The Power of Customer Service Analytics AI 🤖

Sharad Khandelwal
SentiSum CEO & Customer Service Expert
Understand your customer’s problems and get actionable insights
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By now, almost everybody has heard of artificial intelligence (AI). You’ve probably also heard or experienced that it’s often not that good yet—many companies have developed very basic AIs that are only marginally better than a keyword search, to the detriment of everybody’s trust in AI.

However, when it comes to categorizing unstructured text and speech in a customer service environment, the latest innovations in machine learning and natural language processing are well suited to the job.

In this article, we’ll walk you through the essentials of AI in customer service analytics, how it works, and the many benefits the insights can have.

Contents:
  • What Is A Customer Service Analytics AI?
  • How Does a Customer Service Analytics AI Work?
  • Three Types of Natural Language Processing AI for Customer Service
  • Why does AI work particularly well in customer service?
  • How To Use Customer Service Analytics AI In Your Business

What Is A Customer Service Analytics AI?

Customer service analytics is the part of your role as a customer service leader where you start digging around in your customer conversation logs for insights. 

That could be done in several ways:

  • Manually, by listening to a handful of phone calls or putting a sample of conversations in Excel and trying to draw conclusions.
  • More systematically, by having agents categorize every conversation as they happen with tags.
  • Or, finally, automatically by leveraging a customer service analytics AI to understand, analyze and display insights in real-time with AI-based tagging.

Each of these methods should help you understand more about what’s causing customer contact as well as customer sentiment—but, the results of each are not equal.

It’s important to note that AI enables your analytics to be more accurate and granular, cut through the subjectivity of human opinion, and do so in real-time.

What the AI does is read or listen to each customer support conversation, much like a human would, and then label that conversation with a predetermined tagging taxonomy (which could include sentiment, happy, sad, angry, or a topic you want to track, or even intent or priority).

AI systematically and scalably draws insights from your support conversations, making it the go to choice for teams with high volumes and frequencies customer contact.

I asked Kirsty Pinner, Chief Product Officer at SentiSum, why AI is such a powerful tool in customer service. Here’s Kirsty’s answer:

“AI can cut through the subjectivity of human opinion, and no matter how something is said, it can report on the customer issue in a simple way. The latest developments in AI analytics can handle complexity extremely well.”
“No other method gives a representation of customer conversations this accurately. Manual tagging is too subjective and trying keyword analysis is too blunt a tool.”

How Does a Customer Service Analytics AI Work?

So, how does it do it? And can it really be as accurate as a human? Haven’t we all heard that AI is still a bit rubbish really?

The definition of AI

The main goal of AI is to simulate human intelligence, enhanced with the capabilities of a machine—infinitely scalable, never tiring, and doing it the same every time.

Put to use in a business environment, AI is particularly good at automating repetitive processes related to understanding, problem-solving and learning.

Two advanced subfields of AI are machine learning (ML) and natural language processing (NLP). These are built around algorithms, which take large datasets (e.g. the last 12 months of your support conversation logs) as their ‘training data’ and some initial human direction and together they understand the logic between words, sentences and phrases in the human language, learn from it, and can apply the logic consistently into the future like a human would.

In our article, ‘Why is machine learning NLP so good at Zendesk ticket categorization?’ we discuss the different types of Natural Language Processing AI in depth.

There are three key types of NLP, each of which produce a varied results. Let's look at all three in a customer service analytics environment.

Three Types of Natural Language Processing AI for Customer Service

If you have 10,000 monthly support calls, emails, chats, Messengers, WhatsApps and so on…the last thing you want to do is spend a week going through them manually. In fact, that’d be very boring and the results would probably be inaccurate.

A computer, on the other hand, doesn’t get bored and has no limits to its reading speed. It can process and categorize millions of conversations in near real-time with NLP.

Of the three types of NLP below, we recommend avoiding 1 and 2. Customer service leaders come to us all the time after trying them and feeling like their time is wasted.

Let’s look at why:

1. Keyword Extraction

You may have come across automated tagging in a helpdesk tool like Zendesk or Intercom. They rely on keyword extraction to tell the automation which tags to apply.

