47 Proven Chatbot Use Cases That Deliver Results 2024

The system is trained using machine learning techniques, using a large dataset of text from the internet. It is widely understood that we are on the precipice of even faster evolving AI functionalities – and every business and leader should be questioning how they can seize this opportunity to their advantage. This is not about striking the iron while it is hot – more about keeping up with the curve. However, your business’ approach to ChatGPT, as with all new technologies, must be calculated.

These use cases for chatbots include assisting with benefits enrollment, answering frequently asked questions, guiding employees through onboarding, and conducting exit interviews. Now, we will explore the valuable chatbot use cases in optimizing HR operations and delivering a seamless employee experience. AI-driven sentiment analysis tools can process vast amounts of feedback data from sources like surveys, reviews, and social media comments. These tools categorize feedback as positive, negative, or neutral and identify common themes and trends.

Onboarding and training chatbots facilitate the orientation and training process for new employees or users by providing guidance, resources, and assistance in a conversational format. These chatbots are designed to streamline the onboarding experience by delivering essential information. AI can be used in customer service to help streamline workflows for agents while improving experiences for the customers themselves.

For more sophisticated forays into data, it’s also possible to create custom dashboards. The most mature companies tend to operate in digital-native sectors like ecommerce, taxi aggregation, and over-the-top (OTT) media services. Leaders in AI-enabled customer engagement have committed to an ongoing journey of investment, learning, and improvement, through five levels of maturity. At level one, servicing is predominantly manual, paper-based, and high-touch. The live agent handover feature in e-commerce chatbots is an essential tool in ensuring seamless customer support. This function allows a chatbot to recognize when it is unable to assist with a customer’s request and transfer the interaction to a human customer service agent.

Live Chat vs Instant Messaging: Which One Is Right for Your Business?

The company targets different visuals and bot sequences based on the page someone’s browsing. The messenger marketing ecosystem has changed for many businesses using chatbots, but the goal remains the same for all i.e. instant and convenient service. When implemented as a part of customer support, bots can automate the whole process of serving customers, when the support reps are busy or unavailable. The 24×7 availability increases the resolution rate which reduces customer churn rate. Use cases are descriptions of the ways users interact with systems to accomplish tasks or reach goals.

It’s obvious that if you don’t know about some of the features that the chatbot provides, you won’t be able to use them. But you would be surprised by the number of businesses that use only the primary features of their chatbot because they don’t know any better. So, if you want to be able to use your bots to the fullest, you need to be aware of all the functionalities. Bots can collect information, such as name, profession, contact details, and medical conditions to create full customer profiles. They can also learn with time the reoccurring symptoms, different preferences, and usual medication.

And now many businesses are utilising the technology and are enjoying AI customer success. Two-thirds of millennials expect real-time customer service, for example, and three-quarters of all customers expect consistent cross-channel service experience. And with cost pressures rising at least as quickly as service expectations, the obvious response—adding more well-trained employees to deliver great customer service—isn’t a viable option.

Users can see options on their phone while an agent is talking and share input via text and images, such as names, addresses, email addresses, and more. They can also respond to visual elements, such as clickable menu options, during the conversation. Airbnb, a global brand for homestays, provides services for different local regions.

Today, chatbots have emerged as powerful AI-driven tools with diverse applications across various industries. With their ability to interact and engage with users through conversational interfaces, chatbots are revolutionizing the way businesses and organizations connect with their audiences. From streamlining customer support to optimizing sales processes, chatbots have become vital assets in delivering efficient and personalized services. Whether it’s providing real-time assistance, automating repetitive tasks, or offering personalized recommendations, chatbots continue to redefine the future of customer engagement and service delivery.

Using AI-generated content in agent responses

This 24/7 availability ensures that customers receive assistance outside regular business hours, enhancing overall satisfaction. Machine learning in customer service acts as a mighty co-pilot for your team of live agents. AI assistants, driven by machine learning algorithms, provide agents with real-time assistance during live conversations. These tools offer a range of support, from recommending relevant knowledge base articles to providing contextual recommendations based on similar resolved cases. By making resolutions faster and more efficient, they ultimately enhance customer satisfaction. Sales chatbots are versatile tools designed to raise various aspects of the sales process.

Through conversational interfaces, users can easily inquire about their orders, receive updates on shipping progress, and address any issues or concerns they may have. AI chatbots for business enable organizations to shift 64% of agents’ focus to solving complex issues, compared to 50% without AI. AI-generated content doesn’t have to be a zero-sum game when it comes to human vs. bot interactions. As with other types of written content, AI writing generators can be used to supplement—not necessarily replace—human-created written communications for customer support applications. These measures don’t solve anything for customers, but they go a long way in setting expectations and keeping them satisfied.

