5 Best Customer Service Analytics Software for 2024

Sharad Khandelwal

SentiSum CEO & Customer Service Expert

Understand your customer’s problems and get actionable insights

There are a variety of customer service analytics softwares on the market today—some of which haven’t yet been snapped up by the CXM giants.

In this article, we’ll cover customer service analytics from five different angles.

Whether you want AI-powered topic and sentiment analysis to make insights gathering from customer support easy or to simply understand the trends in your ticket resolution time, we’ve got a tool for you.

For each tool, we provide:

A short summary
A product screenshot
A customer case study to peruse
A couple of reviews from G2 and their overall rating

🐘 in the room? Yep, we are a customer analytics tool ourselves! We're obviously biased but we hope our case studies and G2 reviews speak for themselves. We've also highlighted the strengths of other platforms to keep things fair.

If you want to chat it through, feel to reach out to us in the live chat.

What are customer service analytics tools?

Customer service analytics tools are plugins or full platforms to help drive visibility into your customer service operations.

They come in all shapes and sizes—from basic first response time and volume charts, through to the cutting edge of real-time AI-powered topic and sentiment analysis reporting.

In this article, we cover a variety of analytics platforms. Each of them aim to help you identify ways to improve your customer service experience with confidence and ease.

5 Powerful Customer Service Analytics Softwares

In this section, we cover five of the top CS analytics softwares on the market today, what value they bring, and what their existing customers are saying about them.

Those tools are:

SentiSum
Zendesk (and other out of the box helpdesk solutions)
CustomerGauge
Clarabridge
MonkeyLearn

Let’s deep dive each one:

1. SentiSum - AI Customer Service Analytics Software - Best for Speech & Text Analysis

SentiSum uses customized AI algorithms to tag every customer conversation and survey results with topics, sentiment, intent and more.

The detail and accuracy of the tags are what make the platform powerful for customer service analytics. The insights platform makes root cause analysis and driver reporting simple, but the granularity of the tags enable a variety of automations useful for your business—like ticket triage and prioritization based on churn-risk topics.

Key value offering:

Accurate detection and tagging of topic, sentiment and intent in support tickets, CSAT and NPS surveys.
Simple reporting and flexible root cause analytics platform
Auto-triage and prioritization flexible to your business goals

What makes Sentisum unique? The AI is customized to suit the business’ needs, there’s very little effort needed in set-up from customers, and their analytics covers any free-text channel (you can get insights across your surveys as well as support conversations).

For companies with lots of existing conversations and survey results, SentiSum makes it easy to identify CX weaknesses impacting customers and arms you with the evidence needed to fix them.

Example case study:

Gousto, the British meal-kit delivery corporation leverages SentiSum to improve customer experience, drive retention and tackle drivers of customer contact.

Using real-time topic and sentiment analysis, Gousto gets served insights from CSAT, NPS and customer service calls, chats, and emails in one platform.

Read the full case study here.

What The Reviews Say on G2— 9.4/10

G2 is a review platform that lets verified customers leave open and honest reviews.

Here’s a couple of examples SentiSum’s reviews on their G2:

"I love the fact that the product is so versatile! You get the benefit of using the product's AI to review the tickets, however you also have the option to dive deeper into specific drivers/topics by creating your own set of parameters. I enjoy how easy it is to scrub through tickets so that you can accurately calculate a trend or to better investigate ongoing issues.”—Alejandra Ruiz, Senior Agent at Skillz

Find SentiSum’s pricing here and book a meeting with their sales team here.

2. Zendesk - A help desk with analytics built-in

Zendesk and other help desk giants like Freshdesk, Dixa, and Helpscout all have basic customer service analytics functionality built-in.

Out of the box, Zendesk comes with pre-built charts and dashboards that cover the essentials of your customer service metrics.

Rather than a focus on customer experience insights or automations, these analytics charts illuminate functional metrics like average call time, CSAT scores, and number of solved tickets. They are a key first point of call to identify trends that impact the customer service experience.

