Introducing artificial intelligence technology (AI) in customer support has completely changed the game for the customer support industry. With the help of AI, brands can learn more about their customers, and as a result, provide better customer service experiences.
It is also a cost-effective method as well as an efficient one for improving customer satisfaction. It is incredibly important that your brand provides a personalized experience to your customers, and AI can help you with just that!
The AI technology market is predicted to grow to over $1.5 trillion by 2030! This means that AI in customer service will become pretty common since more and more brands will adopt this technique to stay on top of the game.
Examples of AI in Customer Service
Now let’s look at the examples of AI in customer service for you to understand better:
Many major companies around the world are actively engaged in the race to implement virtual assistants and chatbots that can answer customer queries. One of the biggest telecom companies in the US i.e. Xfinity has also introduced Xfinity Assistant, a virtual chatbot, that acts as Xfinity customer service for the brand.
Customers can send a message to Xfinity Assistant and receive automated responses that help to narrow down their queries and find a solution. Chatbots are promising technology of the 21st century, that has projected billions of dollars for many major enterprises.
When customers call regarding any issue with their services, brands need to make sure that they’re the right persons. That is their way of keeping customers’ information secure and making sure it doesn’t fall into the wrong hands.
They usually ask questions like, “what is your mother’s maiden name?” or “Who is the name of your childhood friend?”. However, these are very easy for an impersonator to guess. This is where AI comes to save the day for your customers.
Instead of wasting their time authenticating themselves in a phone call, your customers can use biometric authentication for a seamless customer support experience. An example includes a voice-based biometric that allows for assessing your customer’s speech as authentication without your support agent making any effort on their part.
3. Facial Recognition
This is another form of authentication that your brand can apply to make sure impersonators are not accessing your customer’s private information.
One way for an AI-based algorithm to confirm your customer’s identity is by analyzing the distance between the eyes, the shape of their jaw, the width of their nose, etc. They can use this data to find the right match.
You might think lighting conditions, angles, or backgrounds may also impact the facial recognition system. But surprisingly, we have come so far in the advancement of AI technology, that this is not an issue. AI can easily recognize your customer’s facial expressions, no matter what kind of lighting or background there is.
4. Intent Prediction
Amazingly, AI technology can also figure out the science behind your customer’s next-step procedures. Their signals, such as clicks, purchases, views, the duration they spend on a webpage, etc. are all the tells that AI can use to make predictions about your customers and their needs.
This consumer data is used to deliver the next step for relevant customer support. For instance, if a customer is going through new vacuum cleaners on Amazon, the AI will pull up other vacuum cleaners from different brands that might match his needs. Also, customer support agents can use this consumer data to up their game by providing customers with any assistance they need.
Challenges in Intent Prediction
Intent prediction is a complex task that heavily relies on artificial intelligence technology (AI). However, like any other machine learning task, AI-based intent prediction faces several challenges. One of the most crucial challenges is data collection, which requires the AI models to be trained with high-quality, diverse, and relevant data to make accurate predictions. Overfitting and underfitting also pose significant concerns, as the AI models can either be too complex or too simple to capture the complexity of real-world queries. Additionally, handling out-of-scope queries and multi-lingual intent prediction can be challenging, as the AI models need to understand and differentiate between a wide range of intents and languages. Lastly, model interpretability is a key challenge, as the lack of transparency in some AI models can make it difficult to understand how decisions are being made.
5. Automated Responses or Tickets
Providing manual tickets to your customers is one of the most mundane tasks that your customer support agents perform. These tickets are routed on the basis of tiers, urgency, product, team priority, etc. This is done over and over and over again by your customer service agents. But with AI, tickets get automated at a much faster rate, and also devoid of any human error.
If ticketing or chats are automated through AI, then sending email responses through AI is also possible! Your customer support agents can generate automated email responses, full of correct and appropriate information, with the help of AI.
Your customer support agents can get access to a widget on their computer screens to help them find the correct information for customer queries, based on company data and previous interaction with the customer.
Hence, such activities, when transferred to AI, could allow your customer support agents to focus on much more complex customer queries.
Such a tremendous impact of AI in customer service being discussed in this article goes to show that this is a positive change for your brand, customers, and support agents. It has brought about a competitive environment for the brand as well as encouraged them to deploy AI technology for better customer experience.