How to leverage AI and machine learning in customer service

AI and machine learning (ML) in customer service are rapidly evolving, and the number of applications they offer to business operations is expanding at a similar pace. 

According to Statista, 73% of digital professionals indicate that artificial intelligence and machine learning have the potential to impact customer experience at one of the fastest rates when compared with other emerging technologies.¹

Intercom’s 2023 report, The State of AI in Customer Service, further reinforces this notion. According to the study, an overwhelming 73% of support leaders anticipate that customers will expect AI service within the next five years.² Moreover, 72% of these leaders recognize that AI offers a competitive advantage in the realm of customer service.

In this article, you’ll learn what machine learning is, gain a better understanding of the roles ML and AI can play for your business, and discover their specific applications in customer service.

What is machine learning?

Machine learning is a subset of artificial intelligence that allows systems to learn from data and improve automatically without being explicitly programmed.

Machine learning algorithms build a mathematical model based on sample data, known as "training data." As the machine learns from the information it analyzes, its ability to accurately predict outcomes or make choices improves. The more data a model processes, the better it can perform the task it was initially trained on.

One common application of machine learning found on many websites and apps are recommendation systems. The goal of these recommendation tools is to provide personalized suggestions to users based on their past behaviors, preferences, and purchases (and in many cases, viewing and listening habits). 

Sites like Amazon, Netflix, YouTube, and Spotify collect huge amounts of data on what individual customers view, purchase, and rate. The data then forms a training set used to build machine learning models. For instance, Netflix tracks every movie and show you watch, whether you completed it or stopped partway through. 

Recommendations may not produce great results right away, since the algorithms need time to learn each user’s specific taste. However, as users engage with the service more and more, a distinctive profile and set of preferences start to emerge from the accumulation of data.

Types of machine learning

There are three main types of machine learning:

• Supervised learning: This uses labeled datasets to train models on how to classify data and predict outcomes accurately. An example of supervised learning might be showing a machine learning model many labeled pictures of cats and dogs so it can identify new pictures.

• Unsupervised learning: Here, the model identifies hidden patterns or insights from datasets without labels or specific programming. Through unsupervised learning, the model reveals structures in the data that may ultimately yield useful information.

• Reinforcement learning: The process of reinforcement learning trains a model to make decisions by rewarding it for correct actions. It learns optimal behavior within a specific context, such as winning a game or completing a complex task. The model determines the best action to take based on the accrual of feedback from its interactions.

What is the difference between AI and machine learning?

Though certainly connected in important ways, artificial intelligence and machine learning are distinct concepts. AI is the overarching term for computers that are programmed to mimic human thinking and problem-solving abilities. Machine learning, on the other hand, focuses on how algorithms improve their performance over time as they are exposed to more and more data.

In simple terms, machine learning is a type of AI. AI technologies like chatbots, virtual assistants, and self-driving cars rely on machine learning algorithms to gather data, recognize patterns, and make smarter decisions independently.

Having clarified that difference, we'll incorporate both terms – AI and machine learning in customer service – across this article to illustrate their roles and applications.

How can AI be used in customer service?

AI-powered chatbots

One of the most impactful applications of machine learning in customer service today is undoubtedly AI chatbots. Powered by artificial intelligence and machine learning algorithms, these chatbots are able to interpret user inputs and subsequently generate relevant, context-appropriate responses. 

Perhaps most impressively, AI chatbots get better over time. As they interact with more users, they learn from each experience, allowing their performance to continuously improve. 

Artificial intelligence chatbots can expertly guide users through common troubleshooting scenarios like logins, bill payments, technical issues, and product features.

AI-enhanced help desk solutions

A remarkable application of machine learning AI in customer service is within help desk platforms. These sophisticated software solutions can leverage the power of AI to significantly enhance customer support capabilities.

For example, Intercom’s AI-enhanced help desk serves as a valuable tool for customer service teams, offering the following benefits:

• Expand or rephrase responses: AI enables help desk software to assist team members in composing customer responses. By providing shorthand customer replies, AI generates complete, suggested, or rephrased messages, saving time and ensuring accurate and well-crafted responses.

• Change tone: Effective communication requires identifying and utilizing the appropriate tone. AI-enhanced help desks can automatically adjust the tone of written messages to align with the desired style, whether it's casual and friendly or something more reserved and formal.

• Fix grammar: Maintaining professionalism in written communication is essential. Artificial intelligence help desk tools excel at grammar and spelling correction, guaranteeing flawless replies that reflect attention to detail and enhance the overall quality of customer support.

Translate: In a globalized business landscape, language barriers can hinder effective customer service. AI-enhanced help desks offer multilingual support by swiftly translating messages into different languages, enabling seamless communication and catering to diverse customer needs.

