WHAT IS A CHATBOT?

A chatbot is software that simulates human-like conversation using predefined rules and knowledge bases. It allows businesses to create a first point of contact without requiring human interaction. Chatbots have become increasingly popular because they streamline entire departments while improving customer experience. The versatility of chatbots allows them to be applied to various industries, making them a feasible solution for any business looking to enhance its operations, increase its bottom line, and provide top-notch customer service.

There are four main types of chatbots on the market today:

Menu-based chatbots provide a one-click experience with predefined menu options. When a customer clicks on a menu option, it returns the information predefined within that option and nothing else. Menu-based chatbots operate best with straightforward question-answer tasks, such as FAQs, sales information, service information, and any other inquiry requiring limited responses. This results in any follow-up or advanced questions requiring human interaction. While including more menu options to cover more potential questions is possible, it tends to clutter the chat screen, which could result in a customer needing clarification and guidance.

Rule-based chatbots are similar to menu-based chatbots in that they contain predefined information. However, their native decision tree features allow for a more advanced conversation flow, allowing customers to ask questions and input more information to get their desired answers. This chatbot works using simple if-then logic, with more options at each level of the conversation. For example, a conversation could start with a customer choosing to ask about a specific clothing category; once that option is clicked, a few more options appear asking about the type of fabric; once the customer chooses the preferred material, more options appear asking about color, and so on until the customer ends with their preferred product in their cart. Utilizing decision trees allow a business to create an engaging experience and help guide customers to their select products and services.

Linguistic-based chatbots are a specialized subtype of rule-based chatbots that adhere to the same fundamental model as their parent and enable users to provide input. Using if-then logic, linguistic-based chatbots can process user input and match them to predefined word classes, which can contain a list of words, sentences, synonyms, and patterns/order of words. Each word class is then paired with an output the customer sees when the input matches that class. This chatbot offers customers increased flexibility to inquire about products and services, enabling them to achieve more precise and tailored results.

There are however limitations to consider; rule-based chatbots are not responsive beyond what is programmed, meaning if an option doesn’t exist or an input does not match any word class, the conversation could be cut short. It is therefore essential to optimize the chatbots’ options and knowledge base to consistently meet users’ needs, which businesses can accomplish with user feedback and conversation analytics.

ML-based chatbots have machine learning and AI capabilities; these are far more advanced and complex than a menu or rule-based chatbots, allowing businesses to create a top-notch personalized customer experience. With machine learning capabilities, chatbots can store customer conversations, allowing them to learn, optimize, and achieve better results over time. This lets businesses focus more on products and have the software handle the service.

What separates these chatbots most from menu or rule-based chatbots is the speed at which a customer can arrive at a desired result. This is because rule-based chatbots must follow a decision tree to arrive at a preferred answer. In contrast, an ML chatbot can have previous conversations saved, meaning it can skip any qualifying questions and arrive at a solution instantly. ML chatbots can also process open-ended queries, allowing customers to ask a broader range of questions and receive relevant responses. When appropriately trained, ML chatbots enable a flexible and versatile customer experience with limitless potential. 

A hybrid-based chatbot is a type of chatbot that combines the use of rule-based and machine-learning approaches to improve its performance. Rule-based chatbots rely on predefined rules and decision trees to generate responses to user input, while ML-based chatbots use algorithms and data to learn from user interactions and improve their responses over time. Hybrid-based chatbots leverage the strengths of both rule-based and ML-based approaches to create a more effective and accurate chatbot. For example, the chatbot may use a rule-based system to handle simple and common user queries while relying on machine learning to manage more complex and diverse questions. Combining these approaches allows hybrid-based chatbots to deliver a more personalized and engaging user experience.

HOW DO CHATBOTS WORK?

Chatbots interpret user input, a process that inputs and generate an appropriate response. The basic steps involved in the chatbot process are as follows:

• Input Interpretation: The chatbot first interprets the user input, which can involve identifying keywords or phrases, understanding the context of the conversation, and determining the user’s intent.
• Processing: Once the user input has been interpreted, the chatbot uses its underlying algorithms to process that input. This might involve searching a database for relevant information or calculating an appropriate response.
• Output Generation: The chatbot generates an output in response to the user input. Depending on the chatbot’s capabilities, this output can be text, voice, or even images.

Chatbot Architecture

The architecture of a chatbot can vary depending on the specific use case and technology involved. However, here are some standard components found in most chatbot architectures:

• Question and Answer System: The question and answer system answers frequently asked questions. It can be trained manually or using automated techniques, such as machine learning.
• Environment: The environment contextualizes user messages using natural language processing, involving an intent classifier and an entity extractor, which work together to understand the user’s intent and identify relevant keywords.
• Front-End Systems: The front-end system is where users interact with the chatbot. This can be through a website, mobile app, or messaging platform.
• Node Server/Traffic Server: The node server or traffic server is responsible for routing user requests to the appropriate components within the chatbot architecture.
• Custom Integrations: Custom integrations allow chatbots to be integrated with other systems, such as CRM databases or payment apps.

BENEFITS OF CHATBOTS

Improved customer service  → Chatbots can provide 24/7 customer support, handling real-time inquiries and complaints without human intervention. This improves customer satisfaction by providing instant responses and reducing wait times. Chatbots can also analyze customer feedback and behavior to improve overall customer experience.

Increased efficiency and productivity → Chatbots can automate repetitive and mundane tasks, such as answering frequently asked questions, scheduling appointments, and processing orders. This frees human resources to focus on more complex and high-value assignments, increasing productivity and operational efficiency.

Enhanced engagement and personalization → With customer data and machine learning algorithms, chatbots can provide users with personalized recommendations, promotions, and content. This creates a more engaging and relevant experience, increasing customer retention and loyalty.

Cost savings → Chatbots can reduce labor costs by handling a large volume of customer interactions without the need for additional staff. Additionally, chatbots can reduce operational costs by automating tasks that would otherwise require human intervention. Leading to significant cost savings for businesses, especially those with high customer support volumes.

Best Industry Use Cases

Chatbots have proven to be a valuable tool for businesses in various industries, providing efficient and personalized customer service, automating processes, and improving overall customer experiences. With advancements in artificial intelligence and natural language processing, chatbots will continue to evolve and become even more sophisticated, offering businesses new opportunities to improve their operations and engage with their customers. As the demand for quick and convenient interactions grows, chatbots will become an even more integral part of many industries.

Essential Chatbot Software Features To Look For:

• Dialog flow management: A good chatbot software should allow you to design, create and manage the flow of the conversation. This feature ensures that the chatbot can understand the context of the conversation and provide relevant responses to the user.
• No code visual flow builder: Look for a chatbot software provider that offers a no-code visual flow builder. This feature allows you to easily create and modify your chatbot’s conversation flow without needing any programming skills.
• Multilingual capabilities: If your business operates globally, it is essential that your chatbot can communicate with customers in different languages. Look for a provider that offers multilingual capabilities to reach a broader audience.
• Integration with third-party tools: Your chatbot should be able to integrate with third-party tools, such as customer relationship management (CRM) systems or marketing automation software, to streamline workflows and enhance the customer experience.
• Machine learning capabilities: A chatbot with machine learning capabilities can improve over time, learning from user interactions and becoming more intelligent and personalized in its responses. Look for a provider with machine learning capabilities to improve your chatbot’s performance continually.
• Human takeover option: While chatbots are becoming increasingly intelligent, there may be times when a human touch is necessary. Choose a provider that offers a human takeover option, allowing a human agent to take over the conversation if needed.

WHAT TO LOOK FOR IN A CHATBOT SOFTWARE PROVIDER