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Table of Contents
Introduction
It’s not enough to just mechanically go through the motions when solving customer problems. It’s also important to ensure that each of your customers feels heard and valued. To ensure that that happens, many call centers use sentiment analysis, diving deep into the data to get a better understanding of how their customers feel about their products and services, and using those insights to drive better decisions. In this guide, let’s take a look at what sentiment analysis is, how it works within a contact center environment, and how it enhances the customer experience.
Key takeaways
- Customer service with emotional intelligence: Analysis of customer opinions, both in real time and after the fact, allows agents to provide customer support with more empathy and customization, going beyond just the traditional metrics.
- Boost CX and agent performance: By identifying the most common customer pain points and tracking overall customer satisfaction trends over time, call centers can provide targeted training to improve agent performance and learn which agent behaviors are the most successful at providing amazing CX.
- Privacy and integration: The best sentiment analysis tool for the job will smoothly integrate with your CRM as well as comply with the latest data protection standards, such as GDPR, CCPA, and more.
What is sentiment analysis?
Sentiment analysis is a process that uses AI-powered tools to interpret not just the content of customer conversations but the emotions behind them. Call centers often use sentiment analysis to determine whether the customer has positive, negative, or neutral feelings, which can provide insights that aren’t usually captured by standard metrics like resolution rates or handle times.
Sentiment analysis uses Natural Language Processing (NLP) and machine learning to decipher human speech and text, breaking things down by precise word choice, context, and other factors. These analyses, when compared with historical data, uncover patterns that can better quantify customer emotions and sentiment.
Questions that can be answered using sentiment analysis
It’s not just about knowing how your customers are feeling. Sentiment analysis also allows you to uncover why they feel the way they do. Through careful examination of patterns in conversations and the emotional cues expressed within them, businesses are able to gain insights that inform what they are doing correctly, where they stand to improve, and missed opportunities where they can strengthen their support operations. Here are some questions you can answer through sentiment analysis to gain valuable insights.
How satisfied are customers after interactions?
Sentiment analysis listens closely to the emotional tone that conversations take, determining if customers are leaving the interaction feeling satisfied, neutral, or dissatisfied. With this information in hand, teams can identify when customer support is truly improving the customer relationship, or when it’s merely doing the bare minimum to close support tickets.
Which phrases or tones indicate customer frustration?
Sentiment analysis is able to hone in on the keywords, vocal patterns, and sentence structures that subtly (or not so subtly) indicate negativity, usually in the form of frustration and anger. As time goes on, each of those data points adds up, further training the system to recognize the warning signs of negative customer experiences and empowering agents to more quickly respond to problems while they’re still brewing.
How effective are agents in calming negative sentiments?
Not every customer stays frustrated. Through sentiment analysis, you can identify the cases where agents successfully changed a negative sentiment to a neutral or even positive one. Not only does this inform you of which strategies work to de-escalate conflicts, but it also highlights which agents are performing well and are most successful at managing rough conversations.
Are there trends in customer satisfaction over time?
With sentiment analysis, call centers are better able to keep track of how customer sentiment is changing as time passes, whether that be week to week, month to month, or after key events. Perhaps after a product launch, a call center will notice rising customer frustration, which would indicate something has gone wrong with the launch and prompt the call center to take action accordingly. This kind of awareness is crucial for continually improving the customer experience and the overall health of your brand.
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Why is sentiment analysis important for call centers?
Emotional intelligence is a huge asset for call center agents, and it goes beyond surface-level metrics. Sentiment analysis draws agents’ attention towards those subtler indicators, encouraging emotional intelligence and empathy in each conversation. When agents understand the way customers feel, they have a better handle on how to customize their service fro the customer’s unique support needs, ensuring a more positive experience that fosters loyalty.
Agent development is also an important use of sentiment analysis. Through sentiment data, supervisors can find growth opportunities in agent performance, enabling more specific and actionable feedback that allows agents to improve their customer care skills.
How does sentiment analysis work?
Understanding how sentiment analysis works behind the scenes allows contact centers to make the best use of the powerful technologies at play.
Real-time vs. post-call analysis
Real-time sentiment analysis analyzes the emotional tone of a live call, making it possible to detect frustration and other negative feelings in the moment, cluing in agents when things might be going wrong and prompting them to adjust their approach or escalate to a supervisor when appropriate. Real-time analysis is extremely handy for helping customers to feel heard and providing greater results in the moment.