Additionally, AI-enhanced help desks can generate concise summaries of customer conversations or tickets – a valuable asset for customer service teams that need to convey an abundance of information in a time-effective fashion. Further, the feature enhances collaboration within the customer service team, facilitates knowledge sharing, and enables efficient tracking of customer interactions.

Read more: Key features and benefits of AI customer service software. 

Knowledge bases

Knowledge bases, such as a company help center that features support articles, present a prime opportunity to leverage machine learning in customer service. 

One simple, practical way to apply machine learning to knowledge bases is by using algorithms to improve the relevance and accuracy of search results and recommendations. With machine learning systems analyzing keywords, interaction histories, and common inquiries, customers can more easily find the information they need to solve issues on their own.

Going a step further, support reps can tap into past customer conversations to automatically generate entirely new articles that proactively address emerging needs. AI can also enhance existing content by identifying knowledge gaps and expanding the level of detail and breadth of coverage on a particular topic.

While human quality control remains a critical component to strong, trustworthy knowledge bases, AI and machine learning show great potential to significantly expedite the traditionally laborious processes of drafting articles from interactions and expanding on existing support documentation.

Predictive analysis

AI and machine learning in customer service have become indispensable, especially when it comes to leveraging predictive analysis. This powerful capability allows businesses to optimize their support operations and stay one step ahead of customers’ inquiries

Consider Intercom’s feature, “Predictive Answers.” When a customer initiates a conversation through Messenger (our live chat tool), our AI chatbot, Fin, springs into action. It intelligently detects relevant context, such as the specific page the customer is on within an app or website, and suggests the most common and useful answer tailored to that customer.

That level of predictive capability adds immense value to customer service interactions. By proactively presenting the most relevant information based on the customer's context, businesses can provide responses that are faster than ever but don’t compromise on accuracy or relevance.

Find out how AI for customer support helps your team and your customers.

What is the role of machine learning in customer service?

All the machine learning applications in customer service we've discussed thus far highlight the key roles of this technology and the benefits it introduces into the industry. Now let’s dive deeper into some of the technology’s most noteworthy advantages.

24/7 self-service solutions

Machine learning and AI play a pivotal role in expanding customer service capabilities beyond the traditional hours of operation. By merging comprehensive knowledge bases with the round-the-clock interaction capabilities of AI chatbots, companies can offer customers a convenient self-service alternative for support. 

AI chatbots can recommend the most pertinent documentation to resolve issues at any time of day. As a result, customers gain the ability to acquire assistance on their own schedules while also reducing pressure on overtaxed support teams.

Scalable service operations

As businesses expand, customer demand grows alongside them, and scalability becomes ever more crucial to your customer service operations. Fortunately, machine learning can help growing companies meet this formidable challenge by adapting seamlessly to varied operational sizes and volumes. 

AI and machine learning applications in customer service are uniquely scalable – that is, they can effortlessly assume expanded workloads as responsibilities increase. This flexibility provides a clear advantage, allowing both startups and more established enterprises to implement AI and achieve support sustainability suited to their dynamic needs.

Customer experience personalization

Personalization is essential for both customer satisfaction and business growth. According The State of Personalization Report 2023 by Twilio Segment, 62% of business leaders mention improved customer retention as a benefit of personalization efforts.³

Machine learning in customer service precisely addresses this surging demand for personalization. By analyzing patterns in customers' previous contacts, machine learning gains insights into what truly matters to each person. It enables a tailored approach that resonates with each customer and keeps them engaged.

For instance, AI chatbots are able to offer customized responses. By understanding a customer's specific needs and what types of solutions have worked well for them before, chatbots shape their answers accordingly. They can also provide personalized recommendations that feel relevant to specific situations and circumstances (rather than frustratingly generic). 

Feedback loop for continuous improvement

Machine learning in customer service can help maximize the value of feedback through automated data analysis. As AI has the ability to glean insights from diverse unstructured information sources, it can efficiently distill the important themes within surveys, comments, and other feedback streams.

Tools using advanced machine learning can categorize and surface trends within large volumes of customer input. For example, solutions like Delighted AI leverage statistical modeling refined by relevant survey data. This allows recurring patterns to be identified and suggested from within your unique feedback set.

With ML constantly enhancing how input is evaluated, teams receive actionable guidance to drive continuous improvement that responds directly to what matters most to their users. The end result is a far more strategic, insightful approach to the feedback loop that positively boosts both the customer experience and business performance over time.

Multilingual support

In today's global marketplace, offering multilingual customer service is imperative. With machine learning, companies can meet language barriers head-on and support an international customer base like never before.

Machine learning applications in customer service can recognize the language used in a user’s question. The ML platform can then generate a response using content that has been translated into that same language, providing a seamless experience in which customers receive support in their native tongue.

Consider a scenario where a Spanish-speaking user inquires about ordering a product from the US. They reach out to the company's AI chatbot in Spanish, prompting the chatbot to recognize that the message is in a different language. 