How does it work? Simple, if a keyword is present the follow a certain rule: 

“If the word ‘refund’ is found anywhere in the conversation → Apply the tag ‘refund_request’ to the conversation.”

There are obvious flaws in this approach. The main one being that there are hundreds of ways to ask for a refund without using the word ‘refund’, meaning that unless you can think of them all and tell the keyword extraction tool, your analytics will not have accurate results.

This approach fails to provide granular results, too, because it’s simplistic. Unlike other approaches, keyword extraction isn’t an intelligent form of AI, so it can’t tell you additional information like the why behind the request. You may know you had 1,000 mentions of ‘payment issue’ but not that 500 payment issues were caused by Klarna, 300 by Payal, and 200 due to a website bug.

2. Rule-based NLP

Rule-based NLP algorithms leverage libraries of ‘rules’ to help them understand human language.

Instead of you directly telling the automation what to do, rule-based NLP understands things like ‘liberty’ and ‘freedom’ are synonyms.

This has much less complexity and much more accuracy compared to keyword extraction. It has two downsides:

  • If the rule doesn’t exist then it won’t understand the meaning.
  • It’s still looking for words or sentences, rather than understanding the language like a human would. If a customer said “I’m not happy because my payment keeps getting rejected, I think it’s Paypal’s fault but it could be Klarna”, and then the conversation between the agent and customer continued to determine it was neither Paypal or Klarna, the rule-based NLP would still tag that conversation with “payment_issue”, “klarna”, and “paypal”.

In short, it still isn’t intelligent enough to provide meaningful insights.

3. Machine-learning based NLP

Machine-learning based NLP understands speech and text in a similar way to humans. After digesting a dataset being trained in it, it uses statistical inference to carry that knowledge into new environments it’s never seen before.

For example, it can identify and infer the meaning of misspellings, omitted words, and new words like slang by itself.

Thanks to the ML, it learns the patterns between phrases and sentences and constantly optimizes itself to improve accuracy over time.

We highly recommend you ask your provider the type of NLP they’re using and whether they will build you a customized model or not—if they don’t plan to, it’s unlikely to provide the accuracy or granularity you need to achieve your goals.

Advice about AI in customer service

Deep Dive: Machine learning vs keyword tagging: Why ML is best

How We Apply a Customer Service Analytics AI

There are two ways we can apply AI to customer service analytics: topic analysis and sentiment analysis.

The topic analysis method assigns topic tags or categories to text based on the underlying meaning, reason for contact or theme.

Examples of different types of AI doing text analytics

The example in the image above is a topic analysis applied to customer service tickets or survey free text. Topic analysis can go beyond the topic and subtopic of conversation to label things like intent and urgency.

On the other hand, a sentiment analysis simply determines whether human speech or text is positive, neutral or negative. 

Working together, these techniques form what we call a topic-based sentiment analysis. For customer service teams, this is your holy grail of insights.

If you run a topic-based sentiment analysis within your support department (using a tool like SentiSum), you’ll know in real-time the key topics driving survey results or support contact AND the sentiment attached to those topics.

For example, if you identify 50 topics that drive 99.5% of customer contact, you might not be sure which issue to fix first. Your first instinct might be to fix the highest frequency first, that would reduce the most amount of costs, right? 

But, with a sentiment analysis alongside your topic analysis you might discover that your highest frequency issues only cause a low level of discomfort for customers. You might also find that another topic only impacts a handful of customers, but it induces severe anger—driving negative reviews and bad word of mouth.

With all the insight at hand, you can make a decision that best suits your goals and resources at the moment.

See this in action in the SentiSum platform video on our homepage.

Why does AI work particularly well in customer service?

AI is only as good as its training data. Luckily, customer service teams usually have tons and tons of conversations the AI can train on.

Most support teams have thousands (if not hundreds of thousands) of conversations, calls, texts and historical surveys—all of which can be used to build and train a topic-based AI algorithm in a matter of days. 

With our customers we go through this process:

  • Identify your priorities—what issues do you want to track? What goals do you want to achieve?
  • Manually analyze and tag a few hundred or thousands of tickets—identifying patterns, common tags, and building a custom AI that understands the basics.
  • Running the AI through all the historical ticket logs—and then optimizing away any inaccuracies.
  • Use our proprietary tools to continuously monitor the AI and make sure the machine learning is working as it should. For example, if a brand new topic arises, did it catch it?

