How to Build a Winning Customer Service Chatbot

Illustrator: Adan Augusto
how to build a customer service chatbot

Chatbots are popping up across the world wide web. That’s because, among other advantages, businesses are experiencing cost-saving benefits with this tool. A customer service chatbot, for instance, is an excellent complement to human agents.

But what exactly is a chatbot? 

You may know chatbots as those virtual assistant widgets at the bottom left (sometimes right) corner of your computer screen asking if you need any help. In customer service, chatbots answer customer queries using the knowledge base of your website. 

Check out Landbot’s in-app help chatbot:

Source: Landbot

The Help Center bot ensures customer support focuses on more challenging tickets by providing website visitors with the answers to their most common queries. For instance, it gives visitors details on pricing plans and even provides a demo of Landbot. If the visitor doesn’t find the answers to their questions, the bot routes them to the concerned department.   

As you can see, the benefits of a customer service chatbot are many. These include:

  • Effective customer data collection;
  • Reduces costs;
  • 24/7 availability across multiple channels;
  • Instant responses;
  • Increased customer service team productivity.

Here’s how to create a chatbot for effective customer service.

1. Pick Your Development Approach

There are three main development approaches to building chatbots for customer service: rule-based, AI or machine learning, or a hybrid approach.

Rule-based chatbots are the most basic type of chatbots. They follow predetermined rules to discover what the user wants. They’re similar to customer service telephone menus in that the chatbot guides the guests through the service to find the correct answer. 

Machine learning bots are more sophisticated. With AI capabilities, they learn to determine intent from the natural use of language. The more these chatbots interact with customers, the better they become at handling complex queries. For example, an AI restaurant chatbot can remember a customer’s order history. Rather than taking the guest through the entire ordering process, the bot simply asks if the customer is making a repeat order and proceeds onward.

This illustration highlights key differences between rule-based and AI chatbots

Source: GoodFirms

The advantage of a rule-based chatbot is that you can control the conversation. The downside is these chatbots don’t register customer requests outside the programmed rules. They also don’t have learning capabilities, so add any new developments to the chatbot manually.

The main advantage of AI chatbots is that they continuously improve and learn from each interaction they have with a human. They can understand typos and grammatical mistakes and feel more natural. The downside is that the learning process takes time, and you have less control over chat conversations.

The third development approach is a cross between rule-based and AI chatbots. Basically, the hybrid has rule-based tasks and can understand intent and context.

The development approach you choose will depend on the purpose of your customer service chatbot. Rule-based bots are a good choice for small companies with specific chatbot goals, like answering simple questions such as booking appointments. AI-powered chatbots are appropriate for large enterprises with multiple chatbot goals. You can choose the hybrid approach to get the best of both worlds, but it can be more challenging to build.

2. Set Down the Goals and Select Your Channel

You need to decide what exactly you want your chatbot to do. If you know what you want your chatbot to do from the get-go, you can craft a specific strategy that can help ensure your chatbot achieves that goal in the first place.

So, ask yourself, do you want your chatbot to provide first-level support, like answering basic questions? Or do you want it to classify customer issues and route them to the appropriate customer service representative? Or both?

Souce: Statista

According to Statista, many organizations leverage chatbot solutions to answer FAQ-oriented questions. That’s because it frees up almost 80% of agents' time to work on more complex customer issues.

In addition to setting goals for your chatbot, you should determine which channel is the most appropriate to host it. Chatbots can live on messaging apps (WhatsApp), websites, mobile apps, and social media networks (Twitter) — basically any channel that can host a text-based dialogue. Ultimately, the right channel will be dictated by where your customers are. For many companies, the right platform is their website and social media page.

3. Build Your Conversational Architecture

Most chat conversations with customers start with a greeting and a question. From there, the conversation varies depending on the chatbot’s purpose. Conversational architecture organizes or maps out the flow of communication. It’s the same for every chat conversation.

If we’re talking about AI-bases chatbots, there are seven components to their architecture:

  • Natural language processing (NLP): converts users’ language into machine language.
  • Natural language understanding (NLU): classifies the user’s intent and generates the most appropriate response.
  • Knowledge base: holds information the bots need to answer customers’ queries. For example, an eCommerce knowledge base would have product information.
  • Data storage: stores customer service chat conversations.
  • Dialogue manager: monitors the flow of conversation between the user and chatbot within the same chat session and decides how to respond.
  • Natural language generation: converts machine-generated data into human speech.
  • User interface: the physical interaction between the user and the chatbot.

Below is an illustration of the communication flow in an AI chatbot architecture:

Source: AI Multiple

The user types in a query, and the NLU reads to understand the user’s intent. The dialogue manager then kicks in to figure out the best response. 

