connection with clients.”

But AI doesn’t just summarize, it also offers a deep analysis of the findings. You can use this analysis to:

• Identify and track behavioral characteristics like empathy, politeness, anger, and joy
• Monitor and flag calls with unanswered questions, negative sentiment, or no follow-up scheduled
• Detect competitive mentions in sales calls and related questions to use to guide content creation
• Analyze sales calls to find product or feature questions to determine how to personalize product experiences
• Find upsell and conversion opportunities, detect reasons for churn, and proactively create offers to improve revenue goals

Understand customer conversations beyond keywords

Conversation intelligence is a method used to collect data from real-time conversations between AI or human representatives and customers. The goal is to gather insights, such as the topics discussed and the sentiment of the customer.

With conversation AI, you can automate the summarization of customer meetings into helpful notes, and determine whether the customer is happy, upset, or disappointed. It even points out keywords, such as competitor names to identify potential retention issues.

Everything’s collected in real-time, empowering your sales and support teams to capture opportunities to make the situation and CX better.

Stay ahead by using conversation intelligence in your CX strategy 

Understanding your customers is vital to developing an experience that caters specifically to their needs. However, gathering this information is time-consuming, especially when using manual methods such as surveys.

Poring over data can take days—and that’s not good when your customers have an issue that needs resolving immediately. A McKinsey report shows that 25% of customers will leave after only one negative experience.

And in this McKinsey report, companies find surveys to be a subpar means of collecting real-time information from customers. It’s ineffective because surveys are:

• Limited, because only 7% of the customer’s voice is shared with CX leaders
• Reactive instead of proactive
• Ambiguous and don’t always get to the root of a customer’s problems
• Unfocused, which prevents CX leaders from calculating the potential ROI of decisions
• Non-transparent, especially for executives

This isn’t the case with real-time CX solutions. 

The best part is that Symbl.ai offers users an API that can be integrated with your tech stack. So whether you use email, chatbots, ticketing systems, or virtual calls, Symbl.ai’s software can capture important insights from each human-to-human conversation.

Ready to see for yourself how it works? Speak with an expert today to learn how Symbl.ai can benefit your customer experience. 

The post How Voice AI Can Improve Customer Experience appeared first on Symbl.ai.

You already offer a service that eases customers’ worries, which is an excellent start. However, your efforts shouldn’t end there—it’s critical to track the overall experience that companies have with your service to identify areas for improvement. 

This requires ongoing data collection, analysis, and implementation. Thankfully, artificial intelligence (AI) exists to make this process easier. 

If you’re looking to enhance CX in your business, then the following customer experience trends in the BPO industry may point you in the right direction. 

How AI enhances the customer experience

Delivering a great customer experience today means understanding your customers as well as how to exceed their expectations. To achieve this, though, you must monitor and analyze customer behaviors and patterns. 

That being said, there are limited hours in a day, making artificial intelligence a critical component in capturing and analyzing this information. With the right AI tools, you can:

• Provide customers with accurate and personalized recommendations in real-time
• Support real-time decision-making for your sales and customer service teams
• Enhance communications by making omnichannel data analysis seamless (e.g., email, chat, and phone)
• Improve communications between customers and representatives using context and sentiment detection

With capabilities such as these, BPOs can ensure a better end-to-end customer experience.

Business process outsourcing customer experience trends in 2022

Evolving technologies and customer behaviors are transforming the way BPOs build customer experiences (CX). With the industry on track to become a $525.2 billion market by 2030, it’s more critical than ever to polish one’s CX strategies. Otherwise, keeping up with demand and competition will prove to be challenging. 

Let’s review some of the emerging trends in BPO CX. 

1. Customer-centricity is paramount.

The concept of outsourcing business processes is appealing. This means that attracting customers isn’t the challenge—keeping them is. Maintaining a low churn rate is possible if you focus on better CX throughout the customer journey. 

BPOs can achieve this by using AI to uncover customers’ needs and share that information with sales and support teams. By making each touchpoint meaningful, you can create a personalized experience that each customer will cherish. 

For example, when a customer calls your contact center, the agent should be able to understand what the customer wants based on previous interactions with him or her. This allows agents to provide relevant answers and suggestions right off the bat. 

Besides improving customer satisfaction, AI also helps increase conversion rates for salespeople. 

Consider using a CRM that imports customer and prospect information for your teams to access or, better yet, employ conversational AI tools that deliver real-time insights, such as past customer conversations related to the topic you’re discussing at that moment. 

