Sentiment Analysis (SA) is a natural language processing (NLP) technique that involves determining the sentiment or emotional tone expressed in a piece of text (including transcripts of voice interactions). The goal of sentiment analysis is to categorize the text as having a positive, negative, or neutral sentiment.
Sentiment analysis can be particularly valuable in a call center environment, where customer interactions are often rich in textual and verbal content. Here's how you can use sentiment analysis to improve customer experience in a call center:
1. Implement Real Time Service Recovery
Implement sentiment analysis tools that can analyze call center conversations in real time. This allows supervisors to monitor the sentiment of ongoing interactions and take immediate action if negative sentiments arise.
Traditionally, supervisors only indication of a call gone wrong was by live monitoring or by reviewing "abnormal" conversations (typically characterized by long handle times). Both of these approaches are scatter shot at best and don’t reliably allow supervisors to home in on service issues with any level of accuracy and precision.
With real-time SA in place supervisors can create dashboards that shows calls with low sentiment scores and provide real-time coaching to an agent or actively engage with the caller to ensure issues are resolved adequately before they turn into complaints.
2. Agent Performance Evaluation
Analyze the sentiment of customer interactions to evaluate the performance of call center agents. Identify agents who consistently generate positive sentiment and those who might need additional training to handle difficult interactions. Historically agent performance was based on metrics like handle or wrap time. Often CSAT scores or internal audit scores are also added to the mix as well.
The challenge is those metrics don’t indicate if a call left a customer delighted or frustrated (in the case of handle and wrap time) or reach a small number of calls handled (in the case of CSAT or internal audits). SA allows you to gauge agent performance across all calls and determine which agents are leaving customers frustrated and which are delighting your customers.
3. Real-Time Feedback to Agents
Provide agents with real-time sentiment feedback during calls. If a conversation is turning negative, agents can adjust their approach to de-escalate the situation and improve the customer experience.
While many contact center leaders believe agents can intuitively understand when a customer is frustrated, agents may not pick up on more subtle forms of frustration that easily go undetected.
There are many times where a caller’s tone may not indicate frustration, but they are using words or phrases that provide valuable clues to their growing frustration prior to a change in tone or inflection. This allows agents to short-circuit the negative spiral of frustration that often turns into anger.
4. Automatic Sentiment Tagging
Automatically tag call recordings with sentiment labels (positive, negative, neutral) for easy categorization and analysis. This helps in quickly identifying customer sentiment trends across different calls.
5. Customer Insights and Trend Analysis
Analyze sentiment data over time to identify trends and recurring issues. This can guide call center managers in making informed decisions about training, process improvements, and customer communication strategies. Did you recently change your script? Wouldn’t it be helpful to know if this change created positive emotional change in the customers you are servicing?
6. Script Optimization
Analyze sentiment to optimize call center scripts. SA lets you introduce a new level of granularity to your script review process and allows you to home in on specific key segments or questions that need to be revamped because they are negatively impacting sentiment.
7. Feedback Loop and Training
Use sentiment analysis insights to provide targeted training to call center agents. Share examples of positive and negative sentiment interactions to demonstrate best practices.
8. Customer Satisfaction Prediction
Over time, sentiment analysis can help predict customer satisfaction levels based on call content. This information can be used to proactively address concerns and ensure a positive experience.
9. Continuous Improvement
Encourage a culture of continuous improvement by using sentiment analysis results as a basis for regular performance reviews, training updates, and process refinements.
10. Agent Empowerment
Equip agents with sentiment analysis tools that offer real-time sentiment insights. This empowers them to adapt their communication style and address customer concerns effectively.
11. Chatbot Intervention
Proactively offer consumers using your chatbot the ability to interact with a live agent if you detect negative sentiment in a chatbot interaction. Pro tip: move these users to the top of the queue to avoid further frustration.
Sentiment analysis in a call center context empowers you to enhance agent performance, address customer issues more effectively, and create a more positive customer experience. By integrating sentiment analysis into your call center operations, you can drive improvements that lead to higher customer satisfaction, improved agent morale, and stronger customer relationships.
Want to learn more about tools that support sentiment analysis and how to deploy them to upgrade your customer experience? Contact us here.