Retail, counting on a large amount of touchpoint with consumers, was among the first to understand the potential of using Artificial Intelligence to gain competitive advantage and increase business potential in this hyper-digitalized scenario.
The retailer, in fact, can use AI to implement solutions capable of satisfying the needs of buyers by elevating the retailer-consumer relationship in the long run by collecting personal data, information on in-store behaviour and buying habits. From real-time inventory management to the creation of targeted offers for specific types of customers, AI enables companies to respond quickly to changes in demand and provides the analytical, as well as operational, tools to satisfy the customer and improve business performance.
The centrality of the topic of AI in retail is evidenced by the data that emerged from these two surveys conducted by McKinsey and Capgemini among business executives in this sector. About two-thirds of them said they have accelerated the implementation of robotics, artificial intelligence and other emerging technologies, and by 2022 investments in the digitization of this sector will amount to $7.3 billion.
Many companies have begun to look at AI driven by the need to streamline key business processes, only realizing later that they must first work on optimizing certain preparatory aspects such as the automation of in-store logistics, the unique identification of products and the identification of new channels of data acquisition. The first step for retailers is, therefore, to adapt their business processes to global data storage standards to support digital transformation, a fundamental step in creating a correct link between the physical product and the information associated with it.
But, specifically, what applications of AI have enabled retail to respond to new consumer needs and improve business performance despite the challenging environment?
Here we go into more detail about 3 main trends in the adoption of AI-based tools in combination with standardized and structured data.
Analyze new demand patterns
In this sector, C-level have in AI an ally in demand analysis and forecasting to always be able to guarantee the availability of the required products, whether it is the physical store or the online store. As reported by Google in an article on Machine Learning applications in Retail, recommendation engines based on predictive analysis and image recognition are also able to analyze past actions and indicate to the user through the app, the location of a product on the shopping list or purchased periodically.
Optimize product pricing
AI apps for stores can help companies price their products, predicting sales and revenue outcomes based on different pricing choices. To do this, systems must collect information about substitute products, promotional activities, capture historical sales data, monitor competitor pricing, and obtain as much information about the factors that drive purchase choices as possible. In this way, executives can present products at the best price at all times and win new customers, thus creating a virtuous cycle. To align the price to the demand at all times with a strategy known as Dynamic Pricing (a concept that has been applied for years in e-commerce), it is necessary to make a series of structural changes, starting with the integration of pricing software with the checkout systems and the adoption of ELS (electronic shelves labels) that allow the price on the shelves to be changed directly at all times.
Redefine the relationship with the consumer
Each of us has dozens of loyalty cards in his wallet, mobile or smartwatch from the stores he usually visits, and he participates in point collections, offers and uses the digital services provided by the point of sale. All these operations generate thousands of data that allow retailers to have an increasingly detailed idea of the type of consumer they are dealing with, what they usually buy and when. In fact, with the support of Data Mining and Machine Learning technologies, the retailer can carry out specific analyses such as segment customers into homogeneous clusters according to purchasing characteristics and spending capacity or identify the CLV (Customer Lifetime Value), i.e. the value generated over time for the company to retain users with the highest potential.
Ultimately, emerging AI applications can play a strategic role in supporting retailers in making increasingly data-driven decisions, but the benefits of using these technologies will only be tangible if you have a database that is accurate, complete and able to be processed correctly by all systems and apps that are part of the company’s IT environment.
