How do Chatbots Work? A Complete Guide For Customer Service

Chatbots have come a long way in recent years.

Thanks to advancements in bot design and AI customer service, these systems can yield significant efficiency gains for businesses and contact centres.

For consumers, chatbots serve as virtual advisors, transforming the digital customer experience with automated support and a new level of self-service.

It’s why chatbot usage has increased by a remarkable 92% since 2019.

But with automated service and AI constantly evolving, so too are customer expectations - meaning that the pressure on businesses to deliver an exceptional user experience is higher than ever.

With this in mind, it’s vital that businesses know their options when it comes to the various types of chatbot and how they work.

Not all chatbots are made equal.

Understanding the differences between them is essential if you want to find the right solution for your business needs and customer service goals.

Fortunately, we’ve put together a complete guide to help you break down the workings of chatbots and choose the best bot for your brand. We’ll cover:

• What is a customer service chatbot?
• What are the different types of chatbot, and how do they all work?
• Use cases for chatbots in customer support

TL;DR:

How do chatbots work? Different types explained...

1. Rule-based bots: Operate using predefined sets of rules and conditions, following a decision tree structure. These systems are useful for structured and repetitive tasks like answering FAQs due to their consistency and simplicity. However, they struggle with adaptability to handle unforeseen inputs and lack the ability to engage in natural human-like conversations.
2. Intent-based bots: Identify user intentions through techniques like Natural Language Processing (NLP). These models offer more flexibility and accuracy than rule-based systems and can scale to handle a variety of queries. But, despite their advancements, they still require significant technical expertise and can encounter challenges with linguistic nuances.
3. AI chatbots: Leverage advanced AI technologies like NLP/NLU, conversational AI, Generative AI, and Machine Learning algorithms. These bots excel in processing vast datasets, generating human-like responses, and adapting to novel situations. While they offer superior performance, they depend heavily on high-quality training data and face challenges such as bias and data privacy concerns.
4. Hybrid chatbots: Integrate rule-based, intent-based, and AI-driven techniques to capitalise on the strengths of each approach. This approach offers a versatile solution capable of handling both routine tasks and complex interactions seamlessly. While implementation can be complex, the flexibility and adaptability of hybrid bots make them the optimal choice for diverse business needs.

What is a chatbot?

A chatbot is a computer system designed to emulate human interactions, usually through a text-based messaging interface.

They work by generating relevant outputs in response to user inputs/messages, simulating a back-and-forth conversation.

In the context of digital customer service, chatbots are implemented to assist users with queries, support issues, self-service, and other tasks, effectively acting as virtual agents.

In fact, some studies have found they can automate up to 80% of queries independently - reducing support costs by around 30%.

And, in situations where human intervention is needed, customer service bots can also initiate handoffs to the most suitable team/agent. This makes them a great tool for chat routing as well as for automated support.

Chatbots can be deployed across multiple digital touchpoints, including your company website, app, and messaging channels.

They can be used by customers on various devices (i.e. computers, laptops, smartphones, etc.), often appearing as a chat widget in the right-hand bottom corner of the screen.

By automating a range of customer service interactions and tasks, chatbots can significantly improve efficiency and reduce the demand on your human agents.

How do chatbots work: Different types explained

How a chatbot works depends on its underlying technology, programming, design, and the specific tasks or functions it’s built to perform.

These factors determine the type of chatbot and the scope of its capabilities.

In this section, we’ll break down the different types of chatbot, how they work, and the strengths/limitations of each approach.

1. Rule-based (decision tree) models

Rule-based models represent the most basic type of chatbot architecture.

As their name suggests, they operate based on a predefined set of rules and conditions, otherwise known as a “decision tree”.

The decision tree of a rule-based chatbot functions as a hierarchical framework. Each node signifies a decision juncture, with branches leading to potential responses depending on user input or system variables. 

Throughout interactions, these bots work by identifying keywords and phrases within customer messages. These are evaluated against their pre-set parameters to find the most relevant response.

As a customer asks questions and responds to outputs, a rule-based chatbot will progress through its decision tree, responding in accordance with the predefined rules for each situation.

Despite their basic architecture, rule-based chatbots do have some advantages.

Firstly, their adherence to predefined rules means that their outputs are structured and consistent, ensuring accuracy within their designated scope. This makes them a useful tool for automating simple and repetitive tasks (e.g. answering FAQs).

Secondly, their decision-tree structure makes it easy to understand and amend the bot’s design, giving developers complete control over the bot’s outputs and functionality.

And finally, rule-based bots don’t need extensive datasets or intricate AI models to operate/set up. This means that they’re often the fastest and most cost-effective option in terms of development and deployment.

