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Customer Service Sentiment Analysis
Relying on NPS and CSAT surveys to understand your customers? You’re missing the full picture.
To understand what customers really think about you, you need to analyze real-time conversations happening across emails, chats, phone calls, and more.
SentiSum analyzes these conversations at scale and uncovers granular insights like key reasons for contact, friction points, and agent performance.
Know exactly what makes customers happy or unhappy. And have quantitative evidence to drive change.
What is Customer Service Sentiment Analysis?
Sentiment analysis in customer service is the process of using AI to analyze customer conversations across multiple channels (emails, chats, calls, surveys, and more) at scale.
It uncovers:
- What your customers like and dislike about your brand
- What issues they’re facing
- What they’d like to see from your products or services in the future
- How well your agents are performing
- Any other advice on how to improve your customer service or product
By leveraging machine learning-based natural language processing (NLP), sentiment analysis tools like SentiSum can accurately process and analyze thousands of customer interactions at scale.
Here’s how it works in a nutshell:
- Collect data from all customer touchpoints. Easy one-click integrations pull in customer conversations from every voice-of-customer (VoC) channel you use, including emails, chats, phone calls, reviews, surveys, social media, and helpdesk tickets.
- Automatically analyze and tag data. Using NLP, sentiment analysis software processes every conversation to identify key phrases and determine the sentiment, emotions, and urgency behind each interaction.
- Deliver trend insights to a unified dashboard. On your dashboard, you can explore sentiment trends and patterns over time to better understand what’s working and where your organization can improve.
With customer feedback sentiment analysis, you can answer these critical questions about your CX:
- How are customers feeling? Gauge overall customer satisfaction and detect emotional trends.
- What issues are making them unhappy or happy? Identify specific issues that drive positive or negative emotions so you can prioritize areas for improvement.
- How can we best resolve those issues? Get to the root causes of customer sentiments so you can implement targeted interventions and more effective resolutions.
AI-powered sentiment analysis gives you and your team access to previously untapped valuable insights, without having to spend time and resources manually collecting and analyzing the data yourselves.
This means you can truly listen to your customers (yes, all of them!) and give them exactly what they want from your brand.
“Talking to customers face to face, en masse, every day is unique to customer service. That close contact is an untapped and unappreciated opportunity to build strong customer relationships, increase loyalty, hear customer feedback, improve products, build community, upsell, increase basket size, and so on.” —Sharad Khandelwal, CEO at SentiSum
4 Key Benefits of Sentiment Analysis in Customer Service
Get these key advantages when you implement sentiment analysis for customer experience channels:
- Identify and address friction points faster.
When you can quickly uncover customer pain points, your team can promptly resolve problems and improve the overall experience.
“Customer service analytics … can help automate parts of the customer service journey—ensuring the customer gets better, faster service,” says SentiSum CEO Sharad Khandelwal.
- Reduce customer bottlenecks and churn.
By understanding the root causes of customer dissatisfaction, you can proactively address concerns and retain more of your customers.
“When combined with customer churn analysis, [sentiment analysis] can give you the kind of insights that can enable you to nip problems in the bud,” writes Piusha Debnath, a customer service expert.
- Improve agent performance.
Sentiment analysis highlights areas for agent improvement so you can provide targeted training and boost your overall service quality.
Improved customer service leads to more revenue: HubSpot data shows that 93% of customers would make repeat purchases with a company they feel provides excellent customer service.
- Prioritize product features and fixes that customers actually want.
When you can identify customers’ most requested features and critical issues, you can focus product development on areas that will have the greatest impact on customer satisfaction.
“[Sentiment analysis insights are] the basis for our strategic and product meetings, giving us a sounding board for understanding our customers and their needs outside of customer surveys,” says Anders Reckendorff, CEO of Scandinavian Biolabs.
5 Ways Real Companies are Using Sentiment Analysis to Improve Customer Experience
Here’s how real companies are using insights from sentiment analysis to drive business results:
1. Improve Service Quality with Speech Sentiment Analysis
Sentiment analysis software can transcribe every phone call and use natural language processing to assign relevant tags.
