Can you truly tell how a customer feels about your business? There are many different tactics that attempt to acquire this information, such as customer reviews and NPS (net promoter score), which may or may not provide an accurate assessment of overall satisfaction. Did you catch them on a bad day when they left an NPS rating? Did they write an odd review as someone was looking over their shoulder?
Businesses have tried for many years to understand how customers truly feel, but with so many customer interactions moving online, gauging authentic sentiment and satisfaction is becoming more difficult. As a result, some customer support and success teams are beginning to explore ground-breaking technology called emotion detection. Here are a few examples of innovative emotion recognition technology and how it can positively impact customer support teams:
Text analysis – Commonly referred to as sentiment analysis, this type of emotion detection relies on complex algorithms to label large blocks of text instantly with a specific emotion. For example, sophisticated customer support software can classify ticket text as “polite”, “frustrated”, or “sad” depending on what was written. In the hands of support teams, this information can be valuable for time-saving efforts by automatically routing “frustrated” tickets to senior agents.
Speech analysis – What if you could determine whether someone was lying based on the sound of their voice? This is exactly what speech technology can detect. For example, a common support question such as “Is there anything else I can help you with today?” often gets dishonest responses from people who don’t want to discuss other issues. With speech analysis technology, your team can flag customers who answer this question with "no" but are likely lying. Based on their dishonest response, an action plan can be created to have customer success follow up with them in the near future. Taking an educated extra step like this to solidify customer happiness and trust can help improve customer retention rates.
Facial analysis – More often, especially in the B2B industry, customers and support teams are connecting over visual support (including video chat) to solve problems. After all, what can take hours to describe over email can take just a few minutes to show over a video call. With facial analysis technology, businesses can better understand and create actionable information from the facial cues of customers during these calls. Simple movements such as an eyebrow raise or a modest smirk can be deciphered to show how a customer is truly feeling during the support experience. If a customer displays authentic facial features when praising your support agent, they may be a good customer for the greater business to leverage for promotional opportunities.
Realistically, we are still a few years away from some of these emotion detection solutions becoming more mainstream in the customer support industry. But, how will businesses tie together all these innovative technologies? Leading companies are already placing some of these analysis findings into overall customer distress data points located directly in their support software. By combining common support metrics (ticket close time, number of tickets, etc.) with these more modern analysis findings, companies can get a high-level overall “score” of how each individual customer feels about their business. The main benefit of this overall distress information is that no manual work needs to be done; all a business does is click on a company profile within the software to see their score immediately.
To summarize, the future of emotion detection in customer support is promising and the technology will become more widely adopted over the next few years. Whether it’s analyzing text, video, or spoken conversations, being able to create actionable data with minimal effort in real-time could change the way businesses approach customer interactions in the future. This information may be acquired by support teams, but it could also be invaluable to the greater company for both upselling and retention opportunities. How we communicate isn’t changing too much, but how information is being acquired from these conversations is evolving quickly.