Key value offering:

Keep track of key customer service metrics like ticket volume, resolution speed, and more.
Easy to understand reporting.
Complicated to get right, but powerful once setup properly.

These platforms are unique in this list because their analytics and reporting come free when you’re using their help desk systems. They also have a long list of plug-ins to improve and expand their offering.

Example case study:

Etsy, the global vintage e-commerce marketplace, leverages Zendesk as its help desk. In particular, Etsy loves the visibility Zendesk's brings to their customer service operations:

“The visibility that [Zendesk] brings has helped us improve our support operations tremendously—we’re now able to surface problem areas from our tickets, quickly fix routing rules, and identify training opportunities for our team.”—Colin Wilkinson, Senior Manager, Agent Enablement, Member Services at Etsy

Read more about Zendesk’s analytics here.

What The Reviews Say on G2— 8.6/10

Zendesk’s G2 is teeming with reviews. Here are a couple of examples:

"I love their reporting suite (Explore). Its versatility and ease of use is second to none. Zendesk puts its competitors to shame (I'm looking at you, Salesforce)."—Ken W, Senior Support Manager

Find Zendesk’s pricing here and book a meeting with their sales team here.

3. CustomerGauge - Survey Customer Service Analytics Software

CustomerGauge is an enterprise B2B customer experience management platform companies use to run Net Promoter (NPS) programs that reduce customer churn and drive account expansion through upsell and referrals.

Their platform brings together multiple signals that could help a customer service team. For example, transactional NPS surveys after customer service interactions to understand what’s driving satisfaction.

CustomerGauge is particularly powerful as a closed-loop feedback tool for frontline teams to manage account relationships and drive referral programs.

Key value offering:

Collect customer service interaction feedback with best practice surveys.
Identify strengths and weaknesses in customer account relationships with ease.
Make sure angry customer cases are followed up quickly with real-time alerts and notifications.

CustomerGauge is unique in this list because they focus solely on B2B businesses and their analytics are designed to support account management teams to drive retention and account expansion.

Example case study:

Eaton, the global electronics manufacturer, uses CustomerGauge for transactional NPS surveys in their CS department to find improvement areas in their service.

For example, Eaton centralized their customer service in Budapest in 2019. Due to the wide-variety of language skills needed to service a global customer base, collected feedback showed that being able to understand one another had become a main point of complaint.

Eaton gave more attention to language training in the customer service team to improve fluency and triaged particularly unhappy customers to agents with the right language skill set in the future.

“Finding this out was a big win. The amount of complaints about this issue at the moment are close to zero.”—Renan

What The Reviews Say on G2— 9.2/10

CustomerGauge’s G2 has lots of interesting reviews to read through. Here are a couple of examples:

“Reporting was also very good via CustomerGuage, though it takes a while to set up the rules behind a sentiment report it was a very powerful tool for quickly analysing any themes and patterns found within the data collected from a client NPS ask. The report were something I could pull together quickly to present to senior members of staff.”—Enterprise User, >1000 Employees

CustomerGauge has kept their pricing private.

4. Clarabridge (Now part of Qualtrics) - Speech Customer Service Analytics Software

Recently acquired by Qualtrics, the Clarabridge platform has AI text analytics technology at its core which they bring to experience analytics.

Like SentiSum, Clarabridge brings together multiple sources of feedback (survey, call logs, chat, email, social media, etc.) and uses AI analytics to derive meaning from the vast array of qualitative data. Clarabridge specializes in speech analytics including transcription and subsequent text analyses.

These insights are then pushed out to the organization to drive decision-making and real business results.

Key value offering:

Multi-channel customer experience analytics.
Automatically transcribes and analyzes customer call recordings.
Strong integration with Qualtrics, great for existing customers.

While Clarabridge has similar functionality to tools like SentiSum, Clarabridge is unique on this list due to their close relationship with the Qualtrics ecosystem. If you’re looking to combine an enterprise CX program with granular analytics

Example case study:

Clarabridge customer, Acer, ‘was already gleaning valuable information about product defects and replacements from its customer feedback data, but it wanted to gain a deeper understanding of customer challenges and issues.’

