Customer experiences and information discovery have evolved since the creation of the first search engine and simple keyword search. As modern computing gets faster, user expectations continue to accelerate. Speed, relevance, and customised experiences are now the baseline of customer expectations. An impressive 75% of customers say they’ll likely spend more on a business that gives them great service.
Today, we can interact with generative AI chatbots in natural language to find the answers we’re searching for. With the growing availability of AI assistants powered by large language models (LLMs), we’re moving from a place of manually searching for information to quickly getting answers. Rather than scrolling through pages of links to parse out an answer, we can get a single response in seconds in natural language. Generative AI analyses, correlates, and summarises content to provide—in an ideal scenario—relevant, accurate answers, all in the pursuit of new ways to meet customer expectations.
HOW GENERATIVE AI IS REDEFINING HOW WE SEARCH
By leveraging the power of large language models, neural networks, and machine learning (ML), generative AI learns the underlying structures, relationships, and patterns present in the data on which it is trained. Its predictive algorithms produce relevant outputs with impressive speed when queried.
Generative AI enhances search but search also enhances generative AI. By combining search with generative AI models, you can retrieve and combine proprietary data with public generative AI models (multiple LLMs and co-pilots) to ensure answers are contextually relevant. Through a method known as retrieval augmented generation (RAG) to “plug” proprietary data into a model, generative AI can produce accurate, relevant, and personalised results to users.
Generative AI’s natural language processing (NLP) capabilities also introduce a novel concept to information retrieval: conversational search. Generative AI can “understand” and generate human-like responses to user queries. Therein lies the revolution for users and customers alike: generative AI can turn search into intuitive user experiences.
CASE STUDIES: GENERATIVE AI IN ACTION
Already, organisations implementing generative AI are reaping its rewards. With generative AI, users can query an internal knowledge base, their own information, product pages, and more to get personalised answers, fast.
Search is key to improving customer experiences, not only for the customers but also for internal users. If customer service representatives have access to conversational search and can quickly pull up information relevant to the ticket—customer information, protocols when dealing with technical issues, previous procedures, and product information—the quality, speed, and efficiency of service improves. The effect of generative AI search trickles down from operations and security to the customer.
Generative AI-powered search tools give you new and exciting ways to build the trust and loyalty of your customers. Plus, generative AI’s ability to continuously learn and self-improve means organisations need only imagine the possibilities for improving customer experiences. Consider these use cases:
A home improvement website: A customer is searching for supplies to build a cat tree. Rather than search for products one by one, they could type “cat tree supplies” in a toolbar and get the list of products necessary for their build. This allows the customer to quickly find what they need and instils trust that the home improvement site is knowledgeable about the projects customers want to take on.
Home security systems: Imagine a customer trying to install a video doorbell. Things don’t go as planned and they end up with loose wires and no video. They could use the provider’s generative AI chatbot to get help by sending a picture of their setup. Within seconds, the chatbot offers them personalised troubleshooting advice. No need to input a clunky product number to read through FAQs or repeatedly explain the problem to multiple customer service representatives.
Telecom recommendation system: With generative AI, telecoms could provide product recommendations based on individual queries. Customers can ask questions related to their needs, such as “What phone has the best video-recording quality?” Generative AI can list the top five phones with the best picture quality based on the company’s existing product catalogue and descriptions.
Interactive digital manuals: Picture an interactive digital manual for automotive and manufacturing products that can help customers better understand how products work. For example, if a customer had recently purchased a specific vehicle, they could ask the manufacturer’s virtual product assistant how to change certain settings or fix any issues. This interactive experience can help customers find real-time answers on features, maintenance, troubleshooting, and more. Not only does this improve customer satisfaction, it can also reduce the number of customer support calls and tickets.
THE FUTURE OF GENERATIVE AI FOR CUSTOMER EXPERIENCES
Imagine planning a trip with a single query. You might ask an airline generative AI tool “Find me airline tickets during September from SYD to LHR under $X, red eye, window seat.” Instead of manually scrolling through a list of options without knowing which seats are available on those flights, you now get access to extremely specific information, within your parameters, at the speed of a single query. That’s the direction we’re heading with generative AI capabilities.
Generative AI’s ability to provide personalised, relevant, and fast answers to customer queries gives businesses a prime opportunity to enhance their customer experiences. All you need is the vision to consider how to implement it. As it becomes more commonplace, generative AI will likely become synonymous with excellent customer experiences.