Or you can use it to automatically trigger a response that matches the language in the original inquiry. While chatbots are great at troubleshooting smaller issues, most aren’t ready to tackle complex or sensitive cases. Here are ten ways I recommend using AI for customer service based on our State of Service data. Understanding demanding customer expectations and predicting/addressing customer issues before they occur is one of the top challenges service leaders face today. Don’t let outdated case management practices cost your brand the loyalty of hard-earned fans. In this article, we uncover the customer service case management best practices that can turn every care interaction into a competitive advantage.

In this blog, we’ll delve into the role, benefits and use cases of machine learning in customer service, empowering you to elevate and align with the service standards set by top-tier brands. From support data, key performance indicators like Customer Satisfaction (CSAT), First Response Time (FRT), and Total Time to Resolution (TTR), can be pulled and viewed to improve existing workflows. For support agents, CSAT can help with measuring performance while helping staff across the organization, from product and marketing to sales, see where to work towards improvements. If you want to learn more about the applications of sentiment analysis in chatbots, read our comprehensive article. Similarly, service industry workers may be reluctant to adopt AI because they fear it will replace them in their line of work.

Businesses can then make data-driven decisions to enhance their offerings, address pain points, and improve customer satisfaction. With the rise of different problems or to gain familiarity with the offerings, every company strives to lower the response time and pace up the resolution process. The more efficient system in such a scenario is generative AI-based compared to traditional ones of humans.

Customer journey analytics can be predictive, feeding algorithms that provide insight of what can be expected in the future, commonly referred to as “forecasting”. Predictive analytics are massively popular in finance and marketing, and its applications are widespread. And there are concerns regarding the accuracy of AI systems in understanding and solving difficult customer queries. However, the job becomes easier with AI tools as they can collect data from all consumer interactions across different platforms. They can help you implement the gathered data at the right time and help you make the communication more personalised.

For instance, customer journey analytics can follow a customer from their first visit to their last purchase. Examining the actions clients take before completing a purchase helps locate obstacles and simplify the conversion process. For example, a business may utilize analytics to determine that a particular product feature is the source of the majority of complaints.

Chatbots are one of the best tools to improve user retention by managing customer service issues in a timely, efficient manner and upselling & cross-selling relevant products and services. 34% of customers returned to the business within 30 days after iterating with the bot. Chatbots are designed to understand user queries, provide relevant responses, and perform tasks or actions based on the context of the conversation. They can be integrated into various platforms such as websites, messaging apps, and voice assistants.

Healthcare industry opens a range of valuable chatbot use cases, including personal medication reminders, symptom assessment, appointment scheduling, and health education. These virtual assistants improve patient engagement, streamline administrative tasks, and contribute to evidence-based clinical decision-making. Marketing chatbots are powerful tools that offer various applications to elevate marketing efforts and enhance customer engagement.

You can use this data to predict customer needs or issues and address them before they arise. The use of AI for predicting consumer problems can help gain the trust of your prospects and grow your business easily. For example, the AI tool can analyse every interaction quickly without any biases. It can also thoroughly check the agent data for CSAT scores and handling time. Using this data the AI can generate a report that you can use to guide your agents for further interactions. Let’s have a look at 13 examples of using the new technology in your business.

Do your research before deciding on the chatbot platform and check if the functionality of the bot matches what you want the virtual assistant to help you with. They can answer reactions to your Instagram stories, communicate with your Facebook followers, and chat with people interested in specific products. In fact, nearly 46% of consumers expect bots to deliver an immediate response to their questions. Also, getting a quick answer is also the number one use case for chatbots according to customers. Chatbots can serve as internal help desk support by getting data from customer conversations and assisting agents with answering shoppers’ queries. Bots can analyze each conversation for specific data extraction like customer information and used keywords.

There are exciting possibilities for customer service teams to be able to do more to help their customers while keeping the quality of service high. ChatGPT’s conversational style will feel convincing and familiar to customers who want the speed of a bot but don’t want to lose the human touch. Each addition to the repository allows the machine learning model to learn and improve its ability to retrieve correct answers. Moreover, the model can proactively alert human administrators when updates or additions are needed, ensuring the knowledge base remains current and relevant. Today, customer service leaders face the daunting challenge of delivering exceptional service with increasingly limited resources.

