9 ways businesses use AI in customer service in 2024

Voice recognition, meanwhile, digitizes words and encodes them with data such as pitch, cadence and tone, and then forms a unique voiceprint related to an individual. Machine learning algorithms on customer data can predict the customer churn, the reason behind churn, and methods to retain them. Many documentation tools have started using some form of generative AI to help your team.

For businesses with global customer bases, the ability to offer multilingual support is, like my beloved Christmas breakfast burrito, massive. It may not be feasible for every seller to have support agents covering every major language in the world, but it is feasible to employ AI translation tools to support them. As resolution processes change, AI ticketing can change how it sorts and tags conversations, assigning tickets and keeping agents on top of issues. AI can take over manual and routine tasks and automate processes so they happen instantly, no rep input necessary. Some tools, like chatbots, can handle entire tasks independently, while others take on smaller tasks to ensure reps aren’t spread as thin.

Sentiment and advanced analytics

But while AI may be touted as the exclusive path to progress, it’s important to understand how it works; caution and a keen awareness of the technology’s limitations are going to be necessary. Our own research shows that, globally, an enormous $4.7 trillion is being left on the table each year thanks to negative customer experiences. As AI technology advances, we can expect to see even more innovative and effective uses in customer service. HomeServe USA, a prominent provider of home service plans, uses an AI-powered virtual assistant, Charlie, for their customer service. To manage this unprecedented volume without compromising on their high customer service standards, Decathlon turned to Heyday, a conversational AI platform. Facing challenges in supporting multiple languages and inconsistent ticket volumes, they turned to Zendesk, an integrated customer service platform.

Similar to infrastructure as a service (IaaS) and software as a service (SaaS), AIaaS offers a package that a third-party supplier hosts. This is an affordable and dependable replacement for software created by an internal team. With AIaaS, customers may take advantage of AI’s capabilities through tools and application programming interfaces (APIs) without needing to create intricate code.

examples of AI in customer service

Contact Center Pipeline reports that increasing the focus on coaching and development for agents is a top priority for contact center managers. AI-based call center training tools such as gamification, visual assistance and self-monitoring, cut down agent onboarding time and ensure reps are fully engaged from day one. NLP analysis also allows companies to extract product suggestions and complaints from online product reviews in order to proactively address any issues. These technologies enable companies to gain insights on a micro level — by understanding the emotions of each customer – as well as on a macro level, by keeping their finger on the pulse of their customer base’s opinions.

Companies across all industries are putting personalization at the center of their enterprise strategies. For example, Home Depot, JPMorgan Chase, Starbucks, and Nike have publicly announced that personalized and seamless omnichannel experiences are at the core of their corporate strategy. The obvious winners have been large tech companies, which have embedded these capabilities in their business models. But challenger brands, such as sweetgreen in restaurants and Stitch Fix in apparel, have designed transformative first-party, data-driven experiences as well. Using sentiment analysis to analyze and identify how a customer feels is becoming commonplace in today’s customer service teams.

While building out a robust knowledge base or FAQ page can be time consuming, self-service resources are critical when it comes to good CX. While chatbots are great at troubleshooting smaller issues, most aren’t ready to tackle complex or sensitive cases. AI technology can be used to reduce friction at nearly any point of the customer journey.

Automation and customer service AI

Customer Lifetime Value (CLV) is a metric that tracks how valuable a customer is to a company throughout the relationship. CLV is based on the premise that retaining existing customers delivers a higher return on investment than acquiring new ones. Studies have found that the likelihood of selling to a first-time customer is 5-20%, whereas for an existing customer the probability is 60-70%.

Even better, many customers prefer live chat over support channels like phone or email. Nearly 70% of consumers will try to solve a problem themselves first, and customers prefer help centers over all other self-service options. Although we use the term artificial intelligence when we talk about these tools, it’s important to understand that that’s more of a verbal shorthand than an accurate description of what’s happening under the hood. In the same way that a tailored shirt will fit you better than an off-the-rack one will, whether AI works for your organization depends on how well you understand your customers’ needs and your support team’s requirements. One area where AI is presently being used extensively and impacting is customer service. It is utilized in various ways to lower the cost of client service in sectors like fast food, banks, insurance, and retail.

Duolingo Max has generative AI-powered features that allow users to learn from their mistakes and practice real-world conversation skills. In addition to outgoing messages, you can also use AI to identify keywords and analyze the nature of the request before assigning it to one of your reps. Your average handle time will go down because you’re taking less time to resolve incoming requests.

With Zendesk AI, Rhythm Energy deflected 46% more tickets and reduced escalations by 50%. The transformation resulted in a doubling to tripling of self-service channel use, a 40 to 50 percent reduction in service interactions, and a more than 20 percent reduction in cost-to-serve. Incidence ratios on assisted channels fell by percent, improving both the customer and employee experience. While a few leading institutions are now transforming their customer service through apps, and new interfaces like social and easy payment systems, many across the industry are still playing catch-up. Institutions are finding that making the most of AI tools to transform customer service is not simply a case of deploying the latest technology.

