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20 Examples of Generative AI Customer Service

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The use of generative AI has sparked a great deal of intrigue and interest in recent years. This innovative technology allows businesses to quickly create text, images, and music, making it a powerful tool for enhancing productivity and efficiency.

As employees find success, those companies now wonder if they can use generative AI in customer service too. From resolving issues and answering questions to providing product recommendations and personalized marketing campaigns, generative AI has the potential to revolutionize the way businesses interact with their customers.

This blog will define this new technology and offer both generic and real-world generative AI examples showing how to use generative AI for customer support.

What is Gen AI?

Generative artificial intelligence, or Gen AI, is a type of artificial intelligence that can generate new data, such as text, images, or music. It does this by learning from patterns in existing data and using that knowledge to create new data that is similar in some way.

Gen AI is different from other types of AI, such as predictive models, which are designed to make predictions based on existing data. Gen AI is more creative and can be used to generate new ideas and concepts.

Gen AI has a wide range of potential applications, including:

  • Customer service: Gen AI can be used to create chatbots that can answer customer questions, resolve issues, and provide support 24/7.
  • Marketing: Gen AI can be used to create personalized marketing campaigns that are tailored to the interests of individual customers.
  • Product development: Gen AI can be used to generate new product ideas and designs.
  • Education: Gen AI can be used to create personalized learning experiences for students.
  • Art and design: Gen AI can be used to generate new works of art and design.

Gen AI is a rapidly developing field and its potential applications are only just beginning to be explored. As Gen AI technology continues to improve, we can expect to see even more innovative and creative ways to use it to improve our lives.

Gen AI is a type of artificial intelligence that can generate new data, such as text, images, or music. It does this by learning from patterns in existing data and using that knowledge to create new data that is similar in some way.

Generative AI vs. Conversational AI

Generative AI and conversational AI are two different types of artificial intelligence that have different purposes and capabilities.

Generative AI is creative, designed to create new data, such as text, images, or music. It does this by learning from patterns in existing data and using that knowledge to create new data that is similar in some way. For example, generative AI can be used to create chatbots that can answer customer questions, generate creative text formats, or even write different kinds of creative content.

Conversational AI is designed to engage in conversation with humans. It does this by understanding human language and responding in a way that is both natural and informative. For example, conversational AI can be used to provide customer service, answer questions, or even just chat with people for fun.

Here is a table that summarizes the key differences between generative AI and conversational AI:

Feature

Generative AI

Conversational AI

Purpose

To create new data

To engage in conversation

Data input

Existing data

Human language

Data output

New data, such as text, images, or music

Natural language responses

Applications

Chatbots, self-service knowledge bases, customer sentiment analysis, product recommendations, etc.

Customer service, virtual agents, question answering, knowledge sharing, entertainment, etc.

 

As you can see, generative AI and conversational AI are two different but complementary types of AI. Generative AI can be used to create new content, while conversational AI can be used to engage in conversation with humans. By combining these two technologies, we can create even more powerful and versatile AI applications.

How to Use Generative AI for Customer Service

When you think of customer service, you may picture a customer talking on the phone to a contact center agent. But comprehensive customer service includes aspects that customers never see, including data protection and pricing. Here are 15 examples of how companies use generative AI in customer service today:

  1. Chatbots. Generative AI chatbots can answer customer questions, resolve issues, and provide support 24/7. They can also be used to collect customer feedback and identify trends.
  2. Virtual assistants. Some virtual assistants, found in smartphones and smart speakers, have begun using generative AI to generate new responses to questions. For customer service, this should be enhanced with conversational AI to create a virtual agent.
  3. Self-service knowledge bases. Generative AI can be used to create self-service knowledge bases that are filled with up-to-date information on products, services, and policies. This can help customers find answers to their questions quickly and easily, without having to wait for a human agent.
  4. Customer sentiment analysis. Generative AI can analyze customer feedback and identify trends in sentiment. This information can be used to improve customer satisfaction and loyalty.
  5. Customer churn prediction. By analyzing how customers interact with generative AI, companies can predict which customers are at risk of churning. This information can be used to take proactive steps to retain these customers.
  6. Product recommendations. Using its creative capacities, generative AI can recommend products and services to customers based on their past purchases, interests, and preferences. This can help customers find the products and services they are looking for more easily.
  7. Pricing optimization. Companies can also interact with their own generative AI by asking it to research the optimial pricing for products and services. This can help businesses maximize profits while still providing value to customers.
  8. Personalized marketing campaigns. It can create personalized marketing campaigns that are tailored to the interests of individual customers. This can help businesses increase brand awareness, drive sales, and improve customer loyalty.
  9. Fraud detection. It may be one tool companies use to detect fraudulent transactions. This can help businesses protect themselves from financial losses, though they should have other tools and policies dedicated exclusively to this purpose.
  10. Risk assessment. Generative AI can be used to assess the risk of customer interactions. This information can be used to prioritize customer support requests and identify customers who may need more assistance. AI makes mistakes, so you should have other risk assessment measures in place.
  11. Compliance monitoring. Generative AI can be another tool to monitor customer interactions for compliance violations. This can help businesses avoid penalties and legal problems, but again, do not rely solely on gen AI for this purpose.
  12. Training and development. Internally, generative AI is a great tool for creating training materials for customer service agents. This can help agents learn the skills they need to provide excellent customer service.
  13. Performance management. You can use it to track the performance of customer service agents. This information can be used to identify areas where agents need improvement and provide them with feedback.
  14. Dashboards and analytics. This technology can also analyze data, so generative AI can be tasked with creating visual dashboards and analytics that provide insights into customer service performance. This information can be used to make informed decisions about customer service strategy and operations.
  15. Research and development. Generative AI can also be used to research and develop new customer service solutions. This can help businesses stay ahead of the competition and provide customers with the best possible experience.

These are just a few examples of how generative AI is being used in customer service today. As generative AI technology continues to develop, we can expect to see even more innovative and creative ways to use it to improve customer service.

Infographic: 15 Ways to Use Generative AI for Customer Service

Examples of Generative AI Customer Support

This is not a theoretical concept. Many companies have fully adopted generative AI into their business practices. Here are five real-life examples of companies that already use generative AI for customer support today:

  1. Octopus Energy. This UK-based energy supplier uses generative AI to answer customer questions and resolve issues. The AI chatbot is able to handle 44% of customer inquiries, freeing up human agents to focus on more complex tasks.
  2. Chegg. As an educational technology company, Chegg uses generative AI to provide personalized tutoring and homework help to students. The AI tutor can answer questions, provide feedback, and even generate new content.
  3. Freshworks. This software company uses generative AI to create training materials for its customer service agents. The AI can generate personalized learning paths for each agent, based on their individual skills and knowledge gaps.
  4. Samsung. To stay ahead of the competition, Samsung uses generative AI to create personalized product recommendations for customers. The AI can take into account a customer's past purchases, interests, and preferences to recommend products that they are likely to be interested in.
  5. Spotify. A giant among music streaming services, Spotify uses generative AI to create personalized playlists for customers. The AI can take into account a customer's listening history, preferences, and mood to create playlists that they are likely to enjoy.

These are just a few examples of how generative AI is being used in customer service today. As generative AI technology continues to develop, we can expect to see even more innovative and creative ways to use it to improve customer service.

For the best results, generative AI and conversational AI must be used together. For something as important as customer service, don't let the technology dictate what it says to customers. Instead, take the time to craft your conversational design.

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