Algorithms analysing sentiments attempt to gauge emotions based on how someone is communicating. In business environments that include phone systems, these algorithms can convert customer calls into valuable verticals. Sentiment analysis, when applied to language and voice, allows organisations to enhance the quality of their services and the overall experience of their customers.
Analytics that Convert Voice into Text
Sentiment analysis begins with call analytics to recognise speech. Call audio is converted into text, allowing transcription and processing of analytics. Therefore, organisations can analyse huge amounts of call data to identify trends and take appropriate actions, all without having individuals replay audio.
Analysing Words and Their Meaning
After a call is transcribed into text, the algorithm processes the call and analyses the different words and phrases present. Through the use of language patterns, the algorithm determines whether the sentiment is expressed as positive, negative or neutral. Given the current technology, understanding the context of a phrase allows the algorithm to determine a complete and accurate meaning to avoid some phrases which may have a different intention or meaning.
Interpreting Emotion and Vocal Expression
Voice analysis is an equally important element in telephony sentiment analysis. Emotion is captured in the voice. While the words spoken may be neutral, elements such as pitch, rate and volume (along with other factors such as how long a pause is) may indicate frustration, urgency or satisfaction.
Machine Learning and Model Training
Sentiment analysis uses algorithms developed using machine learning principles and trained on big data of actual human conversations. These algorithms understand the nexus of language and the emotional state conveyed through voice. The more the data the algorithms process, the better they get in terms of accuracy and versatility in the various situations encountered in the calls.
Real-Time and Post-Call Analysis
While some systems of sentiment analysis analyse situations after the call and provide analysis and training documentation, others analyse calls in real time to see how the sentiment changes during the call. This helps the organisation in managing the negative situations and also to help the agents in the calls.
Turning Insights Into Action
Sentiment analysis makes the results available via a score and dashboard in the telephony system and sentiment analysis is usable. This helps to improve the performance of agents and to solve the problem of frequently repeating issues in customers. This makes the analysis more of a functional tool, not a technical tool.
Why Sentiment Analysis Matters
Sentiment analysis helps the organisation in understanding the actual problem and better the outcome of the communication. If integrated with the call systems, an analysis will provide insight into the emotional attachment and the satisfaction level of the customer. Today’s sentiment analysis is becoming a crucial part of an organisation’s communication as the customer is becoming more demanding and requires quick responses.
Enhancing the Customer Experience
Sentiment analysis provides great insight into how customers feel about different aspects of the phone call. Teams can pinpoint customer frustration, confusion or satisfaction; and modify how they engage customers and resolve concerns. This fosters a positive customer experience and enhances the camaraderie.
Aiding Agent Performance
Sentiment analysis provides an excellent understanding of the agent performance. It is possible to determine agent performance and where additional mentoring is required. This ensures accurate coaching based on actual conversations, not presumptions.
Spotting Problems Early
Sentiment analysis can aid in the identification of frequent negative sentiments during phone calls in the analysis of multiple calls. This provides insight about potential problems from customer complaints, such as prolonged hold times, lack of clarity in content or product-related concerns. It is possible to focus on the root problem to minimise the volume of complaints and improve the customer service.
Allowing for Real-Time Adjustment
Sentiment analysis provides an avenue to modify the negative experience a customer has during the call. It provides a pathway for a supervisor or agent to execute changes to reduce the level of frustration before a caller becomes calmed.
Making Better Business Choices
Sentiment analysis facilitates informed decision making in staffing and processes, establishing better customer service strategies and focusing improvements based on real customer feedback.