The perils and promise of AI customer engagement

You can foun additiona information about ai customer service and artificial intelligence and NLP. Thus, it is always good to identify customer pain points and essential journeys first to start with. Scant is known for overcoming these challenges when adopting and implementing AI-based technologies. This article presents the array of emerging technologies and offers a framework for organizational transformations with the AI-driven customer journey. The article aims to resolve the personalization-privacy paradox by introducing a solution matrix separating personalization from privacy concerns.

But the compulsively antisocial part of my psyche that makes me not want to make phone calls also appreciates these shifts to using AI in customer service. Freaky or not, artificial intelligence is becoming as common as it is rapidly changing—here’s how companies like Blake’s are putting it to use. This AI tool identifies opportunities where human agents should step in and help the customer for added personalization.

The resulting software is referred to as ‘Generative AI’ tools since they’re able to generate new content on command. In the customer service industry specifically, AI is a powerful force for improving the overall customer experience – and driving up customer satisfaction in the process. MyFashionGPT enhances product discovery by allowing customers to make natural language-based queries, providing them with a variety of options across related categories, and enabling the completion of desired looks effortlessly​. As soon as Decathlon launched its digital assistant, support costs dropped as the tool automated 65% of customer inquiries.

By leveraging tools like CallRail’s conversation intelligence software, customer service teams can operate with heightened efficiency, ensuring improved customer experiences. Engage is a pre-built, intelligent contact center platform that transforms customer service. Engage combines voice and digital communication channels to improve operational efficiency and deliver valuable insights. The platform uses Generative AI to enhance customer service interactions and create personalized experiences that cater to the changing needs of businesses and their customers.

When using AI, be sure to set up an alert that notifies your service team if a customer is unhappy with your bot. If your chatbot has sentiment analysis capabilities, use it to gauge how frustrated a customer is and when your team should intervene. Lastly, there’s the raw ROI of integrating AI as a key tool for your customer service team.

For customers

They expect quick responses from service people, and rage increases the longer they wait. For example, the technology can identify patterns that indicate a customer’s intent based on web activity or text and route the call or chat to the appropriate agent. Intent prediction enables contact centers to up their game by giving customers the assistance they need in the way they want. Artificial intelligence (AI) – the science that deals with the creation of human-like learning and reasoning capabilities – has been catapulted into the spotlight in recent years.

First, we’ll take a look at how AI works, and then we’ll discuss the different ways you can use it to automate customer service tasks. Turn the people who know your business best into brand advocates with head-turning reward programs and impressive customer service. According to Lauren Hakim, a product marketer at Zendesk, proactive engagement is one of the most effective uses for AI-powered chatbots. A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service.

Many businesses currently employ chatbots to answer basic queries using information gathered from internal systems. Just like analyzing the sentiment of tickets, you can also analyze pieces of text—such as customer support queries and competitor reviews. You just need to set up the tags you want the AI model to use when analyzing and categorizing your text—as demonstrated below. With Zendesk, Rhythm Energy was able to spend less time training new agents while maintaining the same level of high-quality customer service. With access to the right data and customer context, bots can proactively make personalized recommendations based on a customer’s preferences, website behavior, previous conversations, and more. Rhythm Energy, a renewable energy company, uses bots to respond to customers quickly and reduce escalations to the support team.

Customers will simple requests are engaged with immediately, while those with more complex issues are met with a human response. And, if the AI can’t resolve the issue, it can redirect the call to a service agent who can. The AirHelp chatbot acts as the first point of contact for customers, improving the average response time by up to 65%. It also monitors all of the company’s social channels (in 16 different languages) and alerts customer service if it detects crisis-prone terms used on social profiles.

Once you’ve trained the AI model with your data, you’re ready to set up its next steps. Essentially—what should your model do once it’s reached a decision on each piece of data? Training your data with an AI tool is as easy as hitting go and waiting for the results. The AI model analyzes your data in order to make accurate predictions on new data—but these predictions are subject to a degree of uncertainty. That’s how you’ll train your own AI model to categorize data according to your specifications. This could help you notice trends and make product changes that will eliminate the problems customers are facing.

AI can automate workflows to help close sales with chatbots that offer discounts, send reminders to the customer to complete the purchase, or proactively reach out to see if they have any questions. Leading natural language understanding (NLU) paired with advanced clarification and continuous learning help IBM watsonx® Assistant achieve better understanding and sharper accuracy than competitive solutions. The future of AI in customer service may still include chatbots, but this technology has a lot more to offer in 2023. It’s a great time to take advantage of the flexibility, efficiency, and speed that AI can provide for your support team.

AI tools can listen to every interaction and score agents against things like script compliance, empathy and issue resolution, and even proactively book coaching sessions whenever a relevant opportunity arises. Natural language processing uses models trained on huge conversational data sets to be able to understand everything being said in real-time. And that means being able to understand the difference between outstanding service and an outstanding bill. Moreover, it efficiently routes calls to the right departments based on the customer’s needs and even offers real-time guidance to human agents during customer interactions.

