Since OpenAI announced the launch of ChatGPT in December 2022, the rapidly-advancing technology has dominated headlines globally.
Today, the technology is being used by businesses to assist with crucial tasks, from enterprise support and customer interaction to product development. Capable of generating human-sounding text, the tool is a powerful one for the next generation of chatbots and, by proxy, omnichannel customer communications.
Large language models like GPT-4 or Google’s LaMDA use Natural Language Processing (NLP) to understand and respond to human-generated text inputs in a conversational manner. A subfield of AI that focuses on enabling computers to process and understand human language, these models utilise NLP techniques to analyse and interpret the text input it receives – including tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, and language modelling. These NLP techniques help related tools understand the meaning, context, and intent behind the text input, allowing them to generate relevant, coherent responses in a conversational style.
With the field of NLP continuing to advance rapidly, the integration of GPT technology is propelling the next generation of chatbots to new heights. With their ability to understand and generate human-like text, GPT-powered chatbots are revolutionising customer interactions, virtual assistants, and other conversational applications.
Powering the future of chatbot technology
The way companies talk to their customers can significantly impact retention and business growth. Three-quarters of US consumers told a study conducted by customer communications company Intercom earlier this year that communication that makes them feel valued is a top factor in continuing to do business with a brand.
The study of 1,000 US consumers found that three in four say communication that makes them feel valued is a top or the most important factor when doing business with a brand. A total of 64% went further and reported they would leave a business if they didn't feel valued.
Furthermore, the study highlighted generational differences in the style and tone consumers want. Good customer support varies based on the person and situation, say the researchers, making it essential for businesses to deliver personalised communication grounded in context and a deep understanding of each individual customer.
As Colin Crowley, Senior Director of Customer Success at Freshworks, explains, the main purpose of a chatbot is to improve the productivity of customer-facing teams, increase customer satisfaction, and reduce the workload caused by live chat.
“It doesn’t matter how impressive a product is or how clean a website looks – if a customer can’t get simple queries answered, updates on orders or speedy resolutions to their issues, they will leave feeling frustrated and disappointed with the brand,” he asserts. “The old adage that it takes months to win a customer and seconds to lose them has never been more true in the digital age, where customers expect instant gratification and fast responses.”
AI driving a new generation of chatbots
The widespread interest in large language models like GPT-4 has created huge gains in interest around NLP and deep learning, according to O’Reilly’s annual Technology Trends for 2023 report, which examines the most sought-after technology topics.
The report shows that developer interest in generative AI is gaining momentum, with NLP being the most significant year-over-year growth among AI topics. In the world of NLP chatbots, one of the main roles that GPT tech is playing is improving the conversational quality and effectiveness of chatbot interactions. GPT-based chatbots can understand and respond to a wide range of queries and prompts from users, providing relevant and contextually appropriate responses. This has significantly enhanced the user experience, making chatbot interactions more human-like, engaging, and satisfying.
“The combination of chatbots and AI allows for far more advanced bots that can mimic human conversations, leveraging NLP, Natural Language Understanding (NLU), AI and ML technology to understand the intent of a query and offer solutions,” Crowley comments.
“Thanks to AI, chatbots are transforming from rule-based solutions to virtual agents with strong conversational intelligence,” adds Raffaele Ferrara, Product Marketing Manager at Odigo. “These solutions can handle even more complex use cases thanks to advanced NLU capabilities: handling multiple entities and intents in one utterance.
“They are also capable of handling all nuances related to human conversations – including interruptions and multiple requests – and retain short-term and long-term memory to make conversations productive.”
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