Agents can use this information to make better decisions and interact with customers through the lens of personalization. 

2. Omnichannel has become automated, but is still human.

If you’re engaging with your customers through multiple channels, but the experience isn’t consistent or seamless, then building an omnichannel strategy will help. It’ll turn your multi-channel approach into an omnichannel experience that delights customers from first contact to final sale (and beyond). 

It works because it prevents customers from having to re-explain issues they already laid out in an email to customer support. Omnichannel also benefits your customer support teams by giving them data to better understand customer needs and concerns. 

If it’s done right, you’ll have a system that follows customers throughout their journey across various channels (e.g., email, online chat, phone, social media). AI can pull insights from these conversations about customer behaviors, patterns, topics, and intents. 

With this information, sales and support teams can quickly identify issues and resolve them on the spot. It also highlights red flags that could lead to a customer leaving. AI automates gathering and analyzing data to quicken decision-making on the human side. 

It’s a better experience for customers, especially as chatbots have a long way to go to provide the level of satisfaction that a human agent can. 

3. The need for real-time communication is growing.

Online chatting. Phone calls. Quick email responses. Whatever channel customers use to access support, they’re expecting near-instant responses. Some BPOs are resorting to chatbots to provide around-the-clock communications. 

Using chatbots is a great way to capture information and intent from customers and prospects after hours. However, they should only ever be an extension of your other customer support services, because AI chat tools are limited in scope and quality and this can leave customers frustrated. 

So consider AI chat as a filter and “answering machine” to drive leads to your sales and support teams. Use algorithms and machine learning to direct people to resources that can help them now. Then refer others to human agents when human intervention is necessary. 

If your BPO offers pay-for-performance outbound calling services, you can automate this process without wasting money. For example, Symbl.ai will disconnect your system within three seconds of detecting an answering machine.

If a message is to be left, then Symbl.ai can ensure that the message begins quickly, so you’re not paying for partial messages. Symbol.ai can also detect when a person picks up, so that you can jump on the call and deliver a seamless customer experience. 

4. AI integrations with customer conversations.

What’s great about today’s conversation AI tools is the real-time insights provided for phone and chat communications. For example, agents can determine the sentiment of customers in both current and past conversations.

Perhaps the customer speaking to the support team had an issue in the past that wasn’t resolved and is now having another issue. Your support agent can provide assistance for the current problem and also bring up the prior issue to resolve it while on the call.

Integrating conversation AI into calls personalizes the customer experience and improves satisfaction and churn rates. The fact that your teams can track data and use it in real time will set your BPO apart from the competition.

5. Emotionally intelligent CX is enhanced by context-aware AI.

One pitfall of AI solutions is that they’re not capable of understanding the context of conversations. This causes them to miss important details or misunderstand what customers are trying to say.

That’s why some companies are turning to emotion-detecting AI to improve customer service. These systems analyze conversations and pick up on emotional cues such as anger, frustration, sadness, and happiness. They even identify patterns to detect trends that agents can use to better understand the intent and issues of a customer. 

This type of technology is proven to be very effective at enhancing the customer experience. For example, Symbl.ai is a conversation intelligence platform that transcribes and analyzes conversations in real time. It also has monitoring dashboards to track agent performances in customer calls. One can identify whether or not the salesperson shows empathy toward customers and whether his or her calls end with customers feeling happy or disgruntled. 

It’s critical for sales and support teams to create emotionally intelligent customer experiences so that clients feel valued and subsequently stick around for the long term. Additionally, product teams can use it to gather feedback and ideas for future product or service updates that appeal to current and future customers. 

Upgrade your CX strategy with the help of conversation AI

If you want to take advantage of these five trends, then it’s time to leverage conversation AI for your customer experience strategy. Doing so will empower your customer-facing teams with analytics that can help them make decisions with the aim of positively affecting the customer experience. 

Symbl.ai makes this possible for BPO’s real-time or asynchronous conversations. It does this by empowering you to:

• Identify sentiment and how it relates to the topics discussed
• Monitor and flag calls with questions/concerns left unanswered, or where the sentiment was negative
• Find competitive mentions on sales calls and the types of questions asked regarding them
• Analyze sales calls to find questions about services to make improvements
• Find opportunities to upsell, convert, or prevent churn 

With a conversation intelligence tool, BPOs can stand apart from competitors and ensure the best possible customer experience. Conversation AI works as a second pair of eyes that oftentimes catches issues your agents otherwise wouldn’t have while taking customer calls. 

