In my previous blog, I talked about what it takes to build a chatbot using GPT-4, including the engineering efforts required and a rough cost and time estimate. Although it is non-trivial to build a practical chatbot solution for real-world applications, with the right tools, one can build a custom chatbot solution quickly to take full advantage of the state-of-the-art large language models (LLMs) like GPT-4.
As indicated by this post, 84% consumers want to be treated as a unique individual not just a number, while 95% of companies saw 3X ROI from their personalization efforts. Now with the rise of conversational commerce, chatbots become a natural and private channel for brands to engage with users and offer personalized services in one-on-one conversations. In such a conversation, a chatbot can naturally elicit users' needs and wants and then provide personalized help or guidance based on the gathered information.
The Chatbot Tsunami has brought us a flood of chatbots to help automate various business functions, including customer service chatbots that automate customer Q&A, marketing research chatbots that automate customer interviews, and HR chatbots that automate job interviews. Although a chatbot is often made to serve one purpose, users expect the chatbot to perform multiple related functions while serving the main purpose.
As indicated by this Forbes article, 76% of customers contact businesses to make inquiries and get support via text messaging. Now with the required social distancing during the COVID-19 pandemic, more and more consumers choose to interact with brands online.
One of the most intuitive uses of chatbots is perhaps to automate customer service, where a chatbot automatically answers user questions, 24x7. In fact, in almost every chatbot application, a chatbot's ability to answer user questions, especially free-text questions, is always desirable.
