Product Mass Customization Strategy: How AI-Supported Customized Products Drive Profit and Customer Loyalty in Retail

Abstract

This thesis investigates the impact of product mass customization strategy in the retail industry, focusing on how Artificial Intelligence (AI)-driven big data collection enhances its effectiveness in driving profit and customer loyalty. Mass customization allows retailers to offer personalized products at scale while maintaining production efficiency. By integrating AI, businesses can streamline the customization process, improve customer experience, and optimize decision-making through predictive analytics. A mixed-method approach was used to explore the impact of AI-supported customization. A quantitative survey was conducted to assess purchasing behaviors, preferences, and barriers related to mass-customized products. Additionally, interviews with business experts specializing in AI and big data collection provided insights into the strategic implementation of AI in retail. The case study of Crocs was analyzed to demonstrate a successful example of AI-supported product customization, showcasing how the brand leveraged customization to enhance profitability and customer retention. The findings reveal that while consumers appreciate customized products, challenges such as pricing, delivery time, and return policies influence adoption rates. AI plays a crucial role in overcoming these challenges by improving efficiency, reducing operational costs, and enabling data-driven personalization. The study concludes with actionable recommendations for retailers, emphasizing the need to invest in AI-driven data collection, to refine customization interfaces, and to adopt cost-effective customization strategies to enhance customer engagement, loyalty, and long-term profitability.