Chatbot or Conversational AI: Which Delivers Better CX?

Imagine trying to book a cab without an app or ordering your favorite meal without online delivery services. 

It feels almost impossible now, doesn’t it? 

That’s how technology has become part of our everyday routines.

Recent reports suggest that 72% of organizations are now interacting with their customers through digital means. And that is a huge number!

Many experts predict that artificial intelligence will power 95% of conversations happening between businesses and their customers.

But exactly what is the difference between Chatbot and conversational AI? 

Let’s dig in.

Chatbot vs Conversational AI: A Basic Overview 

Let us go through the difference between Chatbot and conversational AI with the help of a table.

What Are Chatbots : A Complete Guide

Think of chatbots as customer service representatives who follow a detailed script. They work on predetermined rules and give answers based on specific keywords or phrases you provide.

Imagine them as a helpful assistant who always sticks to a well-organized checklist. When you ask a chatbot a question, it scans your message for keywords and matches them to a response it’s been programmed with. This makes them perfect for handling common requests, like:

• “What’s your return policy?”
• “Can I change my shipping address?”
• “How do I reset my password?”

However, just like a clerk who is not well-equipped to handle extraordinary situations, chatbots struggle to function when presented with an unexpected question.

They have difficulty interpreting complex queries, ambiguous phrasing, or anything that falls outside their established script.

Advantages of Using Chatbots 

Round-the-clock support: Chatbots are always available ensuring your customers get access to support at any hour of the day. This constant availability not only enhances satisfaction but also helps to build long-term customer loyalty.

Automates routine tasks: By answering up to 80% of standard customer enquiries, chatbots allow your human agents to concentrate on more complex or valuable work, increasing overall productivity.

Instant answers: Chatbots can respond to consumer questions up to three times quicker than conventional techniques, cutting down on wait times and guaranteeing customers have the information they require promptly.

Enhanced contact centre efficiency: They help to optimize resources and enhance overall workflow by automating about one-third of contact centre operations, which increases the effectiveness of customer support operations.

Increased conversion rates: Chatbots have resulted in conversion rates of up to 70% in certain industries, greatly enhancing lead generation, sales, and revenue.

Cost and time efficiency: They help to save valuable time and money. With companies reporting a savings of 4 minutes per inquiry and a cost of just $0.70 per interaction, these tools deliver significant operational savings.

However, there are certain limitations that you can expect to face. So what are they? Let’s see. 

Misinterpreting the context:  Rule-based chatbots have trouble understanding the entire context of a conversation, which might result in responses that are inappropriate or irrelevant.

For instance: A chatbot might continue talking about the weather forecast if a user asks, "What's the weather like today?" and then, "Do I need an umbrella?"without considering that the second query relates to the need for an umbrella specifically.

Endless loops: A rule-based chatbot frequently responds with repetitive or irrelevant responses when it comes across a question that is outside of its programming, getting stuck in an endless loop of frustration.

Example: Asking a chatbot for advice on the latest tech trends may result in a repetitive “I don’t understand that request” response.

Limited ability to solve problems: Rule-based chatbots are excellent at answering simple, everyday queries, but they are unable to handle more complicated or multifaceted problems.

Example: A customer trying to resolve a complex billing dispute might receive generic responses from a chatbot that don’t address the specific issues, ultimately needing human intervention to resolve the matter.

Use Cases for Chatbots 

Rule-based chatbots excel in situations where:

• You need to handle repetitive tasks and frequently asked questions automatically.
• You want to simplify the process of gathering and qualifying leads.
• You’re seeking an affordable option for managing basic customer service inquiries.
• Maintaining efficiency and consistency is a key focus.

What Is A Conversational AI : A Complete Guide

Conversational AI refers to advanced technology that enables software to understand and respond to human conversations, whether spoken or written. It leverages four core technologies to function effectively:

• Machine learning (ML)
• Natural language processing (NLP)
• Natural language understanding (NLU)
• Natural language generation (NLG)

The capabilities of conversational AI include:

• Understanding the meaning of user input, even when it is presented in multiple ways or with different languages.

• Adjusting responses according to user history and retaining past conversations to personalise interactions.

• Evaluating user input and data to improve future encounters in order to adjust and improve over time.

