Generative AI is rapidly transforming the retail industry, revolutionizing both the way consumers shop and how businesses operate. To remain competitive, major retailers like Albert Heijn must embrace and experiment with this emerging technology. While Generative AI is already gaining traction in enhancing online shopping, its potential for in-store applications remains largely untapped. As the future of retail increasingly shifts toward an omnichannel experience, and with Albert Heijn’s customers still primarily shopping in-store, the technology presents both new opportunities and heightened importance.

This thesis, in collaboration with Albert Heijn, explores how Generative AI can be leveraged to enhance the in-store customer experience through mobile technology. Specifically, it introduces a practical, AI-driven solution: the shelf label scanner. The foundation of this concept is grounded in comprehensive research. Identifying and understanding genuine customer frustrations were crucial in developing an effective technological solution. Through a mix of quantitative and qualitative research methods, five key in-store frustrations were identified: (1) difficulty locating products, (2) stress caused by product unavailability, (3) crowded aisles and long waiting times, (4) choice overload, and (5) the need for additional information and assistance with bonus products.
For this graduation project, particular attention was paid to the frustration of encountering an empty shelf, a problem that significantly impacts both the customer experience and the retailer. To design a solution that addresses this challenge, further research was conducted, recognizing that customer behavior and needs in these situations can vary widely. A survey with 400 respondents, employing an experimental design with four distinct scenarios, provided valuable insights. The results revealed how customers perceive and react to product unavailability, with findings showing that both the context and type of product strongly influence the support needed when faced with an empty shelf.

These insights were then translated into a solution. Through four design steps, including testing with real Albert Heijn customers, the final solution was developed. Integrated into the Albert Heijn app, this feature empowers customers to easily navigate out-of-stock situations. By simply scanning the shelf label, customers receive instant assistance, including information about when the product will be back in stock, alternative product suggestions available in the store and real-time inventory data from other Albert Heijn locations. This feature is designed to improve the in-store shopping experience by providing customers with timely, relevant information and helping them make informed decisions when confronted with an empty shelf.
In addition to the final user interface design, communication materials have been developed to effectively introduce this feature to customers. Lastly, the report outlines key implementation steps for a smooth and gradual rollout, along with several recommendations. These include an analysis of the shelf label scanner’s potential and an exploration of further opportunities to leverage Generative AI in addressing customer challenges within the company.