Customer service chatbots have evolved to include advanced NLP. The three evolutionary stages of chatbots include basic chatbots, chatbots, and generative AI.
Customer service chatbots often struggle to understand natural language, which can frustrate users. But Generative AI could make these chatbots smarter.
Humans have been conversing with computers since the 1960s, when Joseph Weizenbaum created ELIZA, the world’s first chatbot. Early chatbot technologies, which many contact centers still use, can answer simple questions with scripted responses, but lack real intelligence. However, advances in machine learning (ML) in the 2010s led to more advanced chatbots capable of understanding complex language, learning from past interactions and generate creative content.
Learn about the three iterations of chatbots: basic chatbots, chatbots, and generative AI chatbots – and how they can improve customer service.
Basic Chatbots
The first generation of chatbots began in 1966 with ELIZA by Joseph Weizenbaum. Later examples include ALICE (Artificial Linguistic Internet Computer Entity) and SmarterChild. These basic or rule-based chatbots use algorithms to detect keywords in user requests and provide predetermined responses based on them. Because these chatbots lack advanced natural language processing (NLP) capabilities, human language often confuses them.
A lot contact centers use these chatbots to help customers find answers to frequently asked questions. To avoid confusion, this technology can offer scripted input buttons to help guide user requests. For example, a customer service bot can provide customers with predefined options to choose from, such as “Change Password,” “Order Status,” and “Store Hours.” The chatbot then provides scripted responses based on user selections.
Chatbots
Advances in ML led to the rise of chatbots in the early 2010s. Chatbots use advanced NLP and ML capabilities to understand natural language more accurately than basic chatbots, and can learn from past interactions, understand voice commands, and complete tasks. Chatbots that serve individuals rather than teams, departments, or companies are called virtual assistants. Common examples include IBM Watson, Siri and Alexa.
Contact centers use chatbots to help both employees and customers. For example, conversational AI integrated into a pharmacy’s interactive voice response system can allow callers to use voice commands to resolve problems and complete tasks. These systems can also detect customer sentiment and forward calls to live agents if necessary. Additionally, some contact center software includes virtual assistants for agents who can offer real-time suggestions, schedule appointments, and retrieve information.
Generative AI Chatbots
In the late 2010s, advances in ML, such as transformative neural networks and large language models (LLM) — paved the way for generative AI chatbots, such as Jasper AI, ChatGPT, and Bard. These advancements in ML allow developers to train chatbots on massive data sets, helping them understand natural language better than previous chatbots. Additionally, this advanced technology can generate creative texts, such as poems, song lyrics, short stories and essays, in seconds.
In contact centers, agents can use them to summarize past customer interactions and draft email responses. Additionally, these chatbots offer human-like interactions, which can personalize customer self-service.
The release of ChatGPT in 2022 sparked a wave of interest in generative AI with technology providers, the general public and CX professionals. While simpler chatbots can handle basic customer service inquiries, generative AI chatbots could potentially help contact centers automate a greater percentage of customer service interactions.
