AI in Customer Service: Everything You Need to Know in 2024

What is AI in Customer Service?

Let’s start with the basics—

AI in customer service is the practice of using intelligent technologies in support operations to create more meaningful experiences and enable strategic decision-making across service teams. 

There are three key elements of AI in customer service:

• Natural language processing (NLP)
• Machine learning (ML)
• Generative AI

With these algorithms, your support team can automate the workflow, provide quick responses that feel more human, and improve their productivity—ultimately providing good support at scale.

9 Ways AI is Used in Customer Service

1. Chatbots

Chatbots are software applications designed to automatically resolve any routine customer queries— without human intervention.

Currently, chatbots are powered by sophisticated artificial intelligence that can interpret human languages and generate smart and cohesive responses. 

They can be deployed across a wide variety of channels, including websites, mobile apps, and social media channels like— WhatsApp, Facebook Messenger, Instagram, Telegram, etc.

Conversational AI chatbots can answer 80% of customer queries without human intervention. This means businesses can serve customers 24/7 while agents can focus on complex tasks.

For any company with a large and multinational customer base, AI chatbots are a necessity for customer satisfaction.

2. Sentiment Analysis

AI sentiment analysis is a tool that uses intelligent algorithms such as natural language processing (NLP) to identify customers' opinions and emotions from a piece of text. These sentiments can range from positive, negative, neutral, or more intense emotions like joy, anger, sadness, fear, etc.

Traditional sentiment analysis requires manually analysing an extensive amount of data, which is both impractical and time-consuming.

With modern sentiment tracking tools, you can detect customer emotion from their survey responses, chats, and social media interactions.

Plus, monitoring your customers' sentiments with AI allows you to get critical insights from every stage of their buying journey and customise your strategies to better meet their needs.

3. Skill-based Routing

Customer service platforms can use AI to automatically direct incoming queries to the best-suited agent for resolving the issue. This logic, called skill-based routing, considers factors like agent status, capacity, skill set, and customer intent to find a qualified agent. 

Implemented well, AI-powered skill-based routing can help your agents address urgent issues quickly, offer more personalised support, and comply with service SLAs. 

For example, a Japanese-speaking customer is looking to buy a Nintendo Switch from your business. To satisfy their needs, your brand must have knowledge about the latest console and be able to explain information in Japanese. With skill-based routing, the AI automatically finds agents who speak a proficient level of Japanese and can hold engaging conversations about the product.

Learn more about the best customer service platforms.

4. Content Generation  

Firstly, generative AI is still a relatively new field in AI and consists of a wide variety of technologies. The ones we are going to talk about are the tools specifically used by support teams to create more engaging experiences.

AI-powered writing assistant tools can instantly analyse the inputted text and assist you in editing, such as extending, shortening, rephrasing, correcting grammar, or translating the sentence to make the response look professional.

Another obvious use of generative AI is that it can effectively create conversation summaries. The system can automatically analyse threads of customer interactions to find the key information in the message.

A prime example of a generative AI tool is Mevrik's automatic summary feature. It summarises the topics discussed during an interaction with a single click, thereby saving your valuable time and helping you jot down any crucial information for more proactive support.

5. Sales Forecasting

Traditionally, sales forecasting is the practice of analysing current and historical customer data to predict the future market trends and sales income.

Integrating an AI-based sales forecasting tool like Mevrik Sales Forecasting can be extremely beneficial as it can establish a unified forecasting system for your business instead of relying on fragmented data.

You can automatically collect large amounts of unstructured text data from various sources, such as previous conversations, browsing histories, and social media interactions.

This data can be later analysed with greater accuracy through in-built sales forecasting models such as time series forecasting, regression analysis, market-based forecasting, and qualitative forecasting. By doing so, you draw the right conclusions, avoid expensive miscalculations, and maximise growth.

6. Omni Channel Strategy

An omni-channel strategy is the practice of providing a fast, consistent, and personalised experience across multiple communication channels such as —phone calls, email, messengers, social media, and in-app chats.

AI can bring its advantages to your omni-channel strategy by guiding customers to the right support.

For example, AI can analyse customer inquiries from various channels and route them to the most appropriate agent, regardless of where the initial contact is made. This ensures that whatever communication method your customers use to connect with your brand, they will receive optimal and instant support.

7. Self-service Option

According to a Harvard Business Review, a whopping 81% of customers want to take care of the matters themselves before reaching out to a service representative.

