Have you ever recommended a brand, a company, or a product to your peers, friends, or family members? Have you personally experienced something good in your CX journey? That is where the role of customer service arrives.
There are tools like Net Promoter Scores(NPS), invented by Bain & Company in 2003, that measure scores of customer loyalty on a scale of 0 to 10. However, NPS cannot derive actionable insights related to customer services and the likes.
This is an exclusive domain dominated by the one and only AI (Artificial Intelligence). There exist multiple use cases of AI enhancing customer services and redefining customer service operations.
In this age of AI-powered customer service analytics, application leaders and businesses have enough on their plates to leverage the potential of AI in customer services. Let’s see how!
1. Biometric Solutions
You have come across this powerful use-case that AI emanates. For authentication purposes, AI-enabled biometric solutions are helping industries with customer services on a great level. There are typically two types of Biometric Solutions – Physical Biometric Solutions and Behavioural Biometric Solutions.
The former analysis of human body parts like a person’s face, fingerprints, and iris, whereas, the latter analyzes behavioral patterns like voice tone, gait, emotions, etc. This way, AI-powered biometric solutions are acting as a secure way to authenticate identifications and access control.
2. Voice & Face Recognition
Well, just now you saw how biometric solutions help authenticate the identification process. AI is continuously contributing to machines’ biometric capabilities with add-ons like voice recognition.
Also, it empowers face recognition capabilities by basic comparisons of facial features with respective images and videos lying in a database. For instance, an AI-powered algorithm analyzes the shape of the jaw, and the width between the eyes and then finds a relevant match using specific data. And how AI does do voice recognition?
The AI-powered voice recognition tool uses data like voice pitch, and tone and encodes them (after digitizing words captured from the voice). This leads to forming a unique voiceprint of the very individual. This way, unique identification and authentication of the speaker are done by his voiceprint.
3. Predicting Intent
Based on web activities, AI-powered predictive analytics help predict the intent of customers. What your customer will do in the future can be foreseen via their signals like clicks, views, purchases, and varied customer behavior, which are analyzed by the AI-enabled applications, predicting intent thereafter.
The AI-powered predictive solutions are leveraging the technology by combining relevant data that ultimately help in determining the intent of customers.
4. Chatbots or Virtual Assistants
Chatbots or Virtual Assistants utilize AI-ML capabilities to serve customer queries through a live chat messenger. These AI bots store endless, massive volumes of data and have real-time access to information while predicting customer behavior, too!
AI-enabled chatbots and humans collaborate for optimizing interactions occurring with varied customers! Thus, conversational AI-ML chatbots are proving to be a great boon to businesses requiring help in the customer service domain.
5. Emotion-Analytics
AI-based-Emotion Analytics help in classifying customers’ moods based on which they are routed to the right agents. Take, for instance, if a customer is happy, the analytics route him to the sales team where he can be pitched for a certain product/service.
Similarly, if a customer is angry, he will be directed to the customer retention team for appropriate guidance. Thus, AI-based Emotional Analytics helps to analyze a customer’s mood, and his verbal/non-verbal communication for adequate pitching by the concerned teams.
6. NLP Text-Analytics
NLP means Natural Language Processing. It is AI-ML-powered speech and text recognition analytics that help in analyzing customers’ moods based on texts as well as speech.NLP applications help to derive analytical insights from multiple sources such as reviews, blogs, social media posts, and varied forums.
Companies can leverage this immense AI-ML-NLP capability in the customer service department, gaining valuable insights at the micro-level.
Also Read:
AI Use Cases in the Retail Industry
With the rise of online retail activities, the retail industry is experiencing a wave of business upheaval.
7. Predictive Personalization
Predictive personalization, predictive maintenance is other significant use cases of AI in the customer service area.
AI-based predictive analytics help provide personalized recommendations to target customers and even can identify probable customers who seem to be at churn-risk, who can be proactively handled by the customer service department.
This leads to an improving retention rate. This technology can work wonders via predictive maintenance jobs. AI-powered predictive analytics, predictive maintenance solutions can predict technical issues, maintenance issues proactively, before time.
Precautions measures are taken thereby averting any serious issues to occur. For example, reducing breakdowns for delivery trucks, for elevators, reducing out-of-service time, optimizing network performances, etc.
8. Computer Vision for Object Recognition
Another AI-use case is an analysis of digital images, videos and automatically understanding of the contextual meaning. AI-powered Computer Vision technology processes and analyses objects, thereby allowing systems to recognize and classify them accurately. It reduces the workload of varied contact center agents. How?
By automatically routing specific customer inquiries to chatbots or the so-called self-service channels.
9. Agents Training & Productivity
As a business owner or decision-maker at contact centers, you can utilize AI-powered call-center training tools for imparting advanced training to agents.
You can manage to increase boost their efficiency, productivity by using varied tools, like Virtual Employee Assistants, (VEAs), call center training tools like gamification, etc. You can manage to reduce your agents’ onboarding time, ensuring their productivity from day one.
10. Optimization of CLV
CLV stands for Customer Lifetime Value. It is a valuable metric that is used to track the relationship value quotient of customers with your brand/company.
AI-powered CLV helps you to identify your loyal customers, as well as you can find the underlying value quotient your customer carries for you. Sometimes, retaining existing customers fetches you a higher ROI than getting the new ones.
AI for Valuable Business Insights
Adopting AI to spot trends, fetching insights based on massive customer data is something you cannot ignore in today’s world.
These business insights become the base for the decision-making process in your company. AI-powered holistic solutions, customer analytics, all help in automating business intelligence, facilitating a wide range of business applications in your organization.
AI leads towards huge cost-reduction, increased digital self-service effectiveness, as well as enhanced customer engagement.
How can AI-powered customer service analytics and other AI capabilities enhance your business potentials? Explore from our wide range of dedicated AI applications suitable for your business. Contact Us.
