The ultimate guide to AI customer service chatbots
Understanding AI customer service chatbots: The new business imperative you need to know
Have you interacted with a fun yet helpful customer service chatbot lately?
If you’ve visited a large brand’s website, chances are you’ve seen a customer support chatbot. You may have interacted with AI customer service chatbots to get an answer to a question, to take an action (such as viewing your order history), or just out of curiosity!
In this blog, we’ll tell you everything you need to know about AI customer service chatbots! We’ll delve into how customer service chatbots are shaping the future of customer interactions, examples of well-executed customer service chatbots, best practices for designing and implementing AI customer service chatbots, and case studies of companies that have found success using AI custom chatbots.
First, let's define AI customer service chatbots!
What is an AI customer service chatbot?
An AI customer service chatbot is software powered by artificial intelligence (AI) that lives on a business’s website or app and can answer consumer questions digitally. By simulating human conversation in a digital chat experience, customer service chatbots help companies automate and improve customer interactions and provide more efficient AI customer support.
Think of Apple’s Siri, but branded and embedded into your favorite brand’s app or website. Most customer service AI chatbots aren’t quite as sophisticated as Siri (yet), but the concept is similar.
It’s important to note that today’s AI chatbots are not the same as the scripted chatbots of years past. With the introduction of generative AI into chatbot technology, the experience has vastly improved from early chatbot software limited to pre-written scripts.
Today’s AI customer service chatbots use advanced technologies like natural language processing (NLP) and machine learning (ML) to understand, predict, and respond to customer inquiries in a way that’s both human-like and highly efficient. This integration of AI allows chatbots to handle a wide range of customer service tasks, from answering frequently asked questions to providing personalized product recommendations and processing returns.
One of the significant benefits of customer service AI chatbots is their real-time availability. AI customer support chatbots can provide round-the-clock service where human support agents are limited to specific working hours. Serving immediate responses to customer inquiries at all hours of the day and night is a win for businesses and their customers. With 74% of customers willing to speak with a bot to get an answer to a simple question and 90% of people wanting businesses to use chatbots, consumers are primed to engage with chatbots. Moreover, companies that use chatbots see a nearly 70% increase in sales, and industries such as e-commerce see a whopping 70% increase in conversions with ecommerce AI chatbots!
Efficiency is also a huge benefit. Since support AI chatbots can handle many queries simultaneously, this can dramatically reduce wait times and free up human customer service agents to deal with more complex issues. It’s no wonder that so many businesses that add chatbots experience a positive impact on their bottom line: nearly 60% of companies report a significant return on investment (ROI) from using chatbots.
With more customers requiring customer support online, understanding and effectively implementing an AI customer service chatbot is a crucial strategy for satisfying this growing demand. As customer service chatbot technology continues to improve, becomes more accessible, and transitions to more straightforward implementation, the shift to integrate AI into customer support is only poised to grow.
Revolutionizing customer service with generative AI chatbots
Chatbots have been around for a long time, with the widely recognized first chatbot, Eliza, created in 1966 by MIT computer scientist Joseph Weizenbaum. Chatbot technology has been characterized by a series of advancements and lulls, and several decades passed until the technology emerged on the consumer market after the introduction of the internet as we know it today. Conversational or AI-powered chatbots use NLP and ML to learn and improve over time. This technology came onto the scene in 2010 with IBM’s Watson system. The 2010s also saw the introduction of both Apple’s Siri and Amazon’s Alexa.
But what we’re most interested in here is the introduction of generative AI chatbots, which was kicked off with Jasper AI’s emergence in 2021. Since then, chatbots like ChatGPT and Bard, which are trained on massive data sets and are far more advanced and “intelligent” than previous bots, have quickly advanced the capabilities and applications of this technology. (Check out TechTarget’s full timeline of the evolution of chatbots.) This AI chatbot technology can be applied to fulfill various business customer service (and many other) tasks.
The current AI chatbot technology has undoubtedly transformed how businesses interact with customers, offering more personalized, efficient, and intelligent solutions.
