In the race to adopt Generative AI into chatbots, retailers are experiencing customer dissatisfaction with hallucinated answers; rules-based AI gives a solution. With the global retail expenditure on chatbots projected to reach $72 billion by 2028, the retail landscape has witnessed a significant shift since the advent of ChatGPT in 2022. Eager to embrace cutting-edge AI technology, retailers are striving to harness the full power of generative AI to provide round-the-clock customer service.
The implementation of ChatGPT and other LLM-backed chatbots seemed like a leap forward, yet it’s still plagued by inaccuracies, leaving retailers grappling with the predicament of ensuring customer satisfaction based on reliable information offered by virtual assistants.
So, how can retailers be certain that the information their chatbots provide keeps customers satisfied and above all, is accurate? This question underscores the pressing need for a solution that goes beyond mere efficiency and addresses the core issue of precise generative AI.
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Virtual assistants, despite their prowess, often stumble by generating unhelpful responses or even producing inaccurate information — a phenomenon referred to as “hallucinations”. This discrepancy between customer expectations and the chatbot's capabilities can erode brand loyalty. According to a 2023 Coveo Customer Service Report, it only takes up to three negative experiences for customers to leave a brand. In a world where customers demand real-time assistance and personalised experiences, the stakes couldn't be higher.
Customers seek interactions that mirror the nuance of getting customer service in a brick-and-mortar retail store. Due to the nature of a virtual customer service representative, it’s difficult to ensure customer queries will always be answered correctly. Even though these chatbots are being trained on a vast amount of data, they are often unable to make inferences —especially in the case of a customer asking questions that don’t have a direct preconceived answer. They lack the expert knowledge an expert from the business would bring. The key to make this happen lies in infusing these digital assistants with human-like reasoning, and this can be achieved through Knowledge Representation and Reasoning, or KRR for short.
KRR is a type of artificial intelligence that enables machines to think and reason like humans, based on the relevant business or expert knowledge, incorporated as logic in the system. With chatbots backed by KRR and in combination with an LLM, these virtual assistants are able to provide accurate responses that align with the nuanced nature of both human conversation and logical human reasoning — effectively bridging the gap between automated assistance and the personalised touch customers seek.
What we are finding is that knowledge graphs work more effectively to pull the data back to the prompt.
Implementing reasoning into chatbots, however, demands a robust foundation. As the only enterprise-grade knowledge graph with reasoning designed at the forefront based on University of Oxford research, RDFox makes real-time inferences and updates without compromising performance. RDFox’s reasoning capabilities transforms chatbots from mere responders into intuitive conversational partners, reducing friction for customers and strain on the retailer’s workforce — ensuring customer loyalty in the competitive landscape of retail.
In the face of scepticism surrounding ChatGPT-trained chatbots, a KRR chatbot is not just a beacon of hope; it’s a transformative force. With reasoning, customer queries are no longer ambiguous puzzles. In this digital age where customer loyalty is the ultimate currency, embracing knowledge representation and reasoning is more than an investment — it’s a strategic imperative for the future thinking retailer.
Discover the power of reasoning and rules-based AI virtual assistants and revolutionise your place in the retail landscape with RDFox. Try out RDFox for free or request a demo if you’d like to see RDFox in action.
The team behind Oxford Semantic Technologies started working on RDFox in 2011 at the Computer Science Department of the University of Oxford with the conviction that flexible and high-performance reasoning was a possibility for data-intensive applications without jeopardising the correctness of the results. RDFox is the first market-ready knowledge graph designed from the ground up with reasoning in mind. Oxford Semantic Technologies is a spin-out of the University of Oxford and is backed by leading investors including Samsung Venture Investment Corporation (SVIC), Oxford Sciences Enterprises (OSE) and Oxford University Innovation (OUI).
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