In this scenario, the query is broad — there are probably multiple Chinese restaurants in the knowledge base. The chatbot responds to the request by asking the user to specify a location. The communication flow begins again when the user inputs the desired location.

4. Craft Your Bot's Answers

With your conversational architecture in place, you can begin to create chatbot scripts. Chatbot scripts are an outline of the words your bot will use during a conversation. You can design chat scripts to answer FAQs, route customers to the correct department, create tickets for complex issues, and more.

There are things to keep in mind when crafting the chatbot script:

  • Remember your goals: if the purpose of your customer service chatbot is for users to find how-to guides, make that clear.
  • Keep it brief: customers using chatbots are looking for quick response times and accurate answers. Avoid bombarding customers with lengthy text. 
  • Personalization: you’re writing to an actual person. Let your buyer personas guide you, and use the customer’s name or the second person pronoun.
  • Add small talk features: phrases like “Wanna hear a joke?” or  “Looks like it’s gonna rain” add a human touch to chat conversations and increase customer engagement. 
  • Brand voice: you’re probably already branding using social media. Like your social media posts, chatbots present an opportunity to express your brand personality. 

Check out this example:

Source: Voltage

Voltage expresses its friendly and approachable brand personality with a casual tone of voice and the use of emojis. It also feels like you’re talking to a human, with the second person pronoun and small talk phrases. 

Your bot’s answers should not only be helpful but engaging and arouse emotions as well.

5. Implement Your Dialogue Flow

Once you have conversation architecture and the bot’s answers in place, implement your dialogue flow. Dialogue or conversation flow is a visual representation of the steps a user will go through during a chatbot interaction. 

Here’s what one looks like:


Source: Freshworks

Use distinct colors and shapes to differentiate between the types of interactions that will appear in the chat conversation. For example, you can label potential user intents, chatbot actions, bot answers, customer replies, and call-to-action buttons. Also, use script variations for every possible conversation.

Doing this helps you visualize possible chats and their likely outcomes. Pay attention to customer interactions that need follow-ups or refer to previous dialogues to figure out how to handle them.

6. Use Chat Data

The key to a winning conversational customer service software is communicating in terms your customers already use. It eases chat interactions and improves customer satisfaction. Collecting chat data is essential to learn how your customers use language to make requests or ask questions.

The easiest way to learn your customer’s language is by reviewing chat history. These real-life examples are invaluable resources for creating content for your chatbot. If you don’t have access to chat history, you can mine customer support call transcripts and social media interactions to craft your chatbot responses. Mirroring your customers’ communication style helps build rapport and establish customer trust.

Chat data can also help provide a more personalized customer experience. With customer relationship management (CRM) integration, chat data can inform product and marketing team processes. For example, when marketers use email merge to send bulk messages, they can extract segmented chat data to target specific customers. 

7. Test it

Before going live, test your customer service chatbot to ensure effective and efficient service. Chatbot software simulates human conversation, so the main element you need to scrutinize is NLU, i.e., customer intent. If chatbots cannot understand human instruction, it will impair customer satisfaction.

AI chatbots specifically need to be trained and tested with multiple expressions and variations of user intent (words, phrases, syntax, spelling). You’ll want to engage a diverse group of testers from your target audience to assess and revise the structure and content of your dialogue flow. If your chatbot is multilingual-enabled, test this as well. The goal isn’t 100% accuracy, although it should be as close to that as possible. 

The intent classification of rule-based chatbots will need to be manually revised. With AI chatbots, you provide more expressions of intent. Other features you should test are usability, navigation, fallback (tests the bot’s response to irrelevant inputs), identification of the user’s tone of voice, and small talk.

8. Monitor the Performance and Make Changes

Testing your customer service chatbot doesn’t end when you launch the service. There is always room for improvement. Furthermore, human behavior and language are continually evolving. You should monitor and adjust your chatbot’s responses to match customer trends.

Use A/B or split testing to discover the best elements your target customers respond to. A/B testing shows users two versions of the same chat menu. The version that performs better is adopted. You can test chatbot elements like wording, images, fonts, welcome message, interface design, tone of voice, and more.

Collect feedback from users to see if your chatbot is helpful. End each conversation with a short survey or net promoter score (NPS) and make the appropriate changes. Chat testing tools like Selenium have Record and Play functionalities that allow you to experience a user’s journey. They give valuable insight into customer behavior on your chatbot application.


Customer service chatbots act as customer service agents for your organization. They’re changing the way customers and companies interact with each other. 

The benefits of using chatbots for customer service are many. Customers get the help they need when they need it, while customer service teams have more time to address complicated customer issues. 

When done well, a customer service chatbot helps businesses deliver what most customers want — a personal and efficient service — without compromising the quality of the customer support experience.

Are you up for more challenge? Take a deep dive in training your own AI FAQ chatbot.