On the flip side, rule-based chatbots aren’t without their drawbacks.

One significant con is their inability to manage novel or unforeseen user inputs.

Since they rely solely on pre-set conditions, rule-based models lack the adaptability required for these situations, often leading to a frustrating user experience and inadequate responses.

This also means that scaling a rule-based bot beyond answering simple queries is incredibly challenging. 

As tasks become more complex and the range of potential inputs expands, managing the decision tree becomes increasingly difficult, necessitating frequent updates and adjustments to maintain functionality.

Additionally, rule-based chatbots lack the ability to comprehend and replicate natural human language, relying instead on pattern matching and rigid rules. 

As a result, interactions with a rule-based system often feel robotic and disconnected from human conversation, detracting from the user experience.

2. Intent-based models

Intent-based models are the second generation of chatbot technology, offering a more sophisticated system than rule-based architecture.

These chatbots work by identifying the underlying purpose or objective behind a user’s message and responding accordingly.

To achieve this functionality, intent-based chatbots typically employ techniques like natural language processing (NLP) and intent recognition.

A user’s intent represents what they want to achieve or inquire about, such as asking for information or seeking assistance with a particular task.

Once a customer sends a message, an intent-based chatbot will analyse the text to match the input with a pre-learned intent.

Based on this understanding, the chatbot formulates the appropriate predefined response or takes relevant actions to fulfil the user’s request.

By focusing on intents rather than exact keyword/phrase matching, intent-based chatbots offer more flexibility and accuracy than their rule-based counterparts.

Unlike rule-based bots, these systems can be scaled to handle a range of queries/tasks beyond basic FAQs. This is because they incorporate natural language processing technology, giving them the ability to process human language and varied user inputs.

Intent-based chatbots can also remember previous messages during interactions, allowing them to maintain contextual understanding throughout an entire customer conversation.

This means they can provide more coherent and relevant responses, creating a more fluid and conversational user experience.

That said, intent-based chatbots do have their pitfalls.

To start with, the development of these systems demands technical expertise plus a significant investment of time and effort - especially if your bot has to cope with very varied or complex intents.

And, even with the help of NLP, intents-based chatbots still struggle with linguistic nuances and novel inputs, leading to misinterpretations that can negatively impact the customer experience.

Moreover, the performance of intents-based chatbots depends heavily on the quality and diversity of the intents training. This can drastically limit their effectiveness if all the necessary scenarios aren’t included in the training.

3. Artificial intelligence (AI chatbots)

Innovations in artificial intelligence have given rise to a new generation of chatbots.

AI chatbots solutions work by incorporating various artificial intelligence technologies into their design and architecture, including:

• Conversational AI: Conversational AI is an umbrella term for a range of technologies that aim to facilitate natural communication between humans and computer systems. It includes various applications of AI, including NLP/NLU, Large Language Models (LLMs), and Machine Learning.
• NLP & NLU: Natural Language Processing & Natural Language Understanding techniques enable AI to analyse and comprehend human language by extracting relevant information and entities from user inputs.
• Machine Learning: AI chatbots utilise Machine Learning algorithms to continuously learn from interactions with users, adapting responses and improving their performance over time.
• GenAI & LLMs: Generative AI (GenAI) and Large Language Models (LLMs) allow AI chatbots to produce complex and contextually relevant outputs that are almost akin to what a human might say - even in scenarios with limited or ambiguous inputs.

Unlike rule or intents-based models, AI-powered systems can process vast datasets, generate human-like responses, adapt to novel situations, identify patterns, and improve their performance over time.

These capabilities mean that an AI chatbot can automate a huge range of interactions, queries, and tasks - effectively acting as an intelligent virtual assistant for your customers.

One of the ways they achieve this is through integration with your knowledge base - a repository of info that is stored within your chatbot platform. 

For instance, with Talkative’s GenAI chatbot, you can create multiple AI knowledge bases using web page URLs or file-based content.

From there, the bot can learn from your knowledge base and use generative AI to answer countless questions about your business, products, and services - using only the information in your pre-approved dataset.

The sophistication of artificial intelligence chatbots provides a number of benefits.

For instance, the fact that they can be trained using extensive datasets (i.e. knowledge bases) and Large Language Models (LLMs) means that they can generate highly accurate responses tailored to your business and branding.

What’s more, Natural Language Processing (NLP) and Generative AI systems excel at comprehending and mimicking natural human language.

This enables AI chatbots to engage in more human-like and coherent conversations, facilitating a user experience that surpasses rule-based models.

However, even AI chatbots have limitations.

These models still rely on the quality and quantity of information provided during training. Consequently, if the training dataset is biased or limited, it can negatively impact their performance.

Plus, the implementation of forward-thinking AI technology comes with certain challenges. Managing hallucinations, ensuring data privacy, effective knowledge management, and ongoing maintenance are crucial considerations for long-term success.

4. Hybrid chatbots

Hybrid chatbots work by integrating rule-based, intent-based, and AI-driven techniques to capitalise on the unique strengths of each approach.

The objective is to create a sophisticated and versatile system capable of managing a huge spectrum of interactions and use cases.

Hybrid chatbots usually employ pre-established rules or intents for specific functions while also integrating AI to enhance their adaptability, functionality, and comprehension of natural language.

For example, a hybrid system might answer FAQs using a decision tree framework but switch to AI when confronted with more intricate issues or queries.

The key benefit of this approach is that it combines the efficiency and consistency of rule/intent-based models with the intelligence and flexibility of AI, allowing you to get the best of both worlds.

This flexible architecture means that you can pick and choose elements from all types of bot design.

As a result, you can create a bespoke chatbot that’s tailored to your brand-specific use cases and capable of handling both routine tasks and complex interactions seamlessly.

The combined approach to design and programming also makes hybrid chatbots exceptionally adaptable tools, capable of scaling to meet diverse requirements and industry needs.

So, what’s the downside? Well, although a hybrid model is the optimal method for many businesses, the implementation process can be complex.

Building the initial architecture and orchestrating the collaboration between rule-based and AI-powered elements can be challenging.

Fortunately, you can work with a provider like Talkative to mitigate this disadvantage and ensure deployment success.

Use cases for a customer service AI chatbot

The adaptability of hybrid and AI-powered chatbots qualifies them for a broad spectrum of use cases across industries/sectors, for example:

• Automated customer service: Chatbots are most often deployed to automate customer support tasks and interactions 24/7, thereby reducing the workload on human agents. These bots answer customer queries, produce information, guide users through troubleshooting steps, and escalate complex issues to human agents when necessary.
• Retail & ecommerce: From personalised product recommendations to order tracking, chatbots in the retail sector can act as virtual shopping assistants. These ecommerce bots guide users through the checkout process, provide product information or styling advice, and even assist with in-store shopping queries (e.g. branch opening times, location-specific stock information, etc.)
• Insurance: Insurance chatbots act as virtual advisors for potential clients, policyholders, third parties, and brokers. These bots can assist users with things like claims processing, generating quotes, managing insurance plans, and facilitating insurance payments.
• Travel & hospitality: For the travel and hospitality industry, bots can serve as virtual travel agents or concierges, assisting with online bookings, reservation management, travel planning, complaints, and more. They can also provide real-time updates to travellers throughout their journeys, as well as destination information or advice.
• Internal agent training: In addition to engaging with customers, bots can also be used internally as an AI Agent Training Simulator. This allows you to pit your agents against chatbot simulations of various customer service interactions/scenarios. With this use case, your agents can practise communicating with customers, troubleshooting issues, and navigating difficult conversations - all within a controlled, risk-free environment.

The takeaway

Chatbots have evolved beyond being a basic customer service tool.

They’re now an essential CX solution for businesses that want to boost efficiency and embrace innovation.

Understanding how chatbots work and the technologies behind them will help you make an informed decision when choosing a chatbot solution for your brand.

From rule-based systems to AI-enhanced hybrid models, each approach comes with specific strengths that are suited to different business needs and customer requirements.

If you want an intelligent system that can learn over time and deliver the most advanced automated support in a humanised way - an AI-powered or hybrid chatbot is probably the best choice.

But, if you just want to decrease some of the demand on your contact centre in a cost-effective way, an intent or rule-based chatbot can be a good option.

Either way, Talkative has you covered.

With our scalable and flexible chatbot solution, you can:

• Choose between an intent/rule-based system, AI, or a combined approach (an AI chatbot with rule-based fall-back for maximum efficiency).
• Integrate our GenAI chatbot with your own AI knowledge base to create virtual assistants that are experts in your brand, products, and services.
• Meet and serve customers across your website, app, and messaging channels.
• Seamlessly escalate to human agents when needed.
• Leverage AI-driven analytics and reporting.
• Build multiple chatbots in-house (if you prefer to take the wheel with bot design).
• Automate customer-specific queries with chatbot fulfilment.

In addition to chatbots and AI solutions, we offer a suite of customer contact channels and capabilities - including live chat, web calling, video chat, messaging, and more.

Our solution also supports numerous integrations into other contact centre systems and CRMs. In fact, our Salesforce integration is one of the most in-depth on the market.

Want to learn more? Book a demo with Talkative today, and check out our interactive product tour.