Thanks to this method of building custom algorithms from scratch, the AIs we build are incredibly accurate at uncovering topics and sentiments from customer service interactions.

That’s the power of AI—if it’s high frequency, complex and unstructured free-text, then the latest AI innovations can draw insights out of it faster and more accurately than anything else.

How To Use Customer Service Analytics AI In Your Business

The results of a customer service analytics AI create lots of opportunities for improvements in operational efficiency, customer experience and agent experience.

1. Make customer insights widely accessible

One of the most important ways to use AI in customer service analytics is to make the insights widely available.

Because AI is so accurate, granular and consistent in its analytical approach, we’ve seen that other departments across the company are quickly interested in the insights.

This LinkedIn post by Charlotte Lynch sums it up nicely:

"when every employee is able to access VoC data, they are empowered to set objectives and KPIs based on real-life numbers."

The more available you can make insights to the rest of the business, the better and more customer-centric everyone’s decision-making becomes.

Due to the real-time nature and high frequency of customer service interactions, paired with an AI analysis tool they can become the beating heart of the company’s decision-making.

That’s what happened at Gousto (full interview here) when they implemented AI analytics into their support function.

“We use the latest technology to give open access to our voice of the customer data across the business for teams to self-serve insights for anything from discovery work for Tech initiatives through to root cause analysis for any operational complaints to guide improvements.” Joe Quinlivan, Head of Customer Service at Gousto

As one of the most innovative, data-led British corporations to spring up in the last 5 years, it’s worth watching how Gousto does things.

2. Improve customer experience

AI helps you do more regular, in-depth customer service root cause analyses. As you’ll already know these are incredibly important for improving customer experience.

Here’s x ways you can use an AI-driven customer service analysis to improve CX:

  • Create a knowledge centre to tackle FAQs
  • Understand negative CSAT or NPS drivers and tackle them
  • Use AI-tags to provide better customer service (e.g. prioritize urgent support tickets)
  • Use AI tags to help you scale efficiently

We get really excited about this topic because the potential for improvement and time-saving is HUGE. Check out our article on the ‘Importance of Customer Service Analytics’ for more examples or our article on ticket tagging use cases here.

3. Do call centre predictive analytics

The future of customer service analytics is predictive. Imagine if based on certain topics or sentiments you could look to the future, know what’s coming and provide preventative solutions.

Call centre predictive analytics is an emerging field and the use cases are still forming. Here are a couple of examples we deploy with our customers right now:

  • Link CSAT scores to conversation topics to understand what words or phrases used by agents is the best approach to solving specific queries. If the data shows that certain solutions or approaches lead to better sentiment and scores, then agent performance can be improved across the board by training them on that approach in the future.
  • Predict customer churn risks. We developed a tag called “churn_risk” that understood when a customer contacted support about a certain set of topics, and mapped that with other predictive characteristics like sentiment, the AI algorithm then applied this tag to incoming conversations in real-time. As a high priority for the business and customer retention, these tickets could then trigger automated triage to the urgent queue, ensuring those requests were handled quickly and with care.

Leverage SentiSum As Your Customer Service AI

SentiSum customers use our AI platform to automatically surface important topics and customer sentiments from customer service conversations. With our accurate, granular tags you can unlock time-saving automations and experience-boosting insights in real-time. 

Book a product tour with us here to see how we can help you.

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Customer Sentiment Analysis FAQs

How is sentiment analysis useful? 5 examples of customer sentiment analysis

Here are 5 ways sentiment analysis is useful in customer service:

Prioritize customer issues:
Sentiment analysis can help businesses quickly identify and prioritize customer issues based on the emotional tone of their messages. This can enable customer service agents to respond promptly to unhappy customers and resolve issues before they escalate.

Personalize customer interactions: By detecting the emotional tone of a customer's message, sentiment analysis can help businesses tailor their responses to the customer's needs. For example, if a customer is expressing frustration, a customer service agent can respond with empathy and offer a solution to address the issue.

