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Shopping cart abandonment and high return rates are large challenges in the e-commerce industry, resulting in lost revenue and increased costs. This master’s thesis investigates the underlying causes of these issues and proposes practical solutions to address them.

A combination of quantitative and qualitative research methodologies, including semi-structured interviews, where used to collect data. The Stimulus-Organism-Response (SOR) model and the design thinking process were used as theoretical frameