How to use AI in customer service

AI innovations have the potential to revolutionize how businesses operate. That’s because these tools can automate processes that take a long time to complete manually, helping employees work more efficiently.

While AI has uses in many industries, it’s particularly beneficial for contact centers, thanks to its natural language processing (NLP) capabilities.

But how can you use AI to improve customer service? Let’s take a look at 10 ways, plus examples of AI in customer service.

What is AI in customer service?

Customer service AI refers to using AI capabilities, like NLP and machine learning, to improve the customer experience. With its many contact center use cases, AI can help:

• Customer service agents work more efficiently
• Customers reach resolutions faster
• Customer service supervisors/managers improve internal processes

Read more about the basics of AI for businesses for an overview of how this technology works and the most relevant terminology.

Benefits of using AI in customer service

Implementing AI in your contact center has many benefits for customer service, including:

• Increased efficiency: AI tools can help customer service agents work more efficiently by automating previously manual processes. For example, it can transcribe and summarize calls so agents don’t need to take notes.
• Faster service and better customer experiences: AI increases efficiency, as mentioned above, allowing agents to give customers faster resolutions. For example, AI-powered agent assistants can surface resources to help agents troubleshoot customers’ issues, which is faster than agents trying to find the right resource or answer manually. This faster service creates a better customer experience.
• Cost savings: AI-powered virtual assistants have more capabilities than traditional chatbots and interactive voice response (IVR) menus, resolving more customer inquiries without a live agent. This helps the live agents focus on complex tasks while keeping contact centers costs low by resolving more customer interactions without growing personnel.
• Better agent experiences: AI tools can help off-load manual tasks from live agents and equip them to serve customers more effectively, leading to less agent burnout. That’s because live agents can spend more time doing what they do best—supporting customers—and less time on repetitive tasks.

10 ways to use AI in customer service

Now that you know the benefits of AI, how do you integrate it into your contact center? Here are 10 ways your business can use AI for customer service:

1. Personalize chatbot interactions

Chatbots are traditionally rules-based, having predetermined answers to specific questions. For example, if the customer asks “What are your hours?” the chatbot answers “9 a.m. to 5 p.m.” This automated assistance can help customers find answers to basic questions but is lackluster in personalization.

And personalization is crucial for businesses: 66% of consumers admit they’ll quit a brand that doesn’t offer a personalized experience.

So how can you make chat-based support more personalized? Virtual agents powered by conversational AI can do more than traditional chatbots, like analyzing real-time customer data to personalize responses. This means your business can provide more tailored support without a live agent.

For example, say a customer initiates a chat to ask about processing a return. The virtual agent pulls up the customer’s last purchase from their profile and asks if this is the order they need help with. The customer replies that it is, and the virtual agent initiates the return.

This saves the customer time because they don’t have to search through their inbox to find the order number, creating a more seamless exchange. Plus, these conversational chatbots meet the customer’s personalization expectations, contributing to a positive customer experience.

2. Enhance voice assistance

Similar to chatbots, IVR menus are also rules-based. But AI can make IVR menus smarter than before, turning them into a dynamic tool that uses customer data to tailor the experience.

For example, say a customer tries to log into their bank account but can’t remember their password. After three failed login attempts, they get locked out of their account and have to call customer service.

The dynamic self-service menu recognizes the customer’s number and has the context of their browsing history. So when the customer calls customer service, the first menu option they hear is the password reset option.

This means the customer doesn’t have to sit through a long IVR menu to find the help they need and can reset their password quickly. This dynamic support creates a positive customer experience and can help improve customer retention.

3. Route customers efficiently

As we hinted at above, AI tools can analyze customer data to help route callers to the right agent or resource more efficiently.

For example, say a customer browses their bank’s website looking for information about international ATM withdrawal fees. Then, the customer starts a live chat and asks to speak to a live agent.

AI-powered data analysis infers from the customer’s browsing history that they have additional questions on this topic. So it routes the customer to a live agent knowledgeable about international banking.

This ensures the customer talks to an agent equipped to answer their questions and won't need to speak with another agent, creating a faster and more seamless experience.

4. Assist agents with recommended responses

Even the most experienced agents sometimes need to look through their business’ knowledge base to find the answer to a customer’s question or the path to resolution. When this information is difficult to find, it can result in long calls and longer queues.

AI-powered agent assistance can help customer support agents reach resolution faster. For example, Google Cloud Contact Center AI offers Agent Assist, which can analyze customer intent based on voice or written input, surface recommended resources, such as FAQ pages, and make suggestions to resolve the inquiry.

Additionally, agents can use the generative AI tool ChatGPT to create suggested responses tailored to the customer’s profile. This enables agents to respond quickly and accurately, contributing to faster resolutions.

5. Predict customer needs

Meeting customers’ needs proactively can help improve your business’ customer service. But to do so, you need quality customer data. AI can help customer service agents get more from customer data by analyzing it to extract two types of customer traits:

• Inferred traits: These derive from users’ actions and interactions. For example, if a customer has a history of purchasing running shoes and apparel, AI can infer that the customer is a runner.
• Predictive traits: These forecast using historical data and predictive modeling. For example, if a customer has a history of upgrading their cell phone every two years, and they’re approaching two years since their last upgrade, AI can predict the customer is likely to upgrade their phone soon.

So what do you do with these traits? AI-powered assistants can surface these traits during customer interactions, helping virtual agents and live agents predict customers’ needs and engage them proactively.

6. Identify cross-sell and upsell opportunities

The inferred and predictive traits we discussed above can help agents identify cross-sell and upsell opportunities likely to resonate with customers.

For example, say a customer messages an agent because they want to purchase a hoodie with their favorite sports team’s logo and have a question about sizing. The live agent could answer the question and stop there. But with AI assistance, they can go further.

AI can surface recommended products based on the customer’s purchase history, like a matching beanie. And since the customer has purchased a beanie from the brand before and left a positive review, it’s likely they’ll want to purchase another one with their team’s logo. With this data, the agent can suggest the product and increase the customer’s purchase amount.

7. Improve self-service resources

Businesses can use generative AI to improve self-service resources and write new content to help customers resolve their inquiries without contacting a live agent.

For example, you can gather insights from common customer questions to identify gaps in FAQ pages, then use ChatGPT to draft new content to fill these gaps. Finally, have an employee knowledgeable on the topic review and edit the copy to ensure accuracy and consistency.

You can also ask ChatGPT to review your current content and give you recommendations on how to improve it, such as condensing the content for customers to consume quickly. To make the most of the tool, write a detailed prompt explaining what you need. For example: “Please rewrite this FAQ page, condensing the content to no more than 500 words. Use bullet points where possible to make the content easier to scan.”

8. Translate in real time

AI can help expand your business’ reach by enabling live agents to chat with customers in any language. This allows your business to support customers worldwide, even when live agents don’t speak the customer’s preferred language.

For example, AI integration Lionbridge Language Cloud can detect the language of a customer’s text input and translate it in real time, showing the live agent the text in their primary language. The tool then translates the live agent’s response, enabling real-time communication across languages and allowing businesses to provide hyperpersonalized support.

9. Analyze customer sentiment

AI can analyze large amounts of customer interaction data in a matter of seconds, generating valuable insights for customer support and helping your contact center serve customers more efficiently.

For example, AI can extract the most common issues customers call about, like a bug in your software product. You can then share this information with the appropriate team to fix the issue.

Plus, AI can analyze individual customer interactions and identify customers likely to churn. Then, you can create tailored outreach to try to retain these customers.

Finally, AI tools can analyze sentiment in real time and route customers to the right agent or resource based on their needs. For example, if a virtual agent identifies negative sentiment in a chat conversation, it can escalate the conversation to a live agent (along with the context) to handle the interaction with the empathy and tact needed to de-escalate the customer's issue.

10. Summarize customer interactions

Integrating ChatGPT with your contact center dashboard is another way AI can help live agents work more efficiently. For example, live agents can use this generative AI tool to summarize customer interactions, noting the customer sentiment and necessary follow-up action.

This saves the live agent time in writing the summary, so they can move on to the next steps to support the customer or engage another customer.

Plus, with this summary saved to the customer profile, other team members can tailor customer outreach and understand how to best support the customer in the next interaction.

Implement customer service AI with Twilio

As the strategies in this post illustrate, customer service AI can empower agents to work more efficiently when equipped with quality customer data and integrated seamlessly into your contact center software.

Now that you have a better understanding of how AI can improve customer service, you’re ready to take the next step: implementing AI tools into your contact center.

To do this effectively, you’ll need a flexible contact center platform that uses AI to improve the customer experience. Twilio Flex is just that tool.

Flex enables businesses to adapt and scale contact centers to meet customers’ needs. Flex Unify, announced at SIGNAL and currently in private beta, uses real-time customer data and Twilio’s CustomerAI to deliver hyperpersonalized experiences across virtual and live agent interactions.

Powered by inferred and predictive traits, Flex Unify goes a step further, using AI to route customers appropriately, personalize conversations, and provide recommended responses to resolve customers’ inquiries.