The HubSpot Customer Platform

Conversational AI customer service platforms – known as virtual assistants or chatbots – represent a promising technology that is already projected to cut business costs by as much as $8 billion in less than five years (Juniper). This is likely one reason why Oracle found that 80% of sales and marketing leaders say they currently use or plan to deploy chatbots in the near future. The company also licenses its brand to a lesser-known, independently operated sister company, Brinks Home. The Dallas-based smart-home-technology business has struggled to gain brand recognition commensurate with the Brinks name. It competes against better-known systems from ADT, Google Nest, and Ring, and although it has earned stellar reviews from industry analysts and customers, its market share is only 2%.

With the launch of generative AI, many chatbot tools have started introducing the technology into their products. They’re becoming true chat “bots” — software that’s capable of understanding text inputs, then generating human-like responses based on the information they’ve ingested. The practical applications for organizations and customer service teams are still a work in progress, but smart assistants such as Alexa, Google Assistant and Siri are an exciting avenue for personalized service. Customers appreciate and prefer when an organization communicates via their preferred platform, and for some people, that may be via their smart home device. Imagine a future where a user can bypass a phone call or email and troubleshoot any product or service concern via a simple question to their smart speaker. Simplified communications like this could be the difference between a satisfied or frustrated customer.

Biometrics refers to body measurements and calculations for the purpose of authentication, identification and access control. Physical biometric solutions analyze parts of the human body, such as a person’s face, iris or fingerprints, while behavioral biometric solutions analyze other characteristics, such as gait, voice, or interaction with a device. The field is going mainstream with a 2017 Tractica report predicting that biometric hardware and software revenue will grow into a $15.1 billion worldwide market by 2025, at a CAGR of 22.9 percent. We’re looking forward to being your companion on this journey — that’s why we’re building thoughtful AI-powered features that only improve your customer conversations. With the introduction of generative AI, these customer insight tools can now generate actionable summaries of trends, highlights, and concerns from your customer data. Customer service leaders have known for ages that chat support is usually a cheaper and more efficient way to provide support.

These “answer and response” chatbots don’t use machine learning, NLP, or dialog management. This means that while chatbots may manage client requests that proceed in a predetermined manner, artificial intelligence customer support they cannot improvise in the event of unexpected twists. Collaborators can extract important information from client feedback using language analysis technologies and modify their messages.

Are there complexities in the return process that are driving customers to competitors? By compiling this data en masse, businesses can see what’s driving real customers either toward or away from competitors based on customer service experiences. While Interactive Voice Response (IVR) systems have been automating simple routing and transactions for decades, new, conversational IVR systems use AI to handle tasks. Everything from verifying users with voice biometrics to directly telling the IVR system what needs to happen with the help of natural language processing is simplifying the customer experience.

• At the same time, leaders are wondering how to avoid common pitfalls in their AI usage so they don’t spend unnecessary money on flashy tools that won’t deliver.
• Because the translation can happen immediately (and without involving a human translator), the customer can experience more convenient and efficient support.
• That doesn’t mean that these AI tools will get infinitely smarter until they can take over the planet – it just means that every new interaction gets added to the model, resulting in smarter results in the future.
• With AI in particular, there are a few strands working together to help move businesses in that direction.
• This personalized content creation and delivery approach keeps Netflix at the forefront of the streaming industry.

However, with Zendesk, AI for customer service is accessible to anyone and sets up in minutes, not months. There’s no need for developers, data scientists, or a heavy IT lift, so your team can quickly deploy AI across your business and hit the ground running. It’s also intuitive for agents to use and available alongside all their tools in a centralized workspace. Implementing AI for customer service requires significant planning, testing, and refinement–which is why it’s so important to choose an AI solution that takes this work off your team’s plate.

Not to mention, learning how to operate each new tool and figuring out where it fits in your team’s workflow. ChatGPT, Microsoft Bing and Google Bard are all AI-powered tools that use large language models to train their understanding of how we use language to communicate. Charlie provides swift answers to customer queries, initiates the claims process, and schedules repair appointments. The fact that the digital assistant could understand and respond to over 1000 unique customer intentions is a testament to the power of AI.

VR replaces the user’s view and provides a virtual environment on a 3D wearable frame. MR merges the real and virtual worlds and can project a virtual reality environment in natural surroundings. Imagine a shopping experience where customers enter a vegetable shop and find themselves in a mixed reality of a farm.

By transitioning these frequently asked questions to a chatbot, the customer service team can help more people and create a better experience overall — while cutting operational costs for the company. When it comes to customer service, companies use AI to enhance the customer experience and enrich brand interactions. Instead of spending all of their time responding to client queries, service personnel have more flexibility to focus on activities that truly require human-to-human interaction. Using AI in customer service allows customer service teams to gather consumer insights.