Let’s think back a decade. Businesses that used early chatbots relied on basic, rule-based technology. These early chatbots followed predefined rules and scripts to respond to customer queries. They could only answer questions they had been scripted to answer and could not converse in a more natural, human-like way. They were only helpful in a limited capacity and could not assist with more complex questions or inquiries.
Many of these limitations have been eliminated by the introduction of GPT chatbots. Unlike their rule-based predecessors, these chatbots use ML and NLP to learn from interactions, adapt over time, and handle a broader range of inquiries. Because of their advanced training, LLM-powered chatbots can respond more nuancedly and with better awareness of the context of the conversation, which is very similar to how humans interact.
Chatbots can be broadly categorized into three types:
Rule-based chatbots: These are the simplest chatbots, programmed to respond to specific commands or keywords. They’re reliable for straightforward tasks but lack the flexibility to understand variations in language or handle unexpected queries.
AI-powered chatbots: These chatbots are a leap forward. AI-powered chatbots use AI to understand language and context to engage in more natural, conversational interactions. They can be trained on past interactions and improve their responses. Dialogflow chatbots use natural language understanding (NLU) and natural language processing (NLP) to interpret user inputs, match them to appropriate intents, and generate relevant responses. This enables the creation of chatbots and virtual assistants that can interact with users in a way that mimics human conversation. However, it is less advanced and flexible than large language models for generating content.
Generative AI chatbots: These chatbots represent the cutting edge of chatbot technology. Because of their training on massive data sets and quickly advancing ML and NLP technology, they generate their replies and offer a level of personalization and flexibility that was previously unimaginable. These customizable AI chatbots can engage in complex conversations, continuously learning and evolving their understanding of language and context.
The rise of AI in customer service through AI-powered and generative AI chatbots marks a significant shift from transactional customer service chat interactions to more engaging, conversational experiences. Today’s AI chatbots don’t just respond to customer needs; they anticipate them, leading to more satisfying and effective customer interactions. Let’s explore some key benefits of using AI chatbots for customer service.
Top 10 advantages of AI customer service chatbots: Here's why you need them
Custom AI chatbots have notably transformed customer service, offering a range of benefits that enhance both customer experience and improve operational efficiency:
24/7 availability - AI chatbots are available around the clock so customers can get assistance anytime, helping improve overall customer service satisfaction.
Handling large volumes of queries: AI customer service chatbots can manage numerous conversations simultaneously, significantly reducing customer wait times and improving efficiency.
Personalization: By analyzing customer data, AI chatbots can deliver personalized experiences, making suggestions tailored to individual preferences and past interactions.
Cost efficiency: Implementing AI chatbots is an effective way to augment your support staff while limiting the hiring of human agents, though human customer support agents will always be necessary to oversee chatbots, handle complex queries, and offer the crucial element of empathy in customer interactions.
Consistent responses: AI customer service chatbots offer consistency in responses, ensuring that the information provided to customers is accurate and uniform, avoiding inconsistencies or errors from miscommunication.
Improved customer engagement: With conversational AI, chatbots can engage customers in a more human-like manner, helping enhance the interaction quality efficiently (still, AI chatbots are not perfect and require human oversight).
Instant response: Speed is crucial in customer service. AI chatbots provide immediate responses to queries, helping brands significantly improve response times and freeing human customer service agents to deal with complex inquiries and situations requiring a human touch. This also increases CSAT.
Reduced human error: LLM-powered chatbots help minimize human error in customer service interactions. AI customer service chatbots mitigate damage from the inconsistent presentation of information, helping support accurate information delivery (though chatbots may present inaccuracies and require human supervision).
Scalability: Customer service AI chatbots can quickly scale up or down based on demand. AI chatbots for customer service offer more flexibility during peak periods or promotional campaigns, helping supplement your human staff.
Analytics and insights - AI customer service chatbots can gather and analyze large volumes of data from customer interactions, providing valuable insights into customer needs and preferences. Brands can use this data to improve service delivery.