You can dig into individual calls to see the main reasons for contact, overall sentiment, and issue urgency, and better understand how that fits into larger customer service trends.
You can even improve your support team’s performance with built-in agent analysis tools that show what each person did well, where they could improve, and how the customer felt about their interaction.
Case Study: Gousto integrated all nine of its VoC channels into SentiSum to gain accurate, granular insights into customer sentiment and top contact reasons.
Now, the entire company can make data-driven decisions that reduce customer contact and improve the quality of customer service.
2. Increase Customer Satisfaction with Survey Sentiment Analysis
AI-powered sentiment analysis can interpret open-text survey responses and identify key trends, positive and negative drivers, and sentiment changes over time.
This allows you to address issues promptly, improve overall satisfaction, and reduce churn.
Case Study: Lakrids by Bülow increased its 5/5 CSAT ratings by 9% within six months by leveraging insights from SentiSum.
By looking at the support team’s biggest customer service challenges, the company came up with new best practices for those situations and provided clearer pathways to support customers.
3. Understand Low Score Drivers with NPS Sentiment Analysis
Sentiment analysis gives you key insights into NPS trends, sentiment changes, and top positive and negative drivers.
By cross-referencing NPS responses with other communication channels, you and your team can understand the context behind the scores and work to correct those issues.
Case Study: Butternut Box integrated SentiSum with its support platform Dixa and used our automatic tagging system to dive deeper into the root issues of low NPS scores.
With insight access across all departments, the entire company is now able to focus its efforts on areas that matter most to its customers.
4. Proactively Solve Customer Problems with Review Sentiment Analysis
Sentiment analysis software gives you a comprehensive view of customer sentiment across reviews from all platforms, as well as insights into trends over time.
With this knowledge, you can promptly address negative feedback and make informed product improvements that enhance your brand reputation and CX.
Case Study: British Airways Holidays leveraged the power of sentiment analysis to proactively address and prevent negative experiences that customers brought up across 10,000 reviews and NPS surveys.
Being able to deal with these issues quickly has helped BA Holidays improve CX and customer satisfaction.
5. Reduce Response Times with Support Ticket Sentiment Analysis
When you connect your helpdesk software with a sentiment analysis tool like SentiSum, you get trend data on ticket volume, frequent topics, and sentiment changes.
This allows customer service teams to prioritize urgent issues and improve resolution times.
You can even push those insights back to your helpdesk software to ensure your agents always have the full context of a customer’s situation at their fingertips.
Case Study: Shopify Logistics reduced response times by over 90% thanks to insights from SentiSum, leading to higher CSAT scores and improved cross-department collaboration.
How to Conduct Sentiment Analysis for Customer Service in 4 Steps
Here’s a quick, step-by-step look at how sentiment analysis works with a tool like SentiSum.
Not interested in using AI-powered software to streamline your customer sentiment analysis? We have a free template for you to do this manually (you can learn how in this article).
Step 1: Compile and Analyze Customer Feedback from All Channels
Manually gathering and organizing customer conversation data is time-consuming.
SentiSum automatically syncs data from various channels in a single dashboard, typically with just one click.
Simply choose your channel source and authenticate the connection.
SentiSum also supports custom integrations using APIs so you can add any feedback source you use.
Step 2: Get Accurate, Granular Insights into Key Sentiments, Topics, and Trends
Manually analyzing feedback can lead to biased and subjective insights.
SentiSum’s machine learning-based NLP reads every piece of feedback accurately and automatically to determine exactly how customers feel, why they contacted you, and how urgent their issue is.
This AI-driven approach provides more accurate and detailed insights than manual analysis.
As Kirsty Pinner, Chief Product Officer at SentiSum, explains, “AI can cut through the subjectivity of human opinion and handle complexity extremely well. No other method gives a representation of customer conversations this accurately.”
Step 3: Dig into Each Topic to Prioritize Fixes
Once you have your insights, the next step is to identify which issues to address first.
SentiSum’s dashboard shows key issues and top negative sentiment drivers, along with trends in sentiment over time.