If you’d like to learn more about how Symbl.ai can improve your BPO’s customer experience, speak to one of our experts today.

The post Business Process Outsourcing: Enhance Customer Experience appeared first on Symbl.ai.

However, offering 24–7 customer service this way doesn’t come without flaws and hiccups. Chatbots aren’t perfect and require ongoing reiteration and tuning. Knowing what to do and when requires monitoring the right chatbot analytics. So let’s review how chatbot analytics can enhance your customer experience and which metrics to track. 

What’s chatbot analytics?

Chatbot analytics (or conversational AI analytics) consists of KPIs and metrics that guide chatbot improvements needed to enhance the customer experience. For example, customer satisfaction, fallback rate, and customer engagement. 

Tracking the right metrics and KPIs also gives insights into your customers’ needs so you can better meet them. 

What’s the main objective of chatbots?

Businesses use chatbots to engage prospective buyers and customers without the help of humans. It’s a method to automate customer services and sales while providing a great experience with your brand. 

Here’s a look at the top benefits of using chatbots:

We also find businesses using a virtual assistant for a variety of other reasons:

The two types of chatbots (and which is better)

Now, there are two types of chatbots:

1. Chatbots with rule-based programming (e.g., when x happens, do y)
2. Chatbots with artificial intelligence (AI) and machine learning (ML) (e.g., learns mistakes and improves over time)

An AI chatbot using machine learning is best because it continues to evolve. How does it work? By “feeding” it thousands or even millions of samples to learn patterns and how to adapt. For example, an Intelligent Virtual Assistant uses AI and ML to find relevant information based on conversations. It’s able to “understand” text and spoken words using a technology called Natural Language Processing (NLP). This makes it more enhanced than a typical chatbot because it helps customers resolve issues faster, without needing live operators. 

How efficient are chatbots?

As chatbots become smarter, more companies will adopt them. And this is a trend we’re already seeing. We can thank the pandemic for the rapid acceleration in AI adoption. One survey shows 25% of nearly 2,400 business leaders stated they increased AI adoption because of COVID. 

And another 61% of high-performing companies reported increasing their AI investments. These are businesses across various sectors, including healthcare, retail, financial services, legal, and tech. 

But is AI adoption worth it? Another report shows how businesses use AI technologies to personalize customer engagements:

And these companies are seeing a positive impact from AI investments, such as revenue acceleration. A 2021 report reveals 41% of businesses are getting more actionable insights, and 89% see increased revenue. It’s even helping 80% reduce their marketing costs. 

Why is it important to analyze chatbot metrics?

Understanding the virtual assistant performance of your AI chatbots is critical to ensuring your customers are happy. If you’re using conversational AI to answer questions and offer support, and it’s not achieving this, then you need to know immediately to prevent losing customers. 

Treating chatbots as a set-it-and-forget-it solution puts you at risk of hurting customer satisfaction and sales. One report shows 41% of consumers use chatbots to get information and help faster than using search. And another 5% said they preferred to engage with a chatbot vs. a human to reduce wait times. 

This is why we find consumers using chatbots to do everything from booking appointments and hotel rooms to making purchases. 

“I use chatbots in my company to give immediate answers to FAQs presented by regular customers and clients,” says Eliana Levine, HR Manager and Co-Founder of FindPeopleEasy. “To measure the performance of chatbots, I pay attention to the chatbot activity volume, bounce rate, retention rate, chatbot response volume, chatbot conversation length, and so on.

I use these metrics to improve product marketing campaigns and add more questions to the data. It also helps chatbots answer customers fast.”

By monitoring the right metrics, Levine grew sales by 28% and increased website traffic. 

The issue with conversational AI you shouldn’t ignore

AI chatbots have improved exponentially over the years, but they’re still far from perfect. They require ongoing tweaking and A/B testing to enhance their ability to answer customer questions and provide help. 

When your chatbot can’t do the basics, it can present challenges that hurt your brand’s reputation and revenue. Here’s a look at some consequences of AI chatbots that don’t meet customer expectations:

Don’t let this happen to you. Let’s review the top chatbot metrics you should watch for and analyze to prevent these issues. 

What are the key chatbot metrics I should track?

There are dozens of conversational AI KPIs businesses should check based on their business goals. Some help to improve sales and enhance marketing, while others address customer support issues. 

Let’s dive into the top metrics that show whether your chatbot is performing well or needs improvement.

1. Customer satisfaction

How happy are customers with using your chatbot? One way to know is to look at your customer satisfaction rate.

