We are speaking with many customer service leaders, and their main concerns, in general, are that they are receiving too late or not at all insights about their performance or their customer sentiment shift toward a specific topic or across the whole brand level.
This is a significant issue because contact centers contain valuable data that can be used to reduce operational costs, address customer turnover, and improve products. In short, the data hidden in contact centers could help businesses grow faster and more sustainably.
Voice call sentiment analysis tools automatically extract topics, subtopics, sentiments, and trends from interactions between agents and customers.
What is Voice Sentiment Analysis in Call Center?
Sentiment analysis, also known as opinion mining, analyzes people's sentiments, attitudes, and feelings toward various entities, such as products, services, individuals, organizations, topics, and events. This process utilizes Natural Language Processing (NLP) and machine learning to interpret and categorize sentiments expressed in textual data.
We could say that in the past, contact center managers handled a lot of tasks and spent most of their resources on manual work. Unfortunately, this is still the case today in many instances. Of course, if you work for a large enterprise, you might be lucky enough to not spend as much time and energy on manual tasks such as:
- They spend their agent's time categorizing each conversation as it occurs or in one-off analysis projects.
- Listening to a few phone calls, documenting sample conversations in Excel, and attempting to draw conclusions is the typical approach for most quality assurance processes.
These methods offer only a somewhat insightful view of what is causing customer contact and sentiment. However, the results are often biased, inaccurate, or delayed.
AI enables your analytics to be more accurate and detailed, cutting through the subjectivity of human opinion and providing real-time insights.
It transcribes each voice call, similar to a human, and then categorizes it based on predetermined tags such as sentiment, topic, intent, and urgency.
This is particularly beneficial for processing a large volume of voice calls daily. AI reads and analyzes the transcriptions, providing easily understandable quantifiable insights.
How do voice call sentiment analysis tools work?
Here's a quick overview of conducting sentiment analysis within a minute, detecting negative sentiment drivers, performing topic analysis, and summarizing key issues thanks to our Mantra AI Co-Pilot.
Integration with your existing help desk platforms.
The sentiment analysis tool initially imports voice call data from your helpdesk platforms. For instance, ClientZen integrates with the majority of helpdesk platforms, both major and minor.
- Zendesk
- Freshdesk
- Gorgias
But aside from voice calls, ClientZen consolidates data from various feedback channels, including surveys, customer support chats, emails, and more, into one dashboard.
AI analyses voice calls - tagging at scale
ClientZen uses machine learning-based artificial intelligence to automatically analyze and apply detailed tags, eliminating manual work and the need for agents to listen to calls one by one or add tags.
Why not perform AI analysis with Zendesk built-in tools? We already covered this topic in our past article, "Zendesk ticket analysis: The best way to Analyze your Support Tickets," where you can revisit and learn the main difference between keyword extraction, rule-based NLP, and machine learning NLP.
Customer Call Sentiment Metrics and KPIs
Once the analysis and the tagging process is completed, which usually takes a few seconds if you're using ClientZen, in our platform, you'll see all the main metrics made for agents to track any pain point easily and transparently to extract key insights from your client interaction within just a few seconds:
- Customer trends over time
- Main issues for contact
- Top increases and decreases in sentiment and volume
- Negative sentiment drivers
- customer churn over time
- NPS and CSAT score trends
- Real-time customer interactions
Push your insights into action points in just a click with Mantra AI
Detecting the problem is one thing, but taking action is totally different. Instead of just analyzing your customer interaction, turn those insights into solutions using Mantra AI Co-Pilot.
How to use voice call sentiment analysis to boost you call centers agent
Let's see how you can use sentiment analysis to actually achieve results and boost your call center agents' performance.
Understand and detect negative sentiment drivers
ClientZen's AI works like an assistant designed to help your work and enhance your call center game.
This helps your agents understand which issues to address first, taking into account both the sentiment and urgency of the customers (e.g. quickly attending to angry customers).
There are many ways to apply AI to human speech and text, but mentioning just a few of them in terms of importance in call centers:
- Sentiment analysis determines whether human speech or text is positive, neutral, or negative.
- Topic analysis assigns topic tags or categories to text based on the underlying meaning, reason for contact, or theme.
These techniques form what we call topic-based sentiment analysis. For contact centers, this is the holy grail of insights.
Let’s say your voice sentiment analysis tool identifies 20 topics that drive 90% of customer contact. In this situation, how do you prioritize which of these 20 issues to fix first?
