In a recent report, customers reported that their main criteria for trusting a company was their customer service experience.
A great customer experience can go a long way in converting someone into a repeat customer, and the advent of machine learning models and AI tools has led to the creation of new metrics and parameters.
They help organizations better understand how customers feel about products, services and agent interactions.
This blog will be exploring sentiment and emotion analysis and how it can help contact centers provide a better customer experience.
Sentiment analysis or opinion mining is an active area of research in Natural Language processing. It aims to extract people’s opinions and views towards specific topics. In agent-customer interactions, sentiment analysis is used to detect customers’ opinions towards service and the product and as a measure to check customers’ overall satisfaction.
Contact centers are the first point of contact where customer can voice their issues and get the necessary help. And resolving customers’ problems while providing a satisfactory overall experience can strengthen a customer's relationship and loyalty towards the company. Therefore companies need to anticipate and address any negative experiences as soon as possible.
But as the number of interactions between the customer and the agent increases, it becomes increasingly difficult to detect cases where a customer was not happy or, worse, angry. The situation could worsen if the agent did not handle such cases appropriately. Thus, it’s crucial to monitor customers’ sentiments and emotions to promptly mitigate problems and bring them to the attention of a supervisor or manager to resolve them as soon as possible.
There are many off-the-shelf sentiment models available which can detect positive, negative, or neutral sentiment from text. These models are either trained on small datasets where text length does not vary much or on data from different sources resulting in many false negatives when tested on the contact center’s data. Even sentiment models from Google, Amazon & Azure deliver poor precision on detecting actual negatives. At Emtropy, we have trained sentiment models over an immense amount of data from contact centers containing chat, email, and transcripts from voice calls.
Sentiment analysis is excellent and can provide meaningful insights, but it is limited to textual data, i.e., chat & email only. To extract insights from voice calls, we use Speech Emotion Recognition, which analyzes the tonality of the speech and extracts the emotion.
Emotion is a response that occurs when we experience an event, with chemicals being released in our brains that make us feel positively or negatively about an event. Most of the customers’ decisions about assessing the quality of service and product’s value are influenced by positive or negative emotions. Identifying intense emotions like Anger, Frustration, etc., and handling them empathetically in a timely manner can positively impact the customers’ expectations, resulting in enhanced customer experience and loyalty.
Speech Emotion Recognition is a hot research topic with active interest from the research community, but it suffers from a scarcity of high-quality training data. Due to the complex nature of the task and the lack of data, building good Speech Emotion Recognition becomes challenging and time-consuming, and there aren’t any good enough off-the-shelf options available.
At Emtropy, we have trained a Speech Emotion Recognition model over a large amount of high-quality annotated speech data from contact centers. It utilizes the recent advances in Speech Processing & Computer Vision to analyze the tonality of customers’ and agent’s voice.
Combining Emotion and Sentiment analysis provides insights from multiple modalities with different levels of granularity which can be utilized to make quicker decisions to improve the overall customer experience.