This allows you to prioritize fixing topics that are decreasing in positive sentiment.
For instance, if reviews related to your product's pricing show a decline in positivity, SentiSum can provide deeper insights into why this is happening.
This helps you make informed decisions based on real data, rather than guessing what to fix first.
To save time, you can use SentiSum’s GPT-powered AI assistant, Dig In, to ask specific questions about your data and quickly get summarized insights to share with your team or other departments.
Step 4: Stay on Top of All Your Feedback
If you process hundreds or thousands of customer reviews, manually reading each one isn't practical.
SentiSum ensures no feedback slips through the cracks by sorting each review into clear topics and subtopics.
You can easily filter feedback to find exactly what you need.
For example, if you want to investigate reviews mentioning pricing, simply click on the Price topic in SentiSum’s dashboard to view all relevant reviews.
SentiSum can even translate reviews into your preferred language so every customer’s voice is heard and acted on.
How SentiSum's Sentiment Analysis Tool Works
Automated analysis that truly gets your customers
SentiSum plugs into your help desk and applies accurate, hyper-detailed tags to every customer conversation and CX feedback. Whatever the channel—emails, chats, phone calls, surveys, and reviews, you'll have quality insights on reasons for contact, customer issues, sentiment, and more.
Benefits
Experience automated, consistent tagging at scale
Discover true drivers of NPS, CSAT, and sentiment
Free your agents so that they can focus on customers
Instant answers, infinite insights
Say goodbye to manually digging through data or waiting on ad-hoc reports. Get quick, meaningful answers about your customer experience with SentiSum. It's as easy as asking a question.
Benefits
No more waiting, get the answers you need instantly
Complex insights made easy, no expertise needed
Spend less time on 'what' and more on 'why' and 'how'
Efficient support with real-time analysis
Deliver better customer service expriences with intelligent prioritization, routing, and escalation based on reason for contact, customer sentiment, and urgency.
Benefits
Route urgent issues swiftly to appropriate teams
Automate escalations based on predefined criteria
Decrease response times, increase customer satisfaction
Don't take our word for it
We're proud to be working with these incredible companies, empowering them with actionable insights from their support channels
"SentiSum is easy to set up and the insights are accurate. Every team has started using customer conversation insights!"
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Joe Quinlivan
Head of Customer Care
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“With SentiSum you go in, you open it up, and you see the stories being told to you. You see an increase in a particular topic and immediately understand why."
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Nick Moreton
Director of customer support
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"We're very impressed by the technology itself, but even more so by the relentless effort the team puts in to support our specific use case."
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Johannes Ganter
Head of CRM & Digital
Try SentiSum today
Turn every customer feedback into clear, easy-to-use insights.
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How to Measure the ROI and Success of Customer Sentiment Analysis - What Experts Recommend
Measuring the return on investment and success of customer sentiment analysis is crucial for customer service leaders aiming to enhance their CX strategies.
To do this, you’ll need to track metrics and key performance indicators (KPIs) that provide valuable insights into customer satisfaction and business performance.
These metrics can help you make data-driven decisions that improve customer experience, boost loyalty, and increase revenue.
Here’s what you need to know about choosing the right metrics for your business and some suggestions to help you get started.
How to Choose the Right CX Metrics
When determining what metrics to track, consider ones that reflect both the emotional and practical aspects of the customer experience.
According to CX expert Ginger Conlon, traditional metrics like NPS and CSAT are still valuable, but they don’t capture the “why” behind customer behaviors.
That’s why these metrics should be complemented with KPIs that provide a holistic and more real-time view of the customer journey.
For instance, metrics like customer emotion and sentiment offer deeper insights into what customers are experiencing and how they feel about those experiences.
This is important, as “emotions inspire customer decisions and impact long-range customer journeys,” writes Julie Miller, vice president of product marketing at Clarabridge.
5 CX Metrics to Track
- Top Reasons for Contact. Knowing why customers reach out is crucial for identifying areas needing improvement.
Grouping contact reasons into categories like billing, technical support, and general inquiries helps pinpoint specific issues.
