Let’s ask professionals in the app development industry if they ever recommended any business, brand, organization or a product to anyone they know, like their friends, family, colleagues or business connections or anyone else?
More than that, let’s also ask them if they have experienced something personally that was exceptional in their customer experience journey? Why? Because this is where the role of the customer service experience comes in.
There are helpful tools present. Among them are Net Promoter Scores (NPS) which was made by Bain & Company back in 2003. This measured scores of customer loyalty on the scale of 0 to 10. However, NPS was unable to derive actionable insights related to customer services and the likes.
Why? Because it is an exclusive domain dominated by artificial intelligence (AI). Multiple use cases of AI in amplifying customer services and redefining customer service operations exist. Today’s age has customer service functions powered by AI, which also powers customer service analytics.
Application leaders in the mobile app development industry along with other associated professionals already have a lot on their minds, hands and plates. It is now time to see how AI’s potential can be leveraged in the arena of customer service experience.
Biometric Solutions
A lot of professionals and people alike might have come across biometric solutions because this is something that AI derives. For authentication purposes, biometric solutions powered by AI have helped industries towards customer services on an improved level.
Two types of Biometric Solutions are typically available: Physical Biometric Solutions and Behavioral Biometric Solutions. The former analyzes human body parts like a person’s face, fingerprints, iris, retinal scans and the like whereas the latter analyzes human behavior patterns such as voice tone, emotions, gait and the like.
In this manner, solutions powered by artificial intelligence work as a secure way for authenticating identifications and access control.
Facial and voice recognition
After seeing how biometric solutions help authenticate the identification process, AI has also been seen contributing greatly to a machine’s biometric capabilities with add-ons like voice recognition. It empowers facial recognition capabilities through basic comparisons of facial features with respective images and videos lying in a database.
For example, an algorithm powered by AI itself analyzes the jaw’s shape, the width between eyes and then finds a relevant match using specific data. How does AI recognize a person’s voice? The AI-powered voice recognition tool uses data like voice pitch, tone and encodes (after digitizing words captured from the person’s voice).
This leads to forming a unique voice print of an individual desiring to use this feature. In this way, unique identification and authentication of the person are done through their unique voiceprint.
Prediction of user intent
On the basis of a user’s web activities, AI-powered predictive analytics are instrumental in helping predict user intent. What the customer will do in the future can be foreseen through indicators and signals like clicks, views, purchases and the different behaviors of a customer. They are analyzed by AI assisted and enabled apps, predicting user intent thereafter.
These predictive solutions are powered by AI and have leveraged the technology combining relevant data with real time information and insights. This thus helps determine customer intent with relevant ease.
Chatbots (aka virtual assistants)
Chatbots often work as top-notch virtual assistants (and are among the brightest of app ideas too). They utilize AI-ML capabilities that help serve customer queries through a live chat messenger. These kinds of AI bots store a limitless amount of data in huge volumes and have real-time access to information whilst predicting customer behavior as well.
Both humans and AI enabled chatbots collaborate for optimizing interactions occurring with various customers. Hence, conversational chatbots supported by AI and Machine Learning prove to be a great addition to businesses requiring top-class help in the domain of customer service.
Emotion based analytics
Emotion analytics, especially those based on Artificial Intelligence help classify customer moods based on which they are routed to the right material. For instance, if a customer is happy that the product is what they were looking for, then they can be hence guided by analytics to the sales team for further understanding and deal sealing.
Similarly, unhappy customers are guided to the customer retention team for appropriate guidance and help. Hence AI based emotional analytics are helpful in analyzing customer moods and their communication styles (verbal/non-verbal) so concerned teams may contact them for the needed matters.
