Do you know how your customers—and agents—feel about their service interactions?
If you’re relying on low-response-rate customer surveys or anecdotal employee feedback, you’re not getting the full story.
As a Product Manager at Creovai, I work on AI and machine learning features that give call center leaders valuable insights into how their customers and agents feel based on the words they use in their conversations. One of our recent focuses has been developing a sentiment analysis model that provides customer and agent sentiment scores for call center conversations. It’s been rewarding to roll this out to our clients and see how they’re using it not only to understand their call center conversations, but to take action to improve the agent and customer experience.
As call center interactions increase in complexity and customers base their buying decisions on the service experience, sentiment analysis is no longer just a nice-to-have. It’s essential to understand the factors impacting agent and customer sentiment so you can deliver consistently high-quality service, maintain a strong brand reputation, and build customer loyalty.
Sentiment analysis: What call center leaders need to know
Sentiment analysis is used in different ways across different departments, so let me take a step back and explain what it looks like in the call center.
Sentiment analysis is a type of technology that uses AI (and specifically machine learning) to analyze spoken or written words and identify the emotions associated with them. Call center leaders use sentiment analysis to understand when—and why—customers have positive or negative feelings about their service interactions.
There are different ways sentiment can be measured and classified depending on the technology you’re using. For example, Creovai has been using category-based labels like “Confusion” and “Frustration” for years to flag moments in conversations when agents or customers express specific emotions. Category hits occur whenever a customer or agent uses a phrase that a machine learning model has identified as associated with that specific emotion. (Fun fact: our “Customer Frustration” category is built on over 3,000 phrases.)
Recently, we’ve also developed a sentiment-scoring feature using large language model (LLM) technology. This feature provides sentiment scores (positive, negative, or neutral) for both the customer and agent in every interaction. We built separate scores for the customer and agent to help call center leaders track how customers feel about products and services and how agents feel about their work.
Sentiment analysis is a powerful tool for improving the agent and customer experience. When you track sentiment across every conversation, you can identify the controllable factors impacting how your customers and agents feel about their interactions. Armed with those insights, you can start making targeted improvements in your call center.
5 use cases for sentiment analysis in the call center
Knowing how your agents and customers feel is just the start. It’s what you do with that information that matters. Here are five of the top ways I’ve seen Creovai’s clients use sentiment analysis to make meaningful improvements.
1. Root cause analysis
Sentiment analysis can show you when your customers are unhappy—and what factors are causing that. Creovai offers Root Cause Analysis, which uses advanced statistical models to identify correlated factors in your conversations. You can run an analysis against your sentiment scores to understand the top product issues, journey friction points, or agent behaviors causing negative sentiment. Then you can prioritize fixes or coaching initiatives to address those issues and improve the customer experience.
2. Customer data enrichment
You can integrate a sentiment analysis tool with your customer interaction platforms and data sources to better understand and improve your overall customer journey. For example, Creovai can ingest conversation data from multiple sources, including calls and chats, and analyze every interaction for sentiment. It can then push sentiment scores out to CRMs like Zendesk or Salesforce so you can enrich your reporting or perform automated actions. This gives you a feedback loop—you can track sentiment at the level of the conversation and use that data to improve future customer interactions.
3. Automated responses and escalations
You may want to follow up directly with customers after negative interactions—and sentiment analysis can help with this too. Creovai lets you set up automated workflows so that when a conversation gets a negative sentiment score, it triggers a specific action. You could send an automatic notification to an escalation team or assign a follow-up task in your integrated CRM. A team member can then review the interaction and determine whether they need to reach out to the customer or perform some other action. This can help your company course-correct on potentially bad experiences, protect your brand reputation, and even save customers who are a churn risk.
4. Training and development
One of the main ways I’ve seen Creovai clients using sentiment analysis is to improve their agent training and development. Sentiment analysis helps you pinpoint specific agent behaviors that are impacting the customer experience. Your managers can use this information to tailor their coaching sessions around each agent’s top areas for improvement or organize focal point sprints to help the entire team improve on a specific behavior.
5. Real-time monitoring and feedback
Your agents don’t have to wait until a conversation is over to understand how customers feel. By integrating your sentiment analysis tool with your real-time guidance platform, you can give agents instant feedback on a customer’s emotions, allowing them to adjust their approach on the fly.
5 tips for getting the most out of sentiment analysis
There are tons of possibilities for using sentiment analysis in the call center, and it can be hard to know where to start. Here are my top five recommendations for maximizing the value of your new sentiment analysis tool:
1. Start with a narrow focus
Your sentiment analysis tool may uncover a long list of factors negatively impacting customer sentiment. That’s great information, but you can’t tackle everything at once. I always recommend starting with a pilot project where you focus on one specific controllable area, such as a product issue, service friction point, and agent behavior.
For example, let’s say you discover that one of the top drivers of negative agent and customer sentiment is a product that frequently causes agent confusion. Digging deeper, you might discover that customers are asking about a new product that agents haven’t been fully trained on, leading agents to tell customers they can’t answer their questions. This will give you a specific issue you can tackle—which leads to my next tip.
2. Launch a pilot initiative based on sentiment insights
Once you’ve figured out your focus for your pilot project, it’s time to act. In the example I shared above, you could create new knowledge base content and train your agents on the new product, helping them to be more confident in their product knowledge and accurately answer your customers’ questions.
3. Monitor and validate
Track your sentiment scores before and after your pilot project to understand how the initiative impacted sentiment. If you can show the change had a positive impact (i.e., your sentiment scores improved), it will help you get buy-in for your next sentiment improvement initiative.
4. Train your team
Once you get comfortable with your sentiment analysis tool, bring your team in on the action. Train your team on interpreting sentiment data and identifying the top opportunities to improve sentiment (it helps if you’re using a platform with easy-to-read dashboards and easy-to-build reports). Involving your team members (and even other departmental leaders) will foster a culture of data-driven decision-making and continuous improvement.
5. Integrate sentiment analysis into your workflows
I already talked about the benefits of building automated workflows around sentiment analysis, but they bear repeating. When you can trigger specific actions (such as an email notification or follow-up task for your escalation team) as soon as a negative interaction occurs, your company gets a valuable opportunity to turn the experience around before the customer writes a bad review, submits a negative survey, or leaves your business for good.
Improving the agent and customer experience with sentiment insights
Your agents and customers are already telling you how they feel about their interactions—you don’t need to wait to hear from them in a survey. They’re providing valuable insights in their conversations, and it’s up to you as a call center leader to interpret and act on those insights.
Sentiment analysis is a valuable tool for helping you understand what’s happening at the individual conversation level and at scale. It turns unstructured conversation data into insights about customer and agent sentiments, helping you find your top opportunities for improving your call center experience.