SentiSum's tools automate this process and provide visual data, making it easier for teams to understand and address customer needs.
- Increases/Decreases in Contact Volume. Changes in contact volume indicate shifts in customer preferences or the effectiveness of recent initiatives.
Tracking these trends in a tool like SentiSum helps you identify emerging issues or areas where improvements have been successful os you can prioritize your team’s efforts accordingly.
- Customer Sentiment. Analyzing customer sentiment gives a holistic view of the customer experience.
SentiSum's AI-driven tools can categorize emotions and track sentiment trends over time, helping teams understand the emotional impact of interactions.
- CSAT. CSAT measures how satisfied customers are with your service.
By analyzing all customer interactions across channels, SentiSum helps uncover the reasons behind CSAT scores.
This deeper understanding allows teams to make informed changes to improve customer satisfaction.
- NPS. NPS measures customer loyalty by asking how likely customers are to recommend your company.
Analyzing the drivers behind NPS scores helps identify areas for improvement.
SentiSum's tools provide detailed insights into why customers gave specific scores, allowing for targeted actions to boost loyalty and retention.
- Average Handle Time. Average handle time (AHT) measures the average duration agents spend resolving customer issues from start to finish.
Tracking AHT helps identify areas where processes can be streamlined to improve efficiency.
SentiSum's AI-powered analytics can pinpoint the root causes of prolonged handle times so you can optimize your team’s processes.
Whichever metrics you choose to track, be sure to align them across departments for a more unified understanding and approach to improving CX.
Try SentiSum today
Turn every customer feedback into clear, easy-to-use insights.
Frequently Asked Questions
AI uses advanced algorithms to automatically identify and interpret the emotions and opinions expressed in customer communications.
SentiSum’s AI processes large volumes of unstructured data, such as emails, chats, and voice calls, to allow you to understand customer feelings better, identify trends, and proactively address issues before they escalate.
Machine learning-based natural language processing (NLP) is the most effective for sentiment analysis. SentiSum’s NLP model is trained to understand and interpret human language, even with linguistic nuances and imperfect grammar.
Since SentiSum’s NLP continually learns and improves based on historical data, you’ll always have the most accurate insights from customer conversations on sentiments, topics, and key issues.
Here are six primary categories of sentiment analysis that might apply to your business.
1. Lexicon-based sentiment analysis leverages a predefined list of positive and negative words to determine sentiment; however, it can misinterpret context.
2. Machine learning-based sentiment analysis uses algorithms to learn from data and capture nuances and complexities in language.
3. Hybrid sentiment analysis combines lexicon-based and machine-learning approaches to balance context understanding and efficiency, but it may inherit limitations from both methods.
4. Visual sentiment analysis analyzes visual content, such as images and videos, to gauge sentiment through visual cues. It can be helpful for social media monitoring but is limited by current image recognition technology.
5. Multimodal sentiment analysis integrates text, audio, and visual data to provide a comprehensive sentiment analysis. It is complex to implement and requires advanced analytical capabilities.
6. Aspect-based sentiment analysis breaks down text into specific aspects (topics and subtopics) to assess sentiment, offering detailed insights into customer preferences and areas for improvement.
SentiSum can analyze customer interactions from all the channels you use in a single dashboard.
Aside from voice calls, you can also pull in data from your customer emails, chat messages, social media posts, support tickets, and customer feedback surveys for a holistic view of customer sentiments and issues.
SentiSum integrates seamlessly with major helpdesk platforms like Zendesk, Freshdesk, Dixa, Intercom, Gorgias, and more.
The easy, one-click integration allows for real-time data synchronization and automatic tagging within your SentiSum dashboard.
Best of all, you can push insights and automation rules back to your helpdesk platform so all your conversational data is accessible in one place.
SentiSum offers comprehensive onboarding and training programs for new users.
This includes personalized training sessions, detailed documentation, and ongoing support from our customer success team to ensure you can effectively leverage the platform's features and maximize its benefits.
After your initial onboarding, you can take advantage of our numerous SentiSum resources, including webinars, podcasts, blog posts, and in-depth guides to make the most of your conversation analytics efforts.
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