Contact Centre Chatbot

In 2024, Contact Centre Chatbots, also known as Conversational Bots, have become the new standard in the field of customer engagement


Contact Centre Chatbot

Contact Centre Chatbot or Customer Service Chatbots, also known as Conversational Bots, have become the new standard in the field of customer engagement, and by now, it’s safe to assume that almost everyone with internet access has chatted with one at least once.  A study by Juniper Research estimates that by 2024, chatbots will save businesses up to 2.5 billion hours of work; and over the last few years, chatbots responded to 85% of customer service interactions. And while until now chatbots have been mostly used to address routine or simple queries, the recent and rapid developments in Artificial Intelligence have opened a plethora of possibilities. Every month, Conversational AI progresses by leaps and bounds, and today we seem closer than ever to witnessing a chatbot being able to pass the Turing test.


However, as any customer and customer engagement professional knows, not all chatbots are equal: some of them are equipped with AI while others are not; some are better at answering certain types of questions than others; and ultimately, some might be a better fit for certain businesses or customer service operations than others.


In this article, we’ll clarify everything you might be wondering about the question of the Contact Centre Chatbot: its significance, what different types of Customer Service Chatbot there are, the role of AI in the development of chatbots, and what the future holds for customer service Conversational AI.


But first, let’s get reminded of the basics: what is exactly a contact centre chatbot?


What is a Contact Centre Chatbot?

Essentially, a chatbot is a computer program that emulates human conversation, accommodating both written and spoken interactions. Serving as an interface between individuals and digital devices, chatbots come in diverse forms—from basic programs providing quick responses to complex digital assistants. Chatbots leveraging Artificial Intelligence can learn and evolve as they have more conversations, delivering personalised interactions based on accumulated information and creating dynamic and tailored conversational experiences for users.


Naturally, customer service departments and contact centres have found a massive edge in this technology: a Contact Centre Chatbot is able to not only reply to common questions automatically, freeing up agents’ time to focus on high-value interactions, but it’s also available 24/7. However, there are many more applications for Contact Centre Chatbots beyond customer service, from appointment scheduling to lead generation or customer feedback collection. 


Contact Centre Chatbots have become an essential part of today's customer engagement landscape
Contact Centre Chatbots have become an essential part of today’s customer engagement landscape


However, a chatbot’s proficiency in certain tasks will always depend on its level of technological sophistication, on whether it uses AI or not, and on the algorithms backing its performance. As we have said earlier, not all chatbots are alike, and it’s crucial to know the differences between them before implementing one in your business.


In the next section, we’ll go through the different types of chatbots you can find and what differentiates them.


Contact Centre Chatbot: Different Types

Stateless vs Stateful Contact Centre Chatbots

Chatbots can be categorised as either stateless or stateful based on how they handle information and context during user interactions. 


Stateless contact centre chatbots consider each user prompt as a new and independent event. They don’t store any details about the user or the context of past conversations. Consequently, each response is generated from scratch, depending solely on the immediate input. Stateless chatbots are apt for handling uncomplicated queries where it is not essential to maintain context across interactions.


On the other hand, stateful chatbots maintain a record of user details and the ongoing conversation context throughout their interactions, creating a conversational state that allows them to reference previous user inputs. This enables stateful chatbots to provide a more personalised and context-aware conversational experience. They excel in handling more complex and continuous conversations, offering a seamless and engaging user experience.

Scripted or Quick Reply Chatbots

Scripted contact centre chatbots are the most basic type, functioning as a hierarchical decision tree. They engage users through predetermined questions, guiding the interaction until arriving at an answer to the user’s query. Menu-based chatbots, where users are prompted to choose from a predefined list or menu, are also similar.


Keyword Recognition-based Chatbots

Contact centre chatbots using keyword recognition seek to grasp user input and formulate responses by identifying keywords within customer replies. These bots combine customizable keywords with Artificial Intelligence to generate suitable responses. Yet, if the AI component lacks sophistication, these chatbots might encounter difficulties when faced with repetitive keyword usage or redundant questions.


Contextual Contact Centre Chatbots

Contextual chatbots, surpassing their less advanced counterparts when it comes to the quality and efficiency of interactions with users, rely heavily on the processing and interpretation of large amounts of conversational data. 


Utilising artificial intelligence (AI) and machine learning (ML), these bots store and remember user interactions, evolving and enhancing their performance gradually. Instead of depending on specific keywords, they analyse the subtleties in customer inquiries and phrasing, autonomously delivering responses and thereby improving their overall capabilities.


Our Ahena AI Conversational Bot is an example of a contextual contact centre chatbot and stands as a prime illustration of AI’s capabilities. It is specially designed to provide detailed responses and personable responses to customer service queries, with the capacity to fine-tune its training according to domain-specific datasets and learn from past interactions; this allows Athena AI to improve its proficiency at dealing with specialised industry queries.


Deployable on multiple channels, such as websites, social media, and WhatsApp, it facilitates conversations with customers and propels customer journeys forward.


Voice-Enabled Chatbots

Voice-enabled contact centre chatbots are sophisticated conversational interfaces designed to understand and respond to spoken language. Unlike their traditional text-based counterparts, these systems utilise automatic speech recognition (ASR) technology to transcribe spoken words into text, facilitating seamless communication through voice interactions.


These chatbots make use of natural language processing (NLP) and machine learning algorithms to grasp user intent, allowing them to interpret complex spoken queries and provide responses that are contextually relevant. This technological advancement significantly enhances the user experience by providing a hands-free and intuitive means of communication. It proves particularly valuable in applications like virtual assistants, customer service hotlines, and voice-activated systems, where users can easily engage with the bot by speaking naturally.


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