Retailers are faced with the need to continuously adapt to dynamic customer expectations while reducing operational inefficiencies in order to remain competitive. Data is an important key to meeting this challenge. But it is true: the central element of digital transformation in retail is the transformation of data into useful, actionable insights. Only they form the basis for effective measures and thus lead to improved business results.
Artificial intelligence (AI), including machine learning, plays a crucial role in generating such valuable insights. It opens up opportunities for retailers to increase sales and improve customer experiences, rapidly implement innovations and ultimately operate more efficiently. All of this forms the basis to positively differentiate from the competition. Therefore, it is important to integrate AI into one's own company in a meaningful way.
AI and computer vision at the heart of transformation
At the heart of the retail industry's transformation is AI as a powerful tool for redefining processes. A particularly important sub-area of AI is computer vision. This technology captures and interprets visual data, effectively acting as an extra set of eyes where needed. Its implementation enables intelligent product identification, which is a central element for interaction with the clientele. By combining AI and computer vision, retailers can gain valuable insights and create more efficient, personalized and engaging customer experiences. Computer vision is used in, among other things:
Shop surveillance and theft detection: With the help of computer vision, cameras installed in the shop can detect movement patterns and identify suspicious activity. AI algorithms can then be used to learn such patterns and implement more effective theft prevention measures.
Automated check-out: Amazon Go – a business model based on AI and computer vision to enable a fully automated shopping experience – is a well-known example of the use of AI and computer vision. Customers simply take the items they want and leave the shop, while computer vision and AI recognise, track and automatically charge for the selected items.
Inventory management: Computer vision can help monitor inventory in real time to communicate accurate stock levels. AI can use this information to make predictions about future demand and optimize inventory planning.
Personalized advertising: Digital billboards can be equipped with computer vision to recognise the demographic characteristics of people viewing them – such as age and gender. With AI, this data can then be used to deliver personalized ads in real time.
Improving the customer experience: AI and computer vision can also help shops improve the customer experience, for example through interactive mirrors in fitting rooms that suggest accessories or similar items, or systems that help customers find items in the shop.
While computer vision interprets visual data, AI goes further: machine learning – another crucial component of AI – uses algorithms to learn from large amounts of data, make predictions and increase operational efficiency.
Machine learning to increase operational efficiency
Through big data analytics, offers can be adjusted in real time, which in turn helps to increase operational efficiency. In addition, machine learning plays a crucial role in optimizing warehouse management, highlighting the importance of AI throughout the operational process. Machine learning and AI have already been identified as key trends within the retail industry in previous years. The current staff shortage in many shops is accelerating this development and increasing the pressure on companies to automate processes in order to remain competitive.
CRM software and predictive analytics
Many retailers are already integrating AI into various operations. CRM software that uses AI to automate marketing activities or the use of predictive analytics to forecast customer buying patterns are just two of many examples. The cloud plays a central role in this by supporting data storage and processing from multiple sources. This makes cloud computing ideal for tasks that require high computing power and storage and do not require immediate response. In this context, demand forecasting using machine learning or online product recommendations are typical cloud workloads in retail.
Edge computing: data processing directly on site
Implementing AI directly in shop operations, in turn, brings other additional benefits. Edge computing in retail acts as a catalyst for information and transforms extensive raw data into valuable, actionable insights. Edge computing acts like a smart shop assistant that works directly on site, at the edge of the network. This assistant can process information directly where it is generated – in the shop itself – instead of sending it to a distant server first. Examples: Digital signage that dynamically adapts to the target group, real-time analysis of camera or sensor data to see which products customers pick up most often, or sensors that analyze customer movement patterns to identify cross-selling and upselling opportunities. Processing data "at the edge of the network" can reduce latency, lower bandwidth consumption and improve data protection. This is particularly useful for real-time applications.