Improve customer experience: By providing personalized and efficient customer service, sentiment analysis can help improve the overall customer experience. Customers who receive prompt and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

Analyze customer feedback: Sentiment analysis can be used to analyze large volumes of customer feedback to identify trends and patterns. This can help businesses identify areas for improvement and make data-driven decisions to improve their products and services.

Monitor brand reputation: Sentiment analysis can be used to monitor online mentions of a brand or product to detect negative sentiment and address issues before they become a larger problem. This can help businesses protect their brand reputation and maintain customer loyalty.

What is real time sentiment analysis in customer service?

Real-time sentiment analysis in customer service refers to the process of analyzing the emotional tone of customer messages or conversations as they are happening, in real-time. This enables businesses to quickly identify and respond to customer issues, prioritize certain conversations, and personalize interactions based on the customer's emotional state.Here are some examples and analogies to help understand real-time sentiment analysis in customer service:

Real-time monitoring: Real-time sentiment analysis involves monitoring customer messages or conversations as they are happening, in real-time. This is similar to a security guard monitoring a building in real-time for any signs of danger or security threats. Just as the security guard can quickly respond to any threats they detect, businesses can quickly respond to customer issues as they are identified.

Prompt customer service: Real-time sentiment analysis allows businesses to quickly identify and respond to customer issues before they become larger problems. For example, if a customer is expressing frustration about a product issue, real-time sentiment analysis can alert customer service agents to prioritize that customer's message for a quick response. This can help the business resolve the issue before it leads to a negative online review or loss of customers.

Personalized interactions: Real-time sentiment analysis can help businesses personalize their interactions with customers based on their emotional state. For example, if a customer is expressing happiness about a recent purchase, a customer service agent can respond with enthusiasm and congratulations. Conversely, if a customer is expressing frustration or anger, a customer service agent can respond with empathy and an apology. This personalized approach can help businesses build stronger relationships with their customers.

Improved customer experience: Real-time sentiment analysis can help improve the overall customer experience by providing prompt and effective customer service. Customers who receive quick and effective solutions to their issues are more likely to remain loyal to a business and recommend it to others.

Continuous monitoring: Real-time sentiment analysis can be used to continuously monitor customer messages or conversations, providing businesses with a wealth of data that can be used to improve their products and services. For example, if customers are expressing negative sentiment about a particular product feature, a business can use that information to make improvements and better meet the needs of its customers.

Overall, real-time sentiment analysis is a valuable tool in customer service that can help businesses quickly respond to customer issues, personalize interactions, and improve the overall customer experience.

What type of information do companies analyze when conducting sentiment analysis?

Here are the two overarching areas of customer information you can include in your sentiment analysis:

Text data: Sentiment analysis of text data is like analyzing a written letter to detect the writer's emotional tone. By detecting the emotional tone of customer feedback, customer service chats, reviews, or social media posts, companies can gain valuable insights into how their customers feel about their products or services.

Voice data: Sentiment analysis of voice data is like interpreting a person's tone of voice during a conversation to detect their emotional state. By analyzing phone calls or video chats with customers, companies can detect the emotional cues in a customer's tone of voice, such as frustration or anger, and provide a more personalized response.

What are the main goals of sentiment analysis?

The main goals of sentiment analysis are to gain insights into customer emotions and opinions, and to use these insights to improve customer satisfaction and loyalty. Here are some examples of the main goals of sentiment analysis:

Understand customer feedback: One of the main goals of sentiment analysis is to understand customer feedback and opinions about a product, service, or brand. By analyzing the emotional tone of customer feedback, companies can gain insights into what customers like and dislike about their products or services, and make improvements accordingly.

Improve customer experience: Another goal of sentiment analysis is to improve the overall customer experience. By understanding customer emotions and opinions, companies can address any issues or pain points and provide a better customer experience. For example, if sentiment analysis reveals that customers are frequently complaining about long wait times, the company can take steps to reduce the wait times and improve the customer experience.

Enhance customer engagement: Sentiment analysis can also be used to enhance customer engagement by identifying opportunities for positive interactions with customers. For example, if sentiment analysis reveals that customers are expressing positive emotions towards a new product or service, the company can engage with those customers to learn more about what they like and how they can improve the product or service even further.

Prevent negative customer experiences: Another goal of sentiment analysis is to prevent negative customer experiences by identifying potential issues and addressing them proactively. For example, if sentiment analysis reveals that customers are frequently complaining about a specific product feature, the company can address the issue before it becomes a bigger problem and affects customer satisfaction.

Monitor brand reputation: Sentiment analysis can also be used to monitor brand reputation by tracking what customers are saying about a brand, product or service on social media, review sites, and other online platforms. This information can be used to prevent a potential PR crisis and maintain a positive brand reputation.

Want to learn more about how SentiSum automates your customer sentiment analysis? Book a meeting with our team here.

The Power of Customer Service Analytics AI 🤖

Sharad Khandelwal
Sharad Khandelwal
CEO & Co-founder at SentiSum, Expert in AI Analytics

By now, almost everybody has heard of artificial intelligence (AI). You’ve probably also heard or experienced that it’s often not that good yet—many companies have developed very basic AIs that are only marginally better than a keyword search, to the detriment of everybody’s trust in AI.

However, when it comes to categorizing unstructured text and speech in a customer service environment, the latest innovations in machine learning and natural language processing are well suited to the job.

In this article, we’ll walk you through the essentials of AI in customer service analytics, how it works, and the many benefits the insights can have.

Contents:
  • What Is A Customer Service Analytics AI?
  • How Does a Customer Service Analytics AI Work?
  • Three Types of Natural Language Processing AI for Customer Service
  • Why does AI work particularly well in customer service?
  • How To Use Customer Service Analytics AI In Your Business

What Is A Customer Service Analytics AI?

Customer service analytics is the part of your role as a customer service leader where you start digging around in your customer conversation logs for insights. 

That could be done in several ways:

  • Manually, by listening to a handful of phone calls or putting a sample of conversations in Excel and trying to draw conclusions.
  • More systematically, by having agents categorize every conversation as they happen with tags.
  • Or, finally, automatically by leveraging a customer service analytics AI to understand, analyze and display insights in real-time with AI-based tagging.

Each of these methods should help you understand more about what’s causing customer contact as well as customer sentiment—but, the results of each are not equal.

It’s important to note that AI enables your analytics to be more accurate and granular, cut through the subjectivity of human opinion, and do so in real-time.

What the AI does is read or listen to each customer support conversation, much like a human would, and then label that conversation with a predetermined tagging taxonomy (which could include sentiment, happy, sad, angry, or a topic you want to track, or even intent or priority).

AI systematically and scalably draws insights from your support conversations, making it the go to choice for teams with high volumes and frequencies customer contact.

I asked Kirsty Pinner, Chief Product Officer at SentiSum, why AI is such a powerful tool in customer service. Here’s Kirsty’s answer:

“AI can cut through the subjectivity of human opinion, and no matter how something is said, it can report on the customer issue in a simple way. The latest developments in AI analytics can handle complexity extremely well.”
“No other method gives a representation of customer conversations this accurately. Manual tagging is too subjective and trying keyword analysis is too blunt a tool.”

How Does a Customer Service Analytics AI Work?

So, how does it do it? And can it really be as accurate as a human? Haven’t we all heard that AI is still a bit rubbish really?

The definition of AI

The main goal of AI is to simulate human intelligence, enhanced with the capabilities of a machine—infinitely scalable, never tiring, and doing it the same every time.

Put to use in a business environment, AI is particularly good at automating repetitive processes related to understanding, problem-solving and learning.

Two advanced subfields of AI are machine learning (ML) and natural language processing (NLP). These are built around algorithms, which take large datasets (e.g. the last 12 months of your support conversation logs) as their ‘training data’ and some initial human direction and together they understand the logic between words, sentences and phrases in the human language, learn from it, and can apply the logic consistently into the future like a human would.

In our article, ‘Why is machine learning NLP so good at Zendesk ticket categorization?’ we discuss the different types of Natural Language Processing AI in depth.

There are three key types of NLP, each of which produce a varied results. Let's look at all three in a customer service analytics environment.

Three Types of Natural Language Processing AI for Customer Service

If you have 10,000 monthly support calls, emails, chats, Messengers, WhatsApps and so on…the last thing you want to do is spend a week going through them manually. In fact, that’d be very boring and the results would probably be inaccurate.

A computer, on the other hand, doesn’t get bored and has no limits to its reading speed. It can process and categorize millions of conversations in near real-time with NLP.

Of the three types of NLP below, we recommend avoiding 1 and 2. Customer service leaders come to us all the time after trying them and feeling like their time is wasted.

Let’s look at why:

1. Keyword Extraction

You may have come across automated tagging in a helpdesk tool like Zendesk or Intercom. They rely on keyword extraction to tell the automation which tags to apply.

How does it work? Simple, if a keyword is present the follow a certain rule: 

“If the word ‘refund’ is found anywhere in the conversation → Apply the tag ‘refund_request’ to the conversation.”

There are obvious flaws in this approach. The main one being that there are hundreds of ways to ask for a refund without using the word ‘refund’, meaning that unless you can think of them all and tell the keyword extraction tool, your analytics will not have accurate results.

This approach fails to provide granular results, too, because it’s simplistic. Unlike other approaches, keyword extraction isn’t an intelligent form of AI, so it can’t tell you additional information like the why behind the request. You may know you had 1,000 mentions of ‘payment issue’ but not that 500 payment issues were caused by Klarna, 300 by Payal, and 200 due to a website bug.

2. Rule-based NLP

Rule-based NLP algorithms leverage libraries of ‘rules’ to help them understand human language.

Instead of you directly telling the automation what to do, rule-based NLP understands things like ‘liberty’ and ‘freedom’ are synonyms.

This has much less complexity and much more accuracy compared to keyword extraction. It has two downsides:

  • If the rule doesn’t exist then it won’t understand the meaning.
  • It’s still looking for words or sentences, rather than understanding the language like a human would. If a customer said “I’m not happy because my payment keeps getting rejected, I think it’s Paypal’s fault but it could be Klarna”, and then the conversation between the agent and customer continued to determine it was neither Paypal or Klarna, the rule-based NLP would still tag that conversation with “payment_issue”, “klarna”, and “paypal”.

In short, it still isn’t intelligent enough to provide meaningful insights.

3. Machine-learning based NLP

Machine-learning based NLP understands speech and text in a similar way to humans. After digesting a dataset being trained in it, it uses statistical inference to carry that knowledge into new environments it’s never seen before.

For example, it can identify and infer the meaning of misspellings, omitted words, and new words like slang by itself.

Thanks to the ML, it learns the patterns between phrases and sentences and constantly optimizes itself to improve accuracy over time.

We highly recommend you ask your provider the type of NLP they’re using and whether they will build you a customized model or not—if they don’t plan to, it’s unlikely to provide the accuracy or granularity you need to achieve your goals.

Advice about AI in customer service

Deep Dive: Machine learning vs keyword tagging: Why ML is best

How We Apply a Customer Service Analytics AI

There are two ways we can apply AI to customer service analytics: topic analysis and sentiment analysis.

The topic analysis method assigns topic tags or categories to text based on the underlying meaning, reason for contact or theme.

Examples of different types of AI doing text analytics

The example in the image above is a topic analysis applied to customer service tickets or survey free text. Topic analysis can go beyond the topic and subtopic of conversation to label things like intent and urgency.

On the other hand, a sentiment analysis simply determines whether human speech or text is positive, neutral or negative. 

Working together, these techniques form what we call a topic-based sentiment analysis. For customer service teams, this is your holy grail of insights.

If you run a topic-based sentiment analysis within your support department (using a tool like SentiSum), you’ll know in real-time the key topics driving survey results or support contact AND the sentiment attached to those topics.

For example, if you identify 50 topics that drive 99.5% of customer contact, you might not be sure which issue to fix first. Your first instinct might be to fix the highest frequency first, that would reduce the most amount of costs, right? 

But, with a sentiment analysis alongside your topic analysis you might discover that your highest frequency issues only cause a low level of discomfort for customers. You might also find that another topic only impacts a handful of customers, but it induces severe anger—driving negative reviews and bad word of mouth.

With all the insight at hand, you can make a decision that best suits your goals and resources at the moment.

See this in action in the SentiSum platform video on our homepage.

Why does AI work particularly well in customer service?