Your first instinct might be to fix the highest frequency one first, which would directly reduce negative sentiment drivers…right?
However, with an additional layer of sentiment analysis, you might discover that the most frequent reason for contact for a specific topic might not necessarily bother your customers that much.
Instead, you might see that a single topic might be the cause of customer sentiment drivers – driving negative reviews and bad word of mouth.
With this insight at hand, you can:
- Find key drivers of negative sentiment and prioritize fixes accordingly.
- Build evidence-based cases to encourage change across the company that reduces ticket volume and improves customer experience.
Restructure and prioritize based on sentiment
Our prior example of volume vs. negative sentiment drivers was just a great way to illustrate the difference and how important and critical a small difference like this can be. Let's elevate this task.
You'll need full and advanced taxonomy to properly structure and prioritize your to-do's.
Accurate, specific tags are essential for identifying highly negative sentiment and urgent issues. You want your agents to resolve these issues quickly.
ClientZen automatically applies smart tags based on customer interactions, categorizes customer communication easily, and uses semantic tagging to detect negative drivers.
Instant benefits that you'll get:
- Specific to your customer feedback and context.
- A holistic view of the entire customer lifecycle.
- No more manually digging through piles of data.
Share insights company-wide to improve CX
One of the major ways you can use your voice call insights is to tackle friction points in the customer journey that drive customer churn.
Our clients at Plannable chose ClientZen not just because it highly impacted their overall customer experience and reduced negative sentiment drivers while maintaining a balanced workload within their team, but also because ClientZen's dashboard is so intuitive and user-friendly that the learning curve for the whole company took just a few hours.
Now, Plannable's team uses ClientZen on a company-wide level, exporting each team and staff their preferred reports. They no longer spend as much time in meetings, since every team member can rely on Mantra AI's co-pilot and ask the AI directly about a specific problem, leaving space and energy for agents to work on enhancing their customer experience with more strategic approaches.
Use NPS and CSAT surveys to analyze sentiment
Contact centers often conduct NPS and Customer Satisfaction Score (CSAT) surveys. However, analyzing and understanding survey results at scale can be challenging. Text analytics can quickly extract key sentiments, patterns, and topics from these surveys, allowing you to promptly identify the pain points and areas of concern for your customers. This enables you to address issues at the root, reducing churn and enhancing the overall customer experience. Additionally, you can use AI to uncover trends, identify key drivers of CSAT and NPS, and delve deeper into the root causes of issues.
ClientZen utilizes text and sentiment analytics to automate the analysis of CSAT survey results. It even links support ticket topics to CSAT scores, providing insights into why a negative score was given, even if the customer did not leave a comment.
How to choose the Best Voice Call Sentiment Analysis Tools
There are many tools in the market but many help desk software's don't have sentiment analysis capabilities, so you'll need to integrate with a specialized tools.
Your options might be limited, as only a handful of software companies offer full services when it comes to automation and customer sentiment analysis.
A few things we recommend you checking before choosing the right tool for your organization:
Securely connect your source of customer intelligence
Choose an all-in-one analytics tool that gathers feedback from all channels—surveys, reviews, chats, emails, and calls—into a unified dashboard, eliminating the need for multiple tools and saving time. This provides a comprehensive view of customer feedback in one place.
Automated Customer Feedback Analysis for contact center
We highly encourage to choose a tool that applies smart semantic tags automatically, to every customer feedback (tickets, reviews, calls & more). With a call center sentiment analysis tool like ClientZen, you can double your customer base easily without stressing about handling high support volume, ClientZen reduces time spent on feedback analysis by 20% within just a few months.
Accurate Customer Insights Analysis with Full automated taxonomy
From uncovering contact reasons to identifying recurring issues and top feature requests, you'll need a tool that delivers precise and actionable customer insights in a minute with just a click.
By organizing and categorizing all customer inquiries, you can effortlessly meet customer expectations by proactively analyzing their sentiment, predicting churn rates, and identifying negative issues.
Instant answers with AI Co-Pilot Assistant
From predictive analytics to generative AI, choose a tool that offers unparalleled insights into customer behavior, enhancing the entire customer journey.
Unlike other pre-trained AI models on the market, Mantra AI by ClientZen uses your customer data as a knowledge base to quickly learn your customer sentiment and desires within an instance.
Are you ready to boost contact center to a totally new level? Schedule a demo with ClientZen to witness it in action for yourself.
Customer feedback made easy
Customer feedback tagged automatically
Real-time customer sentiment scores
Pain-points evolution over time
