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11 AI Use Cases in Customer Service: In-depth Guide in 2024

11 AI Use Cases in Customer Service: In-depth Guide in 202411 AI Use Cases in Customer Service: In-depth Guide in 2024

AI applications have become widespread in customer services. Research shows that 80% of customer service companies will use generative AI as of 2025 to improve their productivity and customer experience. Besides, 30% of customer service representatives are expected to use AI to automate their work by 2026.

This article provides AI use cases in customer service around typical customer service activities as listed below:

Identify customer issues with social listening and ticketing solutions

Identifying issues wherever they rise is the first step to resolving them. Social listening (also referred to as social media monitoring) and ticketing vendors help you to leverage Natural Language Processing (NLP) and machine vision to identify customers to contact and respond to them automatically or assign them to relevant agents increasing customer satisfaction. Social listening can:

  • increase average spending per customer, according to a study by Bain & Co., customers who engage with companies over social media spend 20% to 40% more money with those companies than other customers.
  • reduce the cost per contact, McKinsey & Co. estimates that shifting to social media customer service can reduce cost per contact by as much as 83%.

Authenticate customers with biometrics

Voice biometric solutions translate words into a voice print that is unique to a person which can help securely authenticate customers. This enables customers authentication without passwords leveraging biometry to improve customer satisfaction and reduce issues related to forgotten passwords.

Assign agents to customers

Ensure that the agent you assign to a customer has the expertise and style which matches the needs of that customer.

Call classification

Call classification systems leverage Natural Language Processing to understand what customer is trying to achieve enabling your agents to focus on higher value added activities and enable you to better match agents and customers

Intelligent call routing

Intelligent call routing systems route calls to most capable agent available. Intelligent routing systems incorporate data from all customer interactions optimizing customer satisfaction

Automate agent activity

AI can help save agents’ time while increasing customer satisfaction.

Call intent discovery

Leverage Natural Language Processing and machine learning to estimate and manage customer’s intent (e.g. churn). Intent prediction enables customer service to give customers the assistance they need in the way they want which helps improve customer satisfaction and business metrics.

To further improve customer experience, emotion AI solutions can estimate customer emotions by analyzing visual, textual, and auditory customer signals. This allows customer service reps to be more conscious of customer emotions and for example pay special attention to angry customers with the intent to churn.

Customer service response suggestions

Bots will listen in on agents’ calls suggesting best practice answers to improve customer satisfaction and standardize customer experience.

Source: Digital Genius

Customer service chatbots

According to studies, 20% of businesses that use chatbots deployed a bot to support the customer service department and it is the third-most business chatbot application area following  IT department (53%) and administrative department (23%). The hype around customer service chatbots is not a surprise, considering 75% of customers believe that it takes too long to reach a human agent.

Providing an AI-powered 24/7 customer service chat can help handle most queries and transfer customers to live agents when needed. This helps reduce customer service costs and increase customer satisfaction.

An example is Dom the pizza bot of Dominos:

Chatbot testing

Not paying attention to your users’ experience with chatbots can have screenshot worthy results like this one. Chatbot testing and analytics solutions enable you to continuously improve your bot.

twitter chatbot fails

For more on chatbot testing, check out our related articles:

And don’t forget to check out our data-driven list of chatbot vendors and voice bot platforms.

Customer service analytics

Analyze all customer service activities so you know how to save costs and improve service quality.

Chatbot analytics

As GE’s Peter Drucker is quoted saying “If You Can’t Measure It, You Can’t Improve It”. It is certainly true for chatbots that produce rich conversational data.

We have a detailed guide covering top chatbot metrics if you want to know more.

If you want to learn more about the applications of sentiment analysis in chatbots, read our comprehensive article.

Call analytics

Advanced analytics on call data to uncover insights to improve customer satisfaction and increase efficiency.

Survey&review analytics

Leverage Natural Language Processing to analyze text fields in surveys and reviews to uncover insights to improve customer satisfaction and increase efficiency.

Now that you know about AI applications in customer service, you could examine AI applications marketing, sales, , IT, data or analytics.

You can also our list of AI tools and services:

And if you have a business problem that can be solved with AI:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Neva Perin
May 31, 2019 at 10:51

I am not sure if the airline contact center implements AI. But when it comes to measuring the agent’s performance in metrics form, I believe tools like CSAT.AI or ScorebuddyQA does that. Do check it out. Hope this helps!

Dakota Dais
Apr 24, 2019 at 12:13

A worth reading article! But could you just explain me in detail how the AI is implemented in the airline contact center and does it reflect the agent’s real-time performance in metrics form?

Apr 27, 2019 at 06:11

Afiniti is using AI in agent to customer pairing which boosts conversion rates in sales calls

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