Conversely, post-call sentiment analysis takes a look at the recordings, transcripts, and chat logs of each customer interaction after the fact, which is perfect for gathering long-term insights, detecting patterns and trends, and identifying opportunities for targeted agent training to improve future outcomes.
Key technologies used in sentiment analysis
The three key technologies at play behind sentiment analysis are Natural Language Processing (NLP), speech recognition, and tone detection. Each plays an important role in deciphering human speech and providing a multi-dimensional picture of the emotion behind both written and spoken customer interactions.
Types of contact center sentiment analysis methods
Depending on the communication channels and data sources you have, sentiment analysis may require different approaches.
- Text-based sentiment analysis: Text-based sentiment analysis is ideal for examining written communications from customers, everything from emails and chat messages to call transcripts. Through NLP, text-based analysis interprets the precise word choices and sentence structures that customers use, as well as the context in which they are used, in order to determine the emotional tone of the messaging. Text-based methods alone are particularly useful in channels where voice inflection isn’t available to go off of, and any cues must be gleaned from the text itself.
- Speech-based sentiment analysis: Speech-based sentiment analysis hones in on vocal indicators in human speech (things like tone, pace, volume, and pitch), which all offer subtle cues as to how a customer actually feels. It’s a great method to use in situations where text alone can miss the point.
- Hybrid sentiment analysis: Combining the best of both worlds, hybrid sentiment analysis provides the most complete picture of the customer emotion available, layering the explicitly expressed words with vocal cues that were used to express them.
Types of contact center sentiment analysis methods
Call centers often find that implementing sentiment analysis can boost the customer journey in various different ways, including:
Enhanced customer experience
Sentiment data enables call centers to personalize the customer service they provide, both in real-time and in follow-ups after the fact, providing more thoughtful, emotionally intelligent service. When customers express their frustration on a call, those interactions can be flagged early for follow-up or escalation to ensure that the situation is noticed and handled right away, showing customers that their feelings and experience are acknowledged and appreciated.
Improved agent performance
The analysis is also helpful for agents, identifying the patterns of cause and effect between agent actions and positive customer outcomes. Managers are able to use sentiment data to understand which agent behaviors tend to produce the best results and drive customer satisfaction, allowing for targeted training as well as recognition for the agents who provide the highest quality of service.
Reduced escalations
When negative sentiments are identified early on in the conversation, contact centers can intervene to prevent escalation. Whether the solution is live coaching, automated prompts, or something else, sentiment analysis empowers teams to make effective use of the tools at their disposal in order to effectively handle issues proactively, making it less likely that customers will need to talk to someone higher in the chain of command during a given interaction.
What to look for in a contact center sentiment analysis solution
If you want the most value out of a sentiment analysis tool for your contact center, you’ll need to ensure that it slots seamlessly into your current operations with flexibility and scalability. Some of the most important features to keep in mind when shopping for sentiment analysis platforms include:
Integration capabilities
The best sentiment analysis tools will easily integrate with your current CRM system, along with your help desk software and your preferred communication platforms. That seamless integration allows your sentiment data to stay available to your team at all times, and does so within the familiar tools that they use every day.
Real-time reporting
Whether you need to escalate a heated conversation to a manager or you need to follow up with a customer for the recommended next steps, real-time reporting allows you to turn sentiment analysis data into immediate action.
Scalability and customization
Your business likely doesn’t have the exact same needs as others in your industry, so your sentiment analysis platform should reflect your unique needs, with flexible options that you can customize to your business. Additionally, the ideal platform will scale with you, providing useful sentiment data whether you’re working with a small team or a global workforce.
Data privacy and security features
Sentiment analysis touches on sensitive customer information, so wherever they are used, it’s vital that sentiment analysis systems adhere strictly to the most current data protection standards, compliant with regulations like CCPA, GDPR, and others.
Conclusion
Contact centers that use sentiment analysis are enabled to better understand their customers’ needs and the concerns brought up in each conversation. Through properly utilized sentiment analysis, changing customer emotions into something more quantitative, agents can provide more empathetic service and drive smarter business decisions.
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