The chatbot then utilizes the company's repository of FAQs and documentation to formulate and translate its response into Spanish. As a result, the user receives a comprehensible answer in their native language instantly, without any additional steps being imposed on them. 

Read more: How you can use AI in customer service to help teams and customers.

AI chatbots and machine learning in customer service

As we've discussed, AI chatbots are one of the key machine learning applications in customer service. After all, ML is what allows chatbots to understand what people say, identify important patterns in large amounts of data, and use those patterns to respond intelligently.

Below, you’ll learn more about the specific capabilities and benefits that an AI chatbot brings to enhancing customer service operations.

Speed up query resolution and ease the workload

One of the biggest challenges customer support teams face is the never-ending flow of queries that require attention each day. A constant barrage of requests can weigh representatives down as they scramble to help all customers. However, with the right AI tool, many of these more straightforward inquiries can be handled almost instantaneously.

For example, our AI chatbot, Fin, can handle up to 50% of customer support queries in a matter of seconds. By taking on simpler tasks, Fin essentially lightens the load for reps. 

Any time-consuming lookups, account updates, or repeat questions can now be tackled by Fin instead of tying up a support representative. This frees up significant time for your team members. Rather than rushing from one ticket to the next, reps actually have space to breathe and apply their skills and expertise where customers need them the most.

Collect information upfront and create context

When trying to resolve customer issues – especially the most complex ones – understanding the full picture is crucial. By gathering relevant details upfront, AI chatbots can get a more holistic view of the problem and try different approaches to finding a solution.

Rather than making assumptions or jumping to conclusions, an effective AI chatbot will ask thoughtful questions to collect important context surrounding the customer's exact concerns. And if the chatbot can't offer a solution? To answer that question, let’s move on to the next functionality.

Transfer conversations for further assistance when needed

When a query surpasses what the chatbot can handle on its own, it’s able to instantly route the full conversation to a support representative. The chatbot knows when it reaches the limits of its programming, and proactively routes the user accordingly.

This specific benefit of machine learning and AI in customer service allows reps to pick up right where the conversation left off, with all necessary context at their fingertips. Issues aren't repeated, explanations aren't required, and the support journey can continue seamlessly.

Use existing content for fast and accurate responses

As we’ve already discussed, machine learning in customer service enables you to continuously generate new helpful articles for your knowledge base. It’s a functionality that seamlessly integrates with AI chatbots, enhancing their capabilities as they draw from existing content.

By optimizing your content sources with comprehensive, well-organized information, an AI chatbot armed with this knowledge can start providing reliable answers to customers quickly. The content acts as a training ground for the chatbot, equipping it with information to scan and understand how to best assist users.

The quality and coverage of the source content determine how effective it can be in practice. To truly support an AI chatbot, the knowledge base should aim to map out your offerings and address any common pain points customers may have. Organizing content in a logical, search-engine-optimized structure helps the chatbot further refine its responses.

Check out our step-by-step guide on building a knowledge base.

Unleash AI in customer service with Intercom’s chatbot

As business needs evolve, new technologies emerge that can, if applied correctly, significantly enhance the customer experience. That’s exactly why we launched Fin, the best customer service AI chatbot on the market.

Powered by large language models (LLMs), including OpenAI's GPT-4 and Intercom’s proprietary technology, Fin is uniquely positioned to resolve a substantial portion of customer queries through natural conversation.

Here are the key benefits of Intercom's Fin:

• Easy setup: Fin can start answering questions within minutes by consuming existing support content from various sources, including Intercom Articles, Zendesk Help Center, and more. 

• Customizable: Fin can be customized to fit your brand. You can rename it, change the icon, and adjust the messaging so customers feel they're interacting with a familiar service.

• Scalable: Fin allows for omnichannel support across Messenger, SMS, WhatsApp, and more, providing a seamless experience for customers wherever they are.

• Accurate: Fin's answers are drawn from your approved support content, ensuring customers receive accurate information tailored to your business.

• Insightful reporting: Track Fin's performance with usage and metric reporting, measuring success and further refining support over time.

Embrace the future with AI and machine learning in customer service. Start a free trial or ask for a demo to explore the full potential of Fin and our comprehensive customer support platform.

Sources

1. Statista, Consumer attitudes towards brands' usage of artificial intelligence and machine learning.

2. Intercom, The State of AI in Customer Service 2023. We commissioned an independent market research firm to survey a random sample of 1,013 global support leaders and practitioners. The margin of error for this study is +/- 3.7% at the 95% confidence level.

3. Twilio Segment, The State of Personalization Report 2023. The research was conducted using online surveys. Survey respondents were 3,001 adult consumers and 500 managers and decision-makers at consumer facing companies that provide goods and/or services online. Respondents were from Australia, Brazil, Colombia, France, Germany, Italy, Japan, Mexico, Singapore, Spain, United States, and the United Kingdom.