Integrating AI customer service chatbots can enhance the entire customer experience when implemented effectively. Sendbird’s AI chatbot technology offers an advanced customer service AI solution for businesses across industries. Let’s look at how it works and how you can quickly get up and running.
How to leverage Sendbird for superior customer service AI
Sendbird’s customer support AI chatbot offers the dependability of proven customer support software combined with cutting-edge AI chatbot technology. This combination delivers a powerful AI-powered customer support solution that redefines the efficiency and effectiveness of customer interactions with features that include:
Personalized customer interactions: Our SmartAssistant delivers customized experiences informed by NLP and ML training. Analyzing customer interaction history and preferences, it tailors its responses and recommendations so that each interaction is individualized.
Efficient query handling: With SmartAssistant, businesses can efficiently manage high volumes of customer inquiries. The AI chatbot handles routine questions, providing instant responses, while more complex issues are escalated to human agents, optimizing the overall customer service process.
24/7 service availability: SmartAssistant ensures that customers have access to support services at any time instead of limiting chat hours to the availability of your human support staff. Offering constant availability can significantly enhance customer satisfaction.
Scalability and flexibility: Adding a SmartAssistant to your customer support solution allows you to scale according to business needs. This is a flexible solution that adapts to varying customer interaction volumes.
Actionable insights for continuous improvement: Beyond handling customer interactions, Sendbird’s integrated solution collects valuable data to help your business better understand customer needs and continually improve your service offerings.
Seamless integration with business ecosystems: Sendbird’s customer service chatbot solution is designed for easy integration with existing business systems so that you can ensure a cohesive and efficient customer service experience across all channels.
Advanced AI capabilities: The heart of Sendbird's SmartAssistant lies in its OpenAI's Chatgpt integration. The LLM allows the chatbot to understand and process natural language effectively, ensuring a smooth, human-like interaction with customers.
Discover the full potential of AI in customer support with Sendbird’s AI customer service chatbot.
Virgin Mobile UAE improved their CSAT with Sendbird Desk.
10 guidelines on how to craft an exceptional AI customer service chatbot
It’s no easy feat to design a chatbot that delivers an exceptional customer experience. Many businesses set out to develop a custom AI customer service chatbot internally, only to run into barriers with in-house resourcing, ongoing maintenance, and the continual upgrades required to keep up with developments in the industry. This is why companies often partner with a customer service chatbot provider, like Sendbird, specializing in this technology.
The goal is to create a chatbot that understands and responds to user needs and engages customers in a way that feels natural and helpful — upholding your brand standards while making your customers feel valued. Here are some guidelines to keep in mind to craft chatbots that excel in customer interactions:
Focus on Natural Language Processing (NLP)
The foundation of any effective AI customer service chatbot is its ability to understand and process human language. Robust NLP capabilities allow the AI chatbot to comprehend nuances, slang, and even typos in customer communication, helping keep interactions as smooth and natural as a conversation with a human agent. With advanced NLP, for example, an AI chatbot would likely be able to identify a similarity between the questions, “How do I change my billing address?” and “Where do I change my billing address?” and provide relevant information for both questions.
Ensure user-friendly chatbot design
The chatbot interface should be intuitive and straightforward. Aim for a clean, simple chatbot design, with easy-to-navigate menus and clear prompts. Remember, not all users are tech-savvy, so make the chatbot easy to use for everyone by mimicking a familiar chat experience like SMS or social media chat. This involves creating not just a streamlined chatbot UI, but also a user-friendly experience as described.
Personalize the chatbot interaction
Train your bot to tailor conversations based on the customer’s previous interactions, preferences, and purchase history.
Implement continuous learning and adaptation
A well-developed chatbot learns from each interaction. Conversational AI for customer service should draw on machine learning algorithms and can evolve and improve over time, adapting to customer feedback and changing behaviors.
Ensure seamless escalation to human customer support agents
Despite advances in conversational AI, situations will always require human intervention. Your chatbot should be able to seamlessly escalate complex or sensitive issues to human agents to avoid frustrated customers.
