August 17, 2023

How Companies are Using AI Tools for Customer Service

The increasing popularity of artificial intelligence (AI) is setting the stage for organizations to improve their efficiency, automate workflows, and deliver better customer experiences. A number of businesses are even now using AI tools for customer service — not just as an optional investment, but as a necessity for staying competitive in the market.

For companies looking for new ways to manage and improve the customer experience, AI has opened up a world of possibilities, particularly in ways brands engage with and serve customers. Let’s take a look at some examples of AI tools for customer service, and how technology today is benefitting both businesses and consumers. 

Provide 24/7 Support Via Chatbots and Virtual Assistants

The use of chatbots and virtual assistants is perhaps the most popular example of using AI tools for customer service. This type of AI excels at providing real-time responses to customer questions, inquiries, and comments, helping eliminate the frustration of waiting in queues for human customer service agents. 

Chatbots and virtual assistants often utilize one form or another of natural language processing (NLP) technology in order to analyze and understand customer queries. One significant advantage of using AI is the ability to provide 24/7, round-the-clock support, unlike traditional customer service channels with limited operating hours and slow response times.

Apart from instant responses and support, AI tools for customer service can also analyze vast amounts of customer data to provide highly personalized interactions. AI-powered customer service systems can remember past customer interactions, purchase history, and custom settings and preferences — enabling organizations to offer tailored solutions for customers.

Use AI to Understand the Customer Perspective

NLP is the ability of a computer program to understand human speech as it is spoken. This allows some AI tools for customer service to interpret customer feedback (from reviews or surveys) the way the human brain interprets language.

One of today’s most popular approaches to NLP is based on machine learning, a method of data analysis that allows AI to find patterns and unlock insights as it is exposed to new data. This approach is fast, consistent, and programmable, which means that organizations can now leverage customer experience analytics to process massive amounts of data from reviews, survey responses, and customer feedback. That way, you understand — at a glance and at scale — precisely what your customers are saying. 

  • Accelerate improvements and breakthroughs. Quickly identify opportunities and issues with the customer experience — and make better business decisions backed by data. 
  • Build a brand that resonates with your customers. Discover topics, issues, and keywords that customers mention organically, then tailor your own messaging so that the voice of your brand speaks the language of your customers. 
  • Anticipate customer needs. Look through the lens of your customers. AI can equip you with the intelligence and foresight essential to delivering 5-star customer experiences — today and in the future.

Use AI to Respond to Reviews

Customer reviews on sites like Google, Facebook, Yelp, and Tripadvisor give people a way to share their experiences not only with businesses but with fellow consumers, too. Consumers rely on these reviews in order to discover great businesses, products, services, and brands. 

This makes responding to reviews extremely important, especially for companies looking to convert prospects and casual visitors into actual customers. This is where AI comes in: now, artificial intelligence is being used to help organizations manage and respond to online reviews

Specifically, AI review response tools can suggest replies based on the content of a review, resulting in responses that sound more personalized than one would get from a standard template of a ready-to-paste collection of positive review response examples.

For multi-location brands that need to respond to a high volume of reviews, leveraging AI can help you save time and move through these reviews faster. No matter your business size or industry, having an AI tool for customer service / review response within your online reputation management software solution can prove extremely helpful if you are prone to writer’s block and would welcome suggestions on how to respond professionally.

Read more about ReviewTrackers’ implementation of AI Review Response

Deliver Multilingual Support with AI

For global brands, AI tools for customer service can be a game-changer when it comes to providing multilingual support. 

Thanks to the growth and advancement of natural language processing technology, businesses can now use AI to offer customer service in multiple languages, helping break down barriers and expand global reach. 

With multilingual support, customers can more easily find information in the language they are most comfortable with, helping reduce the need for live service and support interactions. 

Foster Human-AI Collaboration

No matter how advanced artificial intelligence grows to become, the human component in customer service remains essential, particularly when it comes to handling complex and emotionally charged customer interactions. 

By combining the empathy of human understanding with the efficiency of AI, you can get the best of both worlds and foster greater human-AI collaboration. AI tools for customer service can also assist teams by crystallizing data into actionable insights and suggesting solutions.  

For example, AI can help with automatically sorting and routing incoming emails and tickets. This means human agents can set the right priorities and respond to urgent customer service requests based on information provided by AI. 

These examples of AI tools for customer service demonstrate how organizations can scale their support and service operations. With AI solutions, businesses can drive efficiency and stay ahead of customer needs and preferences, all while reducing operational costs associated with hiring and training.

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