Like it or not, emotions drive the decisions you make today, and they affect behaviors as well. Happy people whistle and show excitement. Angry people get irritated easily and even crash cars. And now with the passage of time, emotion detection & analytics is getting common in the business environment especially call center landscape where agents have to deal with people with different moods and emotions; so, they can handle the way they want to be treated.
For example, a new customer shows a wide range of emotions while following different actions on the call with an agent. They show excitement when choosing new products, frustration while installing any complicated software, annoyance when transferred between different agents and departments, relief when troubleshooting some long-term issues, and satisfaction when everything goes according to their expectations.
That’s the reason, monitoring customer’s sentiment through emotion recognition is getting important day by day, so the business can improve their customer service experiences.
Emotion detection helps measure verbal and non-verbal communication so that customer service agents can monitor and understand the moods, emotions, and behaviors or their customers. The basic idea is to simply evaluate the customer’s experience after using some product or service so that businesses can monitor their weak points that are causing trouble and negative reactions. This is also called emotion analytics, there are a lot of benefits to implementing this technology into the customer support system.
How human detection works?
Emotion detection is considered as the most effective way to monitor and measure consumer feedback; it simply artificial intelligence solutions so they can easily detect and analyze all the details and data, without asking customers to share any additional thing about their issue. In simple words, it is not easy to fake your feelings. Here you can consider some common examples of analyzing emotional data, which includes:
Text analysis mainly uses different algorithms to analyze the text carefully and then determine whether the perspective of a writer is positive or negative.
Emotion detection has now become the most important tool that makes sense of different opinions expressed on some review sites, blogs, and social media.
This is the process of carefully analyzing different voice recordings or assessing live calls while using an emotion recognition software that helps find some meaningful data, like anxiety in customer’s voice and opinion. For instance, efficient speakers can easily monitor the mood and then play music accordingly. This technology is also much useful in fraud prevention and detection that simply analyzes some unique characteristics which can detect dishonesty or the mood to hide information from the agent.
Facial recognition technology can easily analyze user’s facial expressions through some video or photo, wide smiles and raised eyebrows. When you set different essential parameters around facial reactions, educators can easily spot some struggling students in the classroom, similarly, security forces can easily detect malicious intentions during any public event or social gathering.
Business applications in emotion detection:
Some top-level B2C organizations are using these business applications to optimize better customer engagement:
Emotion analytics is helpful in using the voice and mood of the customer, and also route the call to the right agent at the right time. For example, an annoyed customer would be routed towards the technical support department and the happy customer might be routed to the sales & support department so they can pitch their product or service.
Tracking reactions with time:
Data delivered through emotion detection software can help to provide detailed information about every aspect of the customer interaction. For instance, call centers can simply tweak the procedures when emotion analytics detect the friendly introduction which is usually observed as much annoying & intrusive.
Role of emotional detection in call centers:
When you put real-time insights into customer’s experience, it simply helps agents can help agents to engage with them in some personalized way and also deliver highly empathetic service which is the notable attribute in today’s customer-centric landscape. For instance, agents guiding customers about choosing right call center solutions can see some happy or confusing expressions on customer’s face, that enable them to repeat the steps and provide more information after their response.
However, if we implement these technologies in our call center world, it would have a much valuable impact that would usually connect customers and agents on emotional levels and the goals can be accomplished in a much shorter time.