Advantages of Using Conversational AI 

Managing complex queries: Conversational AI is more than just keyword detection. It deciphers the meaning of a customer's query using Natural Language Understanding (NLU). This implies that even in cases when the query is ambiguous or uncommon, it can manage complicated or open-ended enquiries and provide relevant answers.

For instance, conversational AI can recognise the particular context and deliver an appropriate policy response when a consumer asks, "Can I return this product even though it was a gift?" while a chatbot would overlook the nuances and produce a generic response.

Contextual understanding: Conversational AI systems, as opposed to rule-based chatbots, follow the entire conversation rather than just answering specific queries. By understanding the context of previous messages, it can offer more tailored responses and recommendations.

For example, if a customer inquires about the status of their order and then asks, "Can I change the shipping address?" the AI understands the context and provides the necessary information regarding the address change.

Adaptive learning: Conversational AI improves over time. With each interaction, the system learns to better understand user intent, interpret subtle nuances, and provide more accurate and helpful responses.

Example: A customer who frequently interacts with a banking AI might notice over time that it gets better at predicting needs, like suggesting transaction-related FAQs based on their previous inquiries, simplifying the experience.

Although conversational AI is very advanced and innovative, it is not always magic. 

Why? See how.

High reliance on large datasets: Conversational AI needs a huge amount of diverse training data in order to comprehend language nuances, context, and user intent. The process of collecting and organizing this data can be time-consuming and costly.

For instance, a conversational AI designed for the legal sector requires a comprehensive collection of case law, legal jargon, and client enquiries. Collecting and preparing this data can take considerable time and effort, slowing down deployment.

Bias potential: Conversational AI systems can pick up biases from the data they are trained on if they are not carefully managed. This could have a detrimental effect on their ability to make decisions.

Example: Based on skewed hiring data, an AI chatbot used for job recruiting can inadvertently give preference to some applicants over others, resulting in prejudice during the selection process.

Data privacy risks: As conversational AI processes sensitive personal data, there is always a risk of privacy violations or security breaches, especially in industries dealing with confidential information.

Example: A conversational AI tool used in a financial advisory service could expose sensitive client information if proper encryption and security protocols aren’t implemented. This can potentially lead to identity theft or legal repercussions.

Use Cases for Conversational AI

Conversational AI chatbots excel when:

• Delivering tailored and interactive experiences for customers.
• Addressing complex, detailed questions that go beyond simple queries.
• Building lasting relationships with customers and encouraging loyalty.
• Acting as a virtual assistant or concierge for added convenience.
• Collecting valuable, detailed feedback and insights from customers.

Which One to Choose for Your Business: The Final Verdict

Choosing between a chatbot and conversational AI depends on the complexity of your business needs and the level of interaction you desire to offer your customers.

A rule-based chatbot is an excellent option if the majority of the work your company does is repetitive, like setting up appointments or responding to commonly requested queries. It is perfect for answering common questions since it is affordable, simple to set up, and offers round-the-clock assistance.

However, if your business deals with intricate customer inquiries, requires a personalized touch, or needs to handle dynamic, multi-turn conversations, conversational AI is the better option. With its ability to understand context, learn over time, and offer human-like interactions, conversational AI can improve customer satisfaction and foster long-term loyalty.

Ultimately, your choice should align with your business goals, customer expectations, and the resources available for implementation. If your needs evolve, you can even combine both solutions to optimize customer support and engagement at various touchpoints.

Frequently Asked Questions

Which is the best AI chatbot for customer service?

The best AI chatbot for customer support is Zendesk AI, offering advanced features like natural language understanding, multi-channel integration, and personalized support to enhance customer experience and efficiency.

Do chatbots increase customer satisfaction?

Yes, chatbots can increase customer satisfaction by providing quick, 24/7 responses to common queries, reducing wait times, and automating routine tasks, allowing human agents to focus on more complex issues.

What is conversational AI in CX?

Conversational AI in customer experience (CX) refers to advanced technology that uses machine learning and natural language processing to enable personalized, human-like interactions, enhancing customer support and engagement across channels.

Author

Rutvik Boghara

Rutvik, the head of our Shopify department, is your go-to Shopify aficionado, turning complex ideas into smooth-running, scalable stores. With a passion for writing, he blends storytelling with tech to amp up your ecommerce game. When he’s not optimizing or creating content, he’s probably lost in a tech rabbit hole.