There are several ways to collect this information, like asking users to complete a survey after their chat.

Max Hauer, CEO of Goflow, asks chatbot users to click thumbs up or thumbs down after an interaction. 

“The user satisfaction rate provides an overall picture of our customers’ sentiment after using our chatbot. It’s a valuable metric for improving our customer service, sales, and marketing teams,” says Hauer. 

Look at your average chat rating to determine if most customers are happy or dissatisfied. If the latter, dive deeper to determine the why.

2. Completion rates

Reducing customer service costs is one of the top goals of implementing conversational AI into your pipeline. But if users aren’t getting what they need from the chatbot, human workers will need to step in. 

Low completion (or deflection) rates reveal a problem with the bot’s answers or its issue with understanding user requests. And leads to customers closing the chat without completing the conversation. 

Some call this metric Bounce Rate, which looks at how many people leave the chat soon after engaging (but without getting what they came for).

3. Fallback FBR

Another way to identify when your chatbot is struggling to understand user requests is by looking at the fallback rate. When this is high, the chatbot is delivering irrelevant solutions to the user — or worse, none.

For instance, it’ll reply, “Sorry, I don’t understand,” showing the chatbot is confused by the request. Most times, the conversation will end early and a human will take over (defeating the chatbot’s purpose). It’s a critical metric to follow, particularly for rule-based chatbots. 

Shivanshi Srivastava from PaydayLoansUK looks at a metric called Confusion Rate to identify this issue. According to her, it reveals the limited nature of the bot, which many find frustrating (especially if they thought they were speaking to a human). 

When this occurs, she changes the responses to increase customer engagement. 

4. Goal completion rate

What’s your primary goal for adding a chatbot to your pipeline? Is it to convert visitors into leads or leads into sales? Or maybe it’s to help visitors find resources to answer their questions during the consideration stage?

Whatever it is, you need to track whether your virtual assistant is meeting the goal more often than not. 

5. Human takeover rate

How often do customers ask for human assistance when engaging with your chatbot? This shows how well (or poor) your virtual assistant performance is. 

The human takeover rate measures the percentage of conversations that escalate to a human. Some instances will have the user asking for a human. Others will be the chatbot switching to a human assistant because it couldn’t understand the request. 

Alex Wang, Co-Founder of Ember Fund, says to identify when this occurs in a conversation and in what category (sales, support, etc.), so you can optimize the chatbot accordingly. 

6. Total tickets created

If your CRM is integrated with your chatbot, then tracking the total tickets created is a must. Not only does this signal the number of people running into issues your chatbot can’t resolve. It also sheds light on the problems they have. 

Support tickets detail the problem, so use them to categorize issues and find patterns. Then upgrade your chatbot to better address these issues in the future. 

7. Missed chats

Technical issues can occur — like a chatbot failing to accept a conversation started by a user. Or when it fails to connect the user with a live agent. 

These instances are critical because they hurt the user experience and deter customers from engaging with the chatbot again. 

8. Unrecognized customer queries

Some chatbots have multiple-choice options (e.g., rules-based assistants). Others allow the user to type in their question (AI operated). When a user can frame questions on their own, they may be unrecognized or outside of the chatbot’s capabilities. 

If there are too many unrecognized customer queries, you’ll need to either train the chatbot to answer them or use machine learning to understand similar phrases so that you can optimize the training cycles and extend the coverage by integrating more advanced NLU (Natural Language Understanding). 

This is another reason to use AI — you can train an AI chatbot in semantics, so it can understand different variations of a question (vs. having to be an exact match to its programming). It makes chatbots more human-like and capable of understanding requests, no matter how it’s posed by the customer.

Map out customer conversations to cover various scenarios and problems they may run into. Then fill in the gaps using the data you collect from the unrecognized queries — semantics training will take care of the rest.

9. Average handling time

How quickly is your chatbot handling customer inquiries? The role of an AI virtual assistant is to speed up customer service. So it should be fast to answer FAQs and common requests it’s programmed to handle. 

After integrating your chatbot, check handling times to see if it’s shorter than when humans were handling the messenger.

10. Number of bot sessions initiated

Are site visitors and customers using your chatbot? By looking at bot sessions initiated, you’ll see usage frequency. Compare this with the number of visitors your site receives to determine the percentage that use it. 

Now, the goal isn’t to get everyone to use your chatbot. But if no one is using it, maybe it’s not visible enough, or people aren’t happy with the chatbot’s results. 

Another similar metric is the “total number of users.” The goal is to see how many people are using it. And if you dig further, you can learn how many users are new vs. returning.

11. New vs. returning users

Why is this metric important? Because it shows how many people use the chatbot. And determines if the chatbot is successful — hence the high rate of returning users.

“I track this metric weekly, which reveals how users value and are happy with our services,” says James Parsons, Founder and CEO of Content Powered. “It improves customer experience and builds trust and loyalty, bringing us more new clients through word-of-mouth and referrals.”

12. Blockers

Where are chatbot users getting stuck during conversations? Is there a common drop-off point? Investigate to see if patterns exist. If so, find the blocker and remove it. 

For example, maybe a resource link the chatbot shares is broken, leaving the customer without a solution. Update the link or provide an answer in the chatbot to prevent further issues. 

13. Conversation logs 

Seeing the chat logs between users and chatbots reveals lots of insights. Look for problem areas and what happens right before the customer bounces away or requests human intervention. 

If you have voice calls, you can use a tool like Symbl.ai to transcribe the audio and generate common topics, themes, and questions. It’s an AI-powered conversational analytics platform that analyzes conversations to detect sentiment and understand intents that can be converted into voice or text virtual assistant flows. 

Use real-time information from unstructured voice calls to find conversations where customers are angry, frustrated, or showing other negative emotions. Then review what’s happening to find ways to optimize the chatbot to perform better and faster. 

14. Conversation duration

How long a user spends on a chatbot is telling. If it’s short, it may be doing a great job of providing answers. Or folks are dropping out because of its ineffectiveness. 

“The conversation length between the chatbot and user should be short enough to inform, yet long enough to engage. Too long? Then the user hasn’t found what they’re looking for. Too short? Then the bot failed to engage the visitor, causing a potential customer to head to the competition,” says Chris Gadek, VP of Growth at AdQuick.

“But measuring session duration and conversion rate can determine whether you need to adjust your chatbot content. We found a session containing 5 steps lasting roughly 4 minutes boosted our lead conversions by 40%.”

Baruch Labunski, CEO at Rank Secure, also uses conversation duration as a metric. 

“We’ve used this metric in real-life situations to address areas where the chatbot couldn’t resolve an issue or help with a problem. This results in the customer leaving the chat quickly. Looking at the metrics leads us to the transcripts, specifically identifying the problem areas.”

15. Revenue generated

Is growing sales your top priority? Then track revenue generation. Review how many users purchase through the chat or a live agent (via the chat or phone). 

It doesn’t matter who closed the deal — your live agents and the chatbot are working together to complete the same goal.

16. Lead generation

Or maybe you’re not heavily focused on sales and want your chatbot to drive more leads. In this case, your chatbot’s goal is to get users to fill out a form, download content, or sign up for a newsletter. 

Track how many visitors convert into leads via the chatbot to see how well it’s performing. 

17. Sentiment 

Are the folks using your chatbot happy or angry during the conversation? Some may turn to your chatbot for support and will start out upset and then become satisfied after receiving help. 

This is a great sign your chatbot is doing well. But if the opposite is true, it’s time to step in and adjust the chatbot. 

Optimize your chatbot for better performance

Using conversational AI to enhance customer service and sales workflows isn’t the future — it’s the now. If you’re already using chatbots or plan to soon, then it’s critical to monitor the right metrics. 

And it’s not something you do once or twice. Keeping a close eye on your KPIs (weekly, bi-weekly, or monthly) prevents your chatbot from causing damage to the customer experience. 

So do what you can to learn what works and what doesn’t, including:

• Giving post-chat surveys
• Creating an escalation path
• Writing dialog that feels human
• Adding videos, articles, and other content to improve engagement
• Updating your chatbot with new information and resources
• Using NLP to improve sentiment detection and understanding
• Use other unstructured conversation data like audio calls, transcripts to scale the intents and scope.

“To improve my AI pipeline, I collect the transcripts of the various questions and responses that the tool worked on. I identify common question clusters from the transcripts and refine the pipeline to address them better. I also check on variations in questions and adjust the AI to these variations,” says Gisera Matanda, Co-Fonder of WeLoans.

“In the beginning, the AI tools will mostly not be as good as you would wish. However, the tool becomes better with time, analysis, and improvement of the various metrics. I, therefore, advise that, in the refinement of a conversational AI pipeline, let the user and the tool teach you what needs to be improved.” 

If you’d like to process unstructured chatbot conversations and turn them into actionable insights, then explore Symbl.ai today.

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