Feedback mechanism for improvement
Incorporate a feedback system where customers can rate their chatbot interactions and regularly update and refine the chatbot based on this feedback. If you deploy your chatbot on an app, consider sending an in-app notification to ask for feedback or programming the AI chatbot to ask customers directly.
Cultural and linguistic adaptability
If your business serves a diverse customer base, ensure your chatbot can handle multiple languages and cultural nuances, ensuring inclusivity and accessibility. Remember that poorly translated bot interactions can be hilarious at best and seriously damage your brand reputation at worst.
Proactive AI chatbot engagement
Instead of being purely reactive, a well-designed chatbot should also have the capability to initiate conversations, such as for offering assistance or recommendations, based on certain triggers or customer behaviors, at just the right moment.
Transparency and customer trust
Be clear with users that they’re interacting with a chatbot and give them easy access to terms. Demonstrating that the AI customer service chatbot adheres to privacy standards and data protection regulations is crucial for maintaining customer trust.
Integration with your technology stack
A chatbot should not exist in isolation. Integrating a chatbot with your CRM, sales, and marketing systems can provide a more holistic view of the customer and help inform larger business decisions.
By focusing on these key aspects, your business can design a chatbot customer experience that meets the functional requirements and enhances the overall customer experience.
But what does a well-implemented customer service chatbot look like? Let’s explore some examples.
Top 10 customer service chatbots you need to try
1. Zoom
Zoom’s chatbot, present both on its homepage and within customer accounts, assists users by immediately offering selectable options, or quick replies, based on their needs. This approach helps streamline the process of finding help or information and reduces the time to a resolution.
2. Cox
The chat icon on Cox's homepage offers a quick and easy way for consumers to engage with the company and receive fast answers or assistance. Its immediate accessibility and straightforward approach offer instant customer support as soon as visitors land on the website. The branding of the chatbot under the name Oliver also offers a customized, branded experience for Cox customers.
3. Notion
Notion’s chatbot for customers is called Q&A and is still in beta. It sources answers to user questions based on the information it can access - including all of the pages in a user’s space. It can also answer basic product questions while referring customers to the Help Center for more advanced product help. The integration of user content into the chatbot’s response system is a unique feature that offers more personalized assistance to their workspace.
5. T-Mobile
T-Mobile’s chatbot starts by segmenting requests between current and potential customers, streamlining the routing process. This streamlined approach ensures efficiency by quickly directing visitors to the most relevant resources.
6. Amazon
Amazon’s chatbot for logged in customers asks specific questions to understand the inquiry better, then offers two options for engaging with their support team. By pinpointing the nature of inquiries first, the e-commerce giant is able to provide more targeted and effective support.
7. Canva
Canva’s AI chatbot sets expectations upfront with a disclaimer about accuracy and terms of use, preparing users for the AI interaction. This preemptive communication helps manage user expectations and reduce potential misunderstandings or complaints.
8. Target
This big-box retailer offers a virtual assistant as one option for customer support. It begins by asking what the user needs help with, prioritizing assistance with Target orders, and includes a Terms of Use notice, similar to Canva.
9. PayPal
PayPal’s chatbot guides users on how to interact effectively, stating its limitations and offering common question options. The bot’s upfront guidance and honesty about its learning process help foster a user-friendly and transparent support experience.
10. Etsy
Like several of the other AI chatbot examples we’ve explored, Etsy’s bot clearly states its terms of use and then helps users by asking them to specify their needs based on two options: buying or selling on Etsy. This enables it to efficiently segment user requests, allowing for quicker and more relevant assistance.
Two key components in these examples are clear guidelines for users, and options for users to select from, helping ensure a more efficient AI chat experience.
4 quick tips on how to implement your own AI customer service chatbot
Introducing AI customer service chatbots requires careful planning and execution to be truly effective. Here are some tips to help with a smooth and successful implementation:
Integrate your customer service AI chatbot with existing channels
Make sure to integrate your chatbot software with your existing customer service channels. The chatbot should complement and enhance your current support system, not operate in isolation. This integration ensures a cohesive experience for customers, whether they interact with a human agent or a chatbot.
Familiarize staff with the AI chatbot
It’s equally important to train your staff to work alongside your customer service AI chatbot. Employees should understand how the chatbot operates, the types of queries it can handle, and when to take over from the chatbot. Build in clear rules for how to monitor and supervise the chatbot for customer service, as it should not operate without human oversight.
Set clear objectives and expectations for customer service AI
Define what you want to achieve with your customer service AI chatbot. What are your major goals and KPIs? For example, do you aim to reduce response times, handle common queries, or provide 24/7 support, or more? Align clear objectives with key performance indicators (KPIs) for gauging success, which will help guide the AI chatbot implementation process.
Focus on continuous AI chatbot improvement
A customer service chatbot isn’t a set-it-and-forget-it tool. Regularly analyze chatbot interactions to identify areas for improvement. Review customer feedback and chatbot performance data frequently to refine and update the chatbot’s responses and capabilities.
Remember, a chatbot is part of a larger customer service strategy, and its success should be measured by how well it enhances the overall customer experience.
Enhancing customer service AI chatbot efficiency through analytics
Upholding an optimal chatbot customer experience involves analyzing chatbot interactions and making data-driven improvements. Start by choosing which main metrics to track.
4 key customer service AI chatbot customer experience metrics to track
User satisfaction rate: A straightforward indicator of how well the customer service AI chatbot meets customer needs, this metric is gathered through direct feedback from users after their interaction with the chatbot. A high satisfaction rate means your chatbot is on the right track, while a lower rate signals a need for adjustments.
Resolution rate: This measures the percentage of queries resolved by the chatbot without escalating to a human agent. The higher the resolution rate, the more effective your chatbot is at handling customer inquiries independently.
Conversation abandonment rate: How often users disengage or drop out of conversations with the chatbot. A high abandonment rate could indicate confusion, frustration, or dissatisfaction with the chatbot’s responses.
Response time: The AI chatbot response time measures how long it takes for the chatbot to respond to user queries. Aim for fast response times to help ensure a positive user experience. Of course, the response time should be balanced with the quality and relevance of the response.
Remember that the most important thing about metrics is how you act on the insights they provide. Follow these 5 steps to fully leverage your data:
Identify patterns: Look for common trends or issues in interactions with the AI customer service bot. Are there specific points where users tend to drop off? Do certain questions lead to lower satisfaction rates? Adjust accordingly.
Gather user feedback: Your software should have a simple way to gather direct user feedback, providing insights into issues that metrics alone might not reveal.
Continuously test and optimize: Use A/B testing to try different approaches in the chatbot’s conversation flows. Analyze how these changes impact your key metrics and user satisfaction.
Update content regularly: Keep the chatbot’s content up-to-date and relevant. This includes not only the information it provides, but also the style and tone of its communication.
Train the AI: Use the data to train the chatbot’s AI engine, especially if it's built on machine learning algorithms. The more data it has, the better it can learn and adapt to user needs.
By regularly analyzing these metrics and making informed improvements, you can significantly enhance the performance and user satisfaction of your conversational AI for customer service.
Real-world success stories of customer service AI: How AI chatbots transform the customer experience
Customer service AI is transforming customer support across various industries. Let’s dive into two case studies of Sendbird customers that have found success with customer service chatbots.
Upwage
Upwage, a job posting platform, needed to make the recruitment process more efficient, particularly for screening hourly workers. The major hurdle was the enormous time it took for staff to screen numerous applications and conduct initial candidate assessments.
To address this challenge, Upwage partnered with Sendbird to integrate AI chatbots for candidate screening using Sendbird's SmartAssistant. With the included integration of ChatGPT's LLM by OpenAI, Upwage developed a responsive and scalable custom AI chatbot that automated the initial screening of job applicants, significantly accelerating the hiring process.
This partnership has greatly improved the team’s applicant screening process, leading to substantial time savings, improved efficiency, and positive user experiences. Upwage saved 50% of recruiters’ time for more nuanced work and achieved 3x concurrent applicant screening sessions.
As COO and Co-founder Greg Call explains, “Our success is measured by the hours saved for recruiters who no longer need to conduct screenings at scale manually. Some recruiters were spending four to eight hours on screenings daily. Now, Upwage AI Screener automates these screenings, enabling them to focus on more value-added tasks.” Read Upwage’s full story.
AllAthlete
AllAthlete, a sports recruitment app, struggled to facilitate effective communication between athletes, recruiters, and coaches. The app’s mission to democratize athletic recruitment relied heavily on maintaining active user engagement and efficient communication. Still, their initial AI chat solution wasn’t up to par — the chat solution deleted chats and changed users’ profile names without anyone knowing it.
To overcome these challenges, AllAthlete turned to Sendbird to build custom ChatGPT chatbots for a more reliable and user-friendly experience. With three different bots designed to guide users on platform usage, assist in athletic development, and bridge communication gaps between athletes and coaches, the technology has added a consistent, personal touch to the user experience.
With Sendbird, AllAthlete saw a 25% increase in user retention and saved 100+ hours with a more reliable, custom AI chatbot for athlete recruiting. Founder Greg Auerbach says, “We’ve been live with Sendbird for a few months and immediately, the solution's reliability has supported more quality conversations and a more trusted relationship with athletes that you just can’t put a price tag on.” Read AllAthlete’s full story.
5 ways the future of customer service AI will be transformed
The landscape of customer service is rapidly evolving, with AI customer service chatbots leading this transformation. Several trends and innovations in AI technology will continue to change how businesses interact with their customers:
Advanced AI personalization
In the future, conversational AI for customer service will likely offer even more personalized experiences. By leveraging data analytics and machine learning, chatbots will be able to understand customer preferences and behaviors in greater depth to tailor conversations and solutions to each individual’s unique needs and to a greater level of specificity.
Seamless human-AI collaboration
We expect to see a more integrated approach where chatbots and human agents work together. As the technology is still in its infancy, with more time to improve the process, AI chatbots will handle routine queries and gather preliminary information before seamlessly handing over more complex issues to human agents. This will take customer service AI to new heights.
Enhanced Natural Language Processing (NLP)
Future AI customer service chatbots will possess even more advanced NLP capabilities to understand and process human language with greater accuracy and nuance. This will make interactions with chatbots more natural and human-like, further reducing misunderstandings and improving overall communication. With sentiment analysis and an increasingly more significant understanding of human emotion, AI chatbots may be able to manage better customers who are frustrated or dissatisfied.
Predictive customer service
AI customer service chatbots will increasingly use predictive analytics to anticipate customer needs and address issues before they arise, helping enhance customer satisfaction and loyalty.
Integration with IoT devices
As the Internet of Things (IoT) expands, AI customer service chatbots could play a pivotal role in interfacing with smart devices, providing customers with an integrated service experience beyond traditional channels.
The future of AI chatbots in customer service will be focused on creating more meaningful, efficient, and personalized customer interactions in an efficient way that balances out the human component. As AI continues to evolve, we can expect conversational AI for customer service to become an even more integral part of the customer support ecosystem.
Start building a robust AI customer service chatbot today
Custom AI chatbots play a crucial role in customer service. As chatbot technology becomes more widespread, it is increasingly transforming the landscape of customer interactions, making them more efficient, personalized, and accessible. Integrating AI chatbots into customer service strategies offers brands several benefits, including 24/7 availability, handling large volumes of queries, consistency in responses, and significantly reduced operating costs.
Given this technology's fast-paced and ongoing evolution, the future holds even more potential for companies looking to make AI customer service chatbots an integral part of their business strategy. Advancements in natural language processing, sentiment analysis, and predictive analytics will further enhance generative AI chatbots' capabilities.
Sendbird is proud to lead the way in powering AI customer service chatbots that put the user experience first. Build a custom AI chatbot for free on your website in minutes, or check our customer support AI chatbot Salesforce integration. If you have questions, contact us! Our experts are always happy to help. Happy AI chatbot building!
