Harnessing generative AI for personalized E-commerce product descriptions: A framework and practical insights

Abstract

The role of electronic commerce (e-commerce) in the global economy has been steadily increasing, highlighting the benefits of business digitization for flexibility and resilience in response to environmental changes. Among emerging trends, the integration of artificial intelligence (AI) and machine learning is particularly notable, especially the application of large language models to personalize user interactions throughout the customer journey. A promising future direction is the use of generative AI to create customized e-commerce product descriptions for personalized, multivariant user interfaces. To validate this approach, a framework and metrics are proposed to assess the impact of segment-specific information on generated text. This led to the positioning of AI-generated content within a multivariant user interface architecture and the adaptation of a cosine similarity measure to evaluate text differentiation. The findings confirmed that the specific characteristics of e-commerce customer clusters enable generative AI to produce significantly distinct product descriptions. While differences were not statistically significant in 26.7% of cases, full differentiation was achieved for descriptions of sufficient length.