Omnichannel, Big Data and a host of other retail trends have all received their fair share of attention over the years. To stay relevant to their customers, retailers have put innovative strategies in place.
They have worked tirelessly to learn more about their customers and what influences their buying habits, and have therefore gathered as much data as possible. It is now time to make the most of this data and turn it into insight as a service. Thanks to advances in artificial intelligence technologies, understanding what customers want is easier than ever.
What is artificial intelligence (AI)?
AI is divided into several subcategories and definitions. Subsets of this technology include machine learning, deep learning, neural networks and cognitive computing.
Data is the most crucial and “hardest to obtain” part of the equation, because that is where value is created. Retailers will be able to get closer to their customers by offering them better product ranges if they have the data needed to feed the algorithms.
What does artificial intelligence (AI) mean for retailers?
AI affects the very foundations of decision-making, for customers and businesses alike. Data-driven deep learning models can help AI understand what customers want.
A consumer can find the goods they want after reviewing all the criteria. Thanks to AI, they can simply point to a product and tell the algorithm they want something “exactly like that”. In the world of e-commerce, AI is the equivalent of what a sales assistant can offer a customer in a physical shop.
Conversion rates in physical shops depend on the offer, the timing of the purchase and, above all, on the ability and availability of sales staff to personalise the service for each consumer.
Nowadays, many shoppers start their search online before making a purchase in a physical shop. According to studies, customers who go through several stages when buying are more likely to make price-based choices. Yet the most important part of shopping is finding what they are looking for more quickly.
Customers expect more personalised, higher-quality suggestions, and they expect them quickly. Not only does AI bridge the gap between what brands offer and what people want to see, it does so at scale. Omnicommerce, marketing, merchandising and the supply chain are all areas where it can be used. It creates customer choice models that boost in-store sales and competitive pricing intelligence models that advise retailers on dynamic pricing strategies.
Data is all around us.
People counting, queue management and heat map systems, such as those offered by anavid, make collecting consumer data easier than ever.
However, analysing this data and predicting customer behaviour are even more crucial.
AI learns from previous behaviour and helps retailers improve what they offer their customers.
Depending on what they are looking for and when, a search for white shoes can mean two different things to two different people. When the same person searches for white trainers a month later, it is no longer the same thing. Every shopping scenario has a different context, and AI can give us that crucial understanding.
While global retail spending may rise year on year, customer preferences are changing faster, and AI models can help retailers keep up with these shifting trends. As a result, by the end of the year, retailers’ investment in AI will probably have tripled.