AI-powered self-service tools such as knowledge bases can store large volumes of support articles that can be easily accessed by customers without human intervention. These articles can cover how-tos, FAQs, installation guides, troubleshooting, etc.

Additionally, articles can be created for internal team use— giving employees a central place to quickly access any business information.

For example, the Knowledge Base feature offered by Mevrik can store both private and public articles. You can create these articles both manually and with the help of generative AI tools—which can create professional-looking articles in seconds. 

Additionally, Mevrik's system can automatically detect the context of the interaction and directly recommend helpful articles from the knowledge base in the chat.

8. Quality Assurance

AI-driven quality assurance involves using AI tools to automatically evaluate agent performance against quality benchmarks you have set for your business.

This can include assessing every customer interaction using a variety of criteria, such as support call quality, response time, compliance, and so on.

Observe AI, a leading AI-based QA software, can automatically pinpoint which criteria your agent is falling short of. You can later use the performance feedback to optimise your coaching workflows and proactively train your agents.

Learn more about the Customer Service Quality Assurance

9. Multilingual Support

If you are a large business serving customers on a global scale, it is a good practice to take care of your customer base's cultural and linguistic diversity.

AI can help you embed this practice in your customer service strategy by automatically translating conversations in multiple languages. 

When a customer sends a message, the conversational AI automatically detects the customer's language and converts their messages into the agent's preferred language. Responses are then translated back to customers.

This helps customers to authentically communicate their needs and agents to provide support that maximises satisfaction.

The Future of AI in Customer Service

Here are some key predictions on what the future might look like for AI in the customer service industry:

More Conversational AI Chatbots

Earlier, we mentioned how a conversational AI chatbot can resolve 80% of customer queries and boost agent productivity. Building on this efficiency, industry analysts predict that the use of conversational AI in customer service will increase from 1.6% to one in every ten encounters by 2026. And the numbers make sense.

A traditional rule-based chatbot can only answer common customer service issues and route complex ones to your human agent. They are less adaptable when it comes to dynamic customer behaviour or unique language patterns.

A conversational AI chatbot, on the other hand, can resolve complex customer queries autonomously. This is due to the fact that conversational AI primarily uses two technologies:

1. Machine Learning (ML) : Keep track of customer interactions and learn as they go.
2. Natural Language Processing (NLP): Helps computers understand human language and converse in a way that is natural to humans.

For example, Mevrik offers next-generation conversational AI chatbots that are trained on high-quality customer experience data. It can easily generate human-like conversations, offer individualised responses, and automate simple tasks. Plus, it supports 130+ languages in text and 70+ in voice, so you can support your customers around the globe.

Finding the Balance Between Personalisation and Privacy 

It's no surprise that customers love brands that deliver personalised products and services. 

However, the use of more and more personal data for marketing strategies, have grown concerns among customers regarding data protection. Customers believe that if there is a flaw in the system or a data breach, millions of people will have access to their personal information.

While personalisation is pivotal for any brand engagement, it is also important to put your customers at ease. Thus, a balance between personalisation and privacy is critical for the success of your business.

To secure a balance between personalisation and privacy, here are some key strategies used by businesses:

• Transparency: Communicate the data policy clearly with customers. Inform them about how you are going to use their personal data, why you are using it, and how you will protect it.
• Limited data collection: Collect only the information needed to deliver a personalised experience.
• Data control options: Give customers the freedom to control their data. They can change cookie settings, opt out of data sharing, or have their data deleted.

More Gen-AI Content for Training Employees

Generative AI has already made a substantial impact since its mainstream launch back in 2022. Going forward, companies can use it to train their support team.

AI-powered CX systems can analyse interactions from multiple channels, such as calls, chats, emails, and social media mentions, to identify any routine question.They can then use generative AI tools to instantly create helpful articles that can be independently accessed by customers.

Final Thoughts

While AI is introducing lots of good stuff when it comes to providing faster, personalised, and cost-effective customer support, it's important to remember that a well-rounded support system needs both automation and empathy.

Your customer might be looking for human interactions, and failing to deliver effective support can weaken trust and create frustration. Thus, make sure your brand is available for your customers, on their terms, and whenever they need you.

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That being said, if you’re looking for an AI-powered CX platform that better addresses customer needs, optimises your workflow, and improves your overall bottom line, sign up for Mevrik today.

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