To do this, they focused on extracting insights from new channels like support chats and emails. They looked to Clarabridge for text analytics to make sense of the new, vast data set. Like all customer support data it was complex and unstructured, so they need AI analytics to quickly understand it.

What The Reviews Say on G2— 7.6/10

Clarabridge’s G2 has a few existing reviews that should give you an idea of what Qualtrics has added. Here are a couple of examples:

“The analytics are really insightful and it is useful seeing them on the dashboard. You can then drill down and analyze the key strengths and weaknesses of your customer service qualitatively and quantitatively. This data can be connected to future performance metrics and measured over time.”—Mid-Market, Accounting Firm

5. MonkeyLearn - DIY AI Customer Service Analytics Software

MonkeyLearn is an AI-powered text analytics platform with use cases in survey, support, and review analysis. For the best results, you log in to MonkeyLearn and train the AI technology yourself by classifying a handful of customer feedback in the way you want, and then the platform runs on its own for all future feedback.

MonkeyLearn has a customizable reporting suite to help you visualize and deep dive into reasons for contacts and their associated customer sentiments.

Key value offering:

Wide range of use cases—the platform is flexible to your needs and new upcoming projects.
AI-based tags enable automated workflows like routing and prioritization.
Bring multiple channels of feedback into one very visual platform.

MonkeyLearn is unique in this list because of its self-serve capability. They’re the only customer service analytics software that you can set up and run yourself, although this comes with the downside that high levels of granularity and accuracy in the insights are not guaranteed.

Example case study:

MonkeyLearn’s customer Archer, the financial services company, uses Zendesk to communicate with fund managers and their business partners.

“The firm receives a high volume of support tickets every day, many of which are time sensitive, including live trading requests, cash requests, and account maintenance requests.”

As a fast growing company, they had increasingly large volumes of monthly support tickets. “To efficiently absorb our fast-increasing volumes, we required automations to stand in as quarterback of our tickets.”

“Archer leveraged the MonkeyLearn topic classification model. After an extended feedback period, during which Archer’s agents evaluated and “taught” Monkeylearn more about the company’s practices, the classifier’s functionality was fine-tuned to Archer’s needs.”

Read the full case study on the MonkeyLearn website.

What The Reviews Say on G2— 8/10

MonkeyLearn’s G2 has a handful of G2 reviews to read through. Here are a couple of examples:

“It has great models of text analysis which helps in getting accuracy. The most useful part is the way of integration it has. This is very helpful for business workflows. It has a very good API and it is also well-documented. The best part is that it is user-friendly and very flexible.”—Software Engineer, Company <50 Employees

How Does SentiSum Help You Uncover Customer Insights?

We've built one of the most powerful machine learning-based customer analytics softwares around today. SentiSum is built to uncover highly granular customer insights from large volumes of qualitative data in real-time.

We integrate into your help desk, survey system and customer review platform and turn hundreds of thousands of data points into clear, actionable insight. Our, and your, superpower is support tickets. They're high volume, rich sources of unbiased feedback. We're one of the few companies who can automatically make sense of them at scale.
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So, how does this works:
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1. Integrate Sentisum's platform with thousands of different channels.

2. Following the integration, we run your data through our exploration system which extracts important text words and phrases that are being mentioned in the support tickets. These words and phrases help us to identify granular insights that needs to be tagged.

3. We build you a custom AI model based on historic data exploration.

4. Once the model is built and trained, we'll get back to you to kick off your customer analytics. Not only is your historical data organised, so we can tell what's normal and what's an anomaly, but any new support tickets are also tagged and categorised in our system.

5. Detecting anomalies: monitors the daily volume of each topic event and our custom anomaly detection module notifies you if there is an abnormally high number of mentions as compared to the number forecasted by our system

6. For you, this is a seamless one-click experience.

7. We work together to build tagging use cases like auto-prioritisation and triage

Request a demo today and we'll show you around the platform.

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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.