Customer service chatbots are applications designed to support customers more quickly, efficiently, and comprehensively, while reducing operational costs.
A customer service chatbot is a software application that helps businesses handle customer inquiries automatically. It responds to questions, provides information, and assists with tasks like tracking orders, or troubleshooting service issues.
Chatbots are nothing new. Legacy customer service chatbots – programmed to follow pre-defined scripts to answer basic questions based on keywords – have been around for decades.
The latest generation of generative AI (GenAI) customer service chatbots combines conversational AI with Natural Language Processing (NLP) to interact with people more naturally. Such chatbots often operate within GenAI frameworks, like Retrieval-Augmented Generation (RAG), that integrate enterprise data into Large Language Model (LLM) prompts to enable more accurate and personalized responses. A RAG chatbot astride an LLM agent architecture improves over time by learning from past interactions.
Businesses integrate AI customer service chatbots into websites, mobile apps, and messaging platforms like WhatsApp or Facebook Messenger. Many also connect to Customer Relationship Management (CRM) systems, support ticketing tools, and knowledge bases to give a chatbot access to customer data to improve the accuracy and relevance of its responses.
GenAI-powered chatbots help companies serve more customers, better, without increasing support staff or compute overheads. They routinely handle more basic questions, reducing wait times and freeing up human agents to focus on more complex issues. While chatbots don’t completely replace human support, if implemented correctly they can improve efficiency, lower costs, and enhance customer satisfaction.
Customer service chatbots offer businesses a way to improve support while reducing costs. They enhance efficiency, provide instant responses, and help companies scale their customer service operations. Notably, customer service chatbots offer:
Customer service chatbots can be found in numerous industries, especially where generative AI data privacy is most stringent:
Prevalence of customer service chatbots by industry
Source: State of GenAI Data Readiness, K2view, 2024
Healthcare and pharmaceuticals
Healthcare providers use customer service chatbots for initial triage questions, insurance verification, and prescription refills. Patients use chatbots to schedule appointments, check symptoms, and access medical records.
Financial services
Financial services use chatbots for balance inquiries, fraud alerts, and loan applications. Clients use them for instant answers about their accounts and for real-time market information. LLM-powered autonomous agents can even proactively detect suspicious transactions and guide users through multi-stage security checks.
Retail and telecommunications
Retailers and telcos use customer service chatbots to answer product questions, process orders, and track shipments. A chatbot can suggest items based on browsing history, helping customers find what they need quickly. Many businesses also use them for handling returns and refunds.
Travel and hospitality
Airlines and hotels use chatbots to handle booking, itinerary changes, and customer inquiries. Travelers can access them 24/7 to receive real-time updates on flight delays, hotel check-in times, and local recommendations.
While customer service chatbots offer numerous benefits, they are not without their challenges. Some real-world incidents and research highlight how chatbots can go wrong, and how they can be improved:
In some cases, chatbots can misunderstand user inputs and deliver embarrassing or unhelpful responses. For instance, a Virgin Money chatbot mistakenly scolded a customer for using the word "virgin" in a query about merging accounts. The bank later apologized and acknowledged the need for improvements to its chatbots’ language processing capabilities.
There have been quite a few instances where chatbots produced content that was inappropriate or offensive. For example, an AI chatbot for a delivery service was coaxed into creating a poem that first insulted its own company and then used inappropriate language towards the customer.
Chatbots can struggle with complex or nuanced customer inquiries. One study examined how chatbots' language affects customer forgiveness after service failures. The research found that the type of language used in apologies impacts customer trust and satisfaction.
Excessive dependence on chatbots can lead to customer dissatisfaction, particularly when users are unable to reach human agents to resolve complex issues. A survey revealed that a significant number of customers express anger when they can’t speak to a real person.
Interactions with chatbots can sometimes be frustrating, especially when the chatbot fails to meet user expectations. Research has identified coping strategies users employ following chatbot-induced failures, including seeking support, active problem-solving, acceptance, or withdrawal from the interaction.
K2view GenAI Data Fusion takes customer service chatbots to the next level by giving them real-time access to accurate, up-to-date enterprise data.
When chatbots rely on the static information of an LLM, they can only deliver generic responses, that are often outdated. GenAI Data Fusion changes this by using Retrieval-Augmented Generation (RAG) to inject up-to-date data from multiple internal sources directly into the LLM prompt engineering process. By grounding GenAI-powered chatbot models with current, customer data, GenAI Data Fusion ensures that chatbots deliver answers that are protected, personalized, and precise.
By integrating K2view GenAI Data Fusion, businesses make their chatbots smarter, faster, and more dependable - leading to better customer experiences and more efficient support.
Explore how the K2view RAG tool, GenAI Data Fusion,
supports and optimizes customer service chatbots!