AI is only as good as its training data. Luckily, customer service teams usually have tons and tons of conversations the AI can train on.

Most support teams have thousands (if not hundreds of thousands) of conversations, calls, texts and historical surveys—all of which can be used to build and train a topic-based AI algorithm in a matter of days. 

With our customers we go through this process:

  • Identify your priorities—what issues do you want to track? What goals do you want to achieve?
  • Manually analyze and tag a few hundred or thousands of tickets—identifying patterns, common tags, and building a custom AI that understands the basics.
  • Running the AI through all the historical ticket logs—and then optimizing away any inaccuracies.
  • Use our proprietary tools to continuously monitor the AI and make sure the machine learning is working as it should. For example, if a brand new topic arises, did it catch it?

Thanks to this method of building custom algorithms from scratch, the AIs we build are incredibly accurate at uncovering topics and sentiments from customer service interactions.

That’s the power of AI—if it’s high frequency, complex and unstructured free-text, then the latest AI innovations can draw insights out of it faster and more accurately than anything else.

How To Use Customer Service Analytics AI In Your Business

The results of a customer service analytics AI create lots of opportunities for improvements in operational efficiency, customer experience and agent experience.

1. Make customer insights widely accessible

One of the most important ways to use AI in customer service analytics is to make the insights widely available.

Because AI is so accurate, granular and consistent in its analytical approach, we’ve seen that other departments across the company are quickly interested in the insights.

This LinkedIn post by Charlotte Lynch sums it up nicely:

"when every employee is able to access VoC data, they are empowered to set objectives and KPIs based on real-life numbers."

The more available you can make insights to the rest of the business, the better and more customer-centric everyone’s decision-making becomes.

Due to the real-time nature and high frequency of customer service interactions, paired with an AI analysis tool they can become the beating heart of the company’s decision-making.

That’s what happened at Gousto (full interview here) when they implemented AI analytics into their support function.

“We use the latest technology to give open access to our voice of the customer data across the business for teams to self-serve insights for anything from discovery work for Tech initiatives through to root cause analysis for any operational complaints to guide improvements.” Joe Quinlivan, Head of Customer Service at Gousto

As one of the most innovative, data-led British corporations to spring up in the last 5 years, it’s worth watching how Gousto does things.

2. Improve customer experience

AI helps you do more regular, in-depth customer service root cause analyses. As you’ll already know these are incredibly important for improving customer experience.

Here’s x ways you can use an AI-driven customer service analysis to improve CX:

  • Create a knowledge centre to tackle FAQs
  • Understand negative CSAT or NPS drivers and tackle them
  • Use AI-tags to provide better customer service (e.g. prioritize urgent support tickets)
  • Use AI tags to help you scale efficiently

We get really excited about this topic because the potential for improvement and time-saving is HUGE. Check out our article on the ‘Importance of Customer Service Analytics’ for more examples or our article on ticket tagging use cases here.

3. Do call centre predictive analytics

The future of customer service analytics is predictive. Imagine if based on certain topics or sentiments you could look to the future, know what’s coming and provide preventative solutions.

Call centre predictive analytics is an emerging field and the use cases are still forming. Here are a couple of examples we deploy with our customers right now:

  • Link CSAT scores to conversation topics to understand what words or phrases used by agents is the best approach to solving specific queries. If the data shows that certain solutions or approaches lead to better sentiment and scores, then agent performance can be improved across the board by training them on that approach in the future.
  • Predict customer churn risks. We developed a tag called “churn_risk” that understood when a customer contacted support about a certain set of topics, and mapped that with other predictive characteristics like sentiment, the AI algorithm then applied this tag to incoming conversations in real-time. As a high priority for the business and customer retention, these tickets could then trigger automated triage to the urgent queue, ensuring those requests were handled quickly and with care.

Leverage SentiSum As Your Customer Service AI

SentiSum customers use our AI platform to automatically surface important topics and customer sentiments from customer service conversations. With our accurate, granular tags you can unlock time-saving automations and experience-boosting insights in real-time. 

Book a product tour with us here to see how we can help you.

Join a community of 2139+ customer-focused professionals and receive bi-weekly articles, podcasts, webinars, and more!