Chatbots can check account details, as well as see full reports about the user’s account. Chatbots can take the collected data and keep your patients informed with relevant healthcare articles and other content. They can also have set push notifications for when a person’s condition changes. This way, bots can get more information about why the condition changes or book a visit with their doctor to check the symptoms. Chatbots can collect the patients’ data to create fuller medical profiles you can work with.

These are specifically designed for end-to-end conversational customer interaction. The chatbots and virtual assistants are utilized to handle routine inquiries, automate customer interactions and offer immediate responses. These types of generative AI provide real-time customer support, can handle high volumes of queries, and are integrated with messaging platforms. It’s true that chatbots and similar technology can deliver proactive customer outreach, reducing human-assisted volumes and costs while simplifying the client experience. Nevertheless, an estimated 75 percent of customers use multiple channels in their ongoing experience.2“The state of customer care in 2022,” McKinsey, July 8, 2022. HR chatbots offer a wide range of applications to streamline human resources processes and enhance employee experiences.

For example, customer experience analytics may include analyzing Net Promoter Scores (NPS) to measure customer loyalty. Monitoring NPS over time enables one to determine whether adjustments to service methods are enhancing or detracting from customer happiness. Customer experience analytics is to assess every point of a customer’s relationship Chat GPT with a company, from first contact to after-sale care. By analyzing feedback, satisfaction scores, and other service statistics, organizations can gain insight into the total customer experience. One of the biggest challenges we hear from customer service leaders is around limitations imposed by their current infrastructure.

Automating data collection ensures you can keep up with the vast amount of benchmarks that impact your care strategy. Sprout’s Case Management Report evaluates the quality and efficiency of customer care by analyzing key metrics like case volume, handle time‌ and response time. These efforts also boost efficiency and make sure agents have the resources they need, when they need them. Up-to-date documentation reduces the time spent hunting down answers in knowledge bases or from other team members.

They are not ready to drop in a ticket and wait for a customer service agent to connect with them hours later. Put together, next-generation customer service aligns AI, technology, and data to reimagine customer service (Exhibit 2). That was the approach a fast-growing bank in Asia took when it found itself facing increasing complaints, slow resolution times, rising cost-to-serve, and low uptake of self-service channels.

FitBot is the way trainers communicate with clients, both onsite and remote coaching. As per research, the participants who used the chatbot were 26% more likely to meet or exceed personal fitness goals compared to participants who didn’t https://chat.openai.com/ use the technology. Chatbots can be used to streamline your personal services such as fitness, diet, health, or day-to-day activities. Every fitness goal requires a different set of workout plans and a nutrition diet to be followed.

Remember to focus on your actors’ wants over the system’s capabilities to understand why users come to your system. Zapier can also make automating customer service apps about as simple as ordering your favorite breakfast meal from your favorite local fast food chain. Adding AI to the mix is like getting extra green chile on the side—without even having to ask for it. Learn more about automating your customer support, or get started with one of these pre-made examples using Zendesk and ChatGPT. If there’s a 10th circle of hell, it probably involves waiting for a customer service representative for all eternity. These tools can automatically detect an incoming language and then translate an equivalent message to an agent and vice versa.

Levit writes that beyond the Customer Effort Score, other useful customer retention metrics are Customer Churn Rate (CCR), in which customers lost are divided by customers from the beginning. Analyze all customer service activities so you know how to save costs and improve service quality. Businesses should use AI for customer service as it works 24/7 without getting tired. And this is one of the main reasons that AI tools are becoming famous for customer service. It allows humans to work on more complex tasks and AI to handle routine repeating tasks. Even before customers get in touch, an AI-supported system can anticipate their likely needs and generate prompts for the agent.

It will enhance it by automating routine tasks and providing support, but human interaction remains essential for complex or empathetic situations. At the moment, ChatGPT has the tendency to offer inaccurate responses when it does not know the correct answer to a question. The technology will develop to a point where ChatGPT will realize when it cannot help customers and escalate the matter to a human agent.

Chatbot analytics

If a customer wants to know account information, for example, ChatGPT for customer service falls short. ChatGPT is good at solving problems and skillfully navigates conversational discussions. But where ChatGPT’s knowledge falls short, it does not have the awareness to admit this and instead fills in the gaps.

Bots not only streamline customer experiences at every stage in the service process but are also aids to the support agents. One of the use cases of chatbots for customer service is offering self-service and answering frequently asked questions. This can save you customer support costs and improve the speed of response to boost user experience. But then it can provide the client with your business working hours if it’s past that time, or transfer the customer to one of your human agents if they’re available.

