Generative AI agents are becoming increasingly popular as they advance beyond traditional chatbots. 

In February 2024, Klarna shared that their AI agent, powered by OpenAI, handled two-thirds of their customer service chats in its first month. Klarna’s AI agent, available in 35 languages, engaged in 2.3 million conversations, matched human agents in customer satisfaction and cut resolution times from 11 to just 2 minutes. It also reduced repeat inquiries by 25%, doing the work of 700 full-time agents.

Similarly, McKinsey & Company conducted a study of 5,000 customer service agents using gen AI and found that issue resolution increased by 14% an hour, while time spent handling issues went down by 9%. 

This article will guide you through everything you need to know about customer service chatbots, including how to create one from scratch and the best platform for building them effortlessly. 

What Is a Customer Service Chatbot? 

A customer service chatbot is an AI-powered software designed to interact with customers and provide assistance through text or voice interfaces. Customer service chatbots can be used on various platforms, including websites, SMS, social media like Instagram, messaging apps like Telegram and Slack, voice assistants like Google Assistant, and more.

‍Will customer service chatbots replace human agents? The answer is no. Companies like Klarna emphasize that AI chatbots are meant to enhance human capabilities, not replace them. Chatbots can manage routine and repetitive tasks, freeing up human agents to handle more complex customer interactions.

How Do Customer Service Chatbots Work?

AI-powered customer service agents use machine learning techniques to understand and respond to customer queries: 

• Natural Language Processing (NLP): Chatbots use NLP to parse and understand human language input. This involves tokenization, part-of-speech tagging, and named entity recognition to break down the user’s message into meaningful components. 
• Intent Classification: Machine learning models, such as Support Vector Machines (SVM), or deep learning models such as Convolutional Neural Networks (CNN), classify the user’s intent to understand what the user wants.
• Entity Extraction: This process identifies specific information such as product names, dates, or account numbers. Named Entity Recognition (NER) models, based on methods like Conditional Random Fields (CRF) or Recurrent Neural Networks (RNN), extract relevant entities from the user’s input.
• Knowledge Base Integration: Vector space models or knowledge graph embeddings are used to efficiently search and retrieve relevant information from the company’s knowledge base. 
• Retrieval Augmented Generation (RAG): This technique combines document retrieval and generation models to produce more accurate and informative responses by fetching relevant documents and generating responses based on them. 
• Human-Like Responses: Advanced AI agents use Transformer-based models like GPT to generate responses that sound human-like.

The Future of Customer Service Automation: Chatbots vs. AI Agents

CNBC refers to AI agents as “what’s next after chatbots,” noting that they are “having their ChatGPT moment.” Grace Isford, a partner at Lux Capital, mentioned a “dramatic increase” in interest in “AI agents”. Here’s a quick table comparing chatbots and AI agents:

Benefits of Using Customer Service Chatbots

Businesses are increasingly investing in and implementing AI solutions for customer service. The combination of cost-effectiveness, improved efficiency, and enhanced customer experience makes AI agents an attractive solution for customer service strategies.

1. Cost Reduction: Gartner reports that chatbot interactions are significantly less expensive than human interaction in customer service. 
2. Improved Efficiency: IBM found that chatbots can answer up to 80% of routine questions, freeing up human agents for more complex issues. 
3. 24/7 Availability: AI agents can provide round-the-clock customer support, addressing consumers’ demand for always-on service.
4. Faster Response Times: AI-powered agents can respond to customer queries in seconds, compared to minutes or hours for human agents. 
5. Enhanced Customer Experience: Capgemini found that 63% of consumers are satisfied with chatbot-only interactions for simple queries. 

Examples of Successful Customer Service Chatbots 

Voiceflow’s Tico

Voiceflow’s AI customer support agent, Tico, successfully resolves 97% of support tickets. By integrating with a comprehensive knowledge base and utilizing advanced query handling through GPT-4, Tico provides quick and accurate responses, significantly reducing the need for human intervention. 

This automation not only enhances response times and customer satisfaction (CSAT) but also optimizes human agent productivity by allowing them to focus on more complex issues. Tico’s implementation has led to a 93% CSAT score and substantial cost savings.

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Roam’s AI Agent

Roam utilized Voiceflow to implement an AI chat agent, drastically improving their customer support efficiency. By automating Level 1 support and integrating a knowledge base, Roam saved over 30 hours of customer support per week. 

The AI agent handled common inquiries, reducing inbound calls and allowing the team to focus on more complex issues. Additionally, the AI provided accurate, comprehensive responses and enabled a better understanding of customer needs, significantly enhancing overall customer service.

How Do I Create a Customer Service Chatbot? 

Here’s how you can create a no-code customer service chatbot that’s powered by generative AI: 

1. Define Tasks: Determine what specific customer service tasks you would like the AI agent to handle and identify which channels you want to deploy the chatbot on (website, mobile app, social media such as Discord and Instagram, etc.)
2. Choose Your Chatbot Type: Decide between a rule-based chatbot or an AI-powered agent based on your needs. 
3. Pick a Platform: Select a chatbot development platform such as Voiceflow. This allows you to design, prototype, and publish the chatbot without any writing any codes. 
4. Design the Chatbot: Design the conversation flow, such as creating a personality and tone of voice for your chatbot and mapping out the typical conversation paths and decision trees. 
5. Develop the Chatbot: Develop and train your AI chatbot using your company’s knowledge base and FAQs. 

That’s it! You can now launch your chatbot on your chosen channels. Make sure to monitor its performance and get user feedback to improve it. Join 250,000+ teams to create a free Voiceflow account to build your customer service chatbot in 10 minutes! 

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Frequently Asked Questions

How do customer service AI agents compare to human agents?

AI agents are great for handling simple, repetitive tasks quickly and consistently. They can work 24/7 and manage large volumes of requests. On the other hand, human agents are better at dealing with complex issues that need empathy and critical thinking. AI frees up human agents to focus on these more challenging tasks.

How can chatbots be integrated with CRM systems?

Integrating chatbots with CRM systems is done through APIs. This lets the chatbot pull customer data, update records in real-time, and personalize responses. It makes the whole support process smoother and ensures that all customer information is up-to-date.

How can the ROI of customer service chatbots be evaluated? 

To evaluate the ROI of chatbots, look at metrics like cost savings, faster response times, and customer satisfaction. Check how many queries the bot handles without human help, the cost per interaction, and feedback from users. Comparing these numbers before and after the chatbot is implemented gives a clear ROI picture.

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Can chatbots handle complex customer queries? 

Chatbots can handle some complex questions, especially if they have a good knowledge base (KB) to draw from. Advanced chatbots use machine learning to understand and respond to more detailed questions. However, for very complex issues, it’s often best to transfer to a human agent.

What are some best practices for using chatbots in customer service?

Here are some best tips and tricks for developing a chatbot for customer service:

• Set Clear Goals: Know what tasks your chatbot should handle.
• Make It User-Friendly: Ensure the chatbot is easy to use.
• Keep Improving: Update the bot based on feedback.
• Smooth Transitions: Make it easy to switch to a human agent when needed.
• Personalize Responses: Use customer data for better interactions.
• Be Transparent: Let users know they’re chatting with a bot.

How do chatbots transition from automated responses to live agents?

Chatbots can switch to live agents when they hit a question they can’t handle. They pass the chat history to a human agent so the user doesn’t have to repeat themselves. This usually happens when the bot recognizes certain keywords or when a user asks to talk to a person.

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