This shows customers where they are in line and how long they have to wait for an agent if they aren’t willing or able to troubleshoot themselves. Using machine learning, you have customers’ profiles automatically segmented into groups aligning browsing history with your product categories. You then have email follow-up campaigns to offer each group 10% discount codes for products within those categories.

• By monitoring interactions throughout all phases, companies can acquire valuable insights about the customer journey and pinpoint opportunities for enhancement.
• Service leaders are facing a skills gap because AI, particularly generative AI, which is a relatively young discipline.
• A chatbot is a program powered by artificial intelligence (AI) that conducts conversations with users through text or speech interfaces.

Learn how Learn It Live reduced support tickets 40% with an AI-powered chatbot and how the nation’s largest transit ad company transformed its customer support with AI. 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.

And, if the AI can’t fully resolve an issue, it smoothly transitions to human support by pre-filling a support form, eliminating repetitive data entry for customers. Customer support teams routinely handle a diverse range of customer inquiries, many of which involve repeatable processes. These can range from simple tasks like guiding customers to specific documentation pages, to helping customers through the process of configuring their domain. Establishing customer service tiers will help you create structure around your customer service case management approach. Cases are routed based on their complexity, urgency and the level of expertise required to solve them.

How to get started with AI in customer service?

To further improve customer experience, emotion AI solutions can estimate customer emotions by analyzing visual, textual, and auditory customer signals. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. 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.

This enables rapid resolution with high accuracy, eliminating the need to transfer the customer to another department and minimizing hold times. By leveraging machine learning, customer service teams can optimize service delivery, improving agent productivity and customer satisfaction. Support leaders managing data should differentiate when to use real-time and historical analytics, and the use of prescriptive dashboards shared across the organization can aid in the visibility of data. Customer service managers get the most out of descriptive customer experience analytics by recognizing trends, such as an uptick in tickets near product launches or during the holiday retail season.

Chatbots offer a variety of notifications you can set, such as minimum balance notifications, bill pay reminders, or transaction alerts. You can improve your spending habits with the first two and increase your account’s security with the last one. People can add transactions to the created expense report directly from the bot to make the tracking even more accurate. Depending on the relevance of the report, users can also either approve or reject it.

A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service. Engaged customers are more loyal, have more touchpoints with their chosen brands, and deliver greater value over their lifetime. The companies we’ve highlighted in this blog are leading the way in adopting these transformative technologies, enhancing their customer service strategies, and delivering exceptional value to their customers. From providing round-the-clock assistance to predicting customer behavior and preferences, AI is increasingly becoming an integral part of delivering a seamless and personalized customer experience. Charlie provides swift answers to customer queries, initiates the claims process, and schedules repair appointments. To manage this unprecedented volume without compromising on their high customer service standards, Decathlon turned to Heyday, a conversational AI platform.

If a machine can handle the majority of customer inquiries, customer service agents are free to focus on adding value instead of fighting fires. No matter how their problems are resolved, customers will be more satisfied if they receive great customer service. ChatGPT seems more human-like than its predecessors, fuelling the interests of customer service teams who are interested in this technology.

These bots can understand the query and pull from a vast knowledge base to provide an immediate response. If the bot cannot resolve the issue, it forwards the request to a human agent and gives the customer an estimated wait time. While the conversational AI vs. chatbot customer service use cases debate has been going on for a long, we should not forget how conversational bots could use artificial intelligence (AI) to assist users over both text and voice. Voice chatbots are all about facilitating your users with a seamless experience with your business.

There are those who still underestimate AI’s capabilities, who stand to lose by eschewing it entirely. There are those who overestimate AI’s capabilities, who stand to lose by rushing in prematurely. Strong customer loyalty is indicated by LCR, which defines customer loyalty and determines the percentage of customers who are continuously active within the organization. It shows the level of satisfaction of clients and the success of the efforts to retain them.

Catering to such a diverse customer base can be challenging, especially regarding language barriers. The more insights into actors, interactions, and outcomes, the better—which is why it’s important to collaborate on use cases with your team and stakeholders. A shared online whiteboard like FigJam streamlines collaboration between remote teams to help you build out comprehensive use cases.

Vainu, a data analytics service, asks questions to visitors with their VainuBot. Visitors can quickly make choices by simply selecting the option most relevant to them. At the end of the conversation, the bot asks for their email address to book a demo or send a report. You can also message Digit commands by texting the number to check your balance updates.

By focusing on the urgency and impact of each case, teams can allocate resources more effectively. All together, these efforts enhance customer satisfaction by making sure customers get timely resolutions that meet—and exceed—their expectations. Thus, if a query becomes too complex for ChatGPT, it will fall back on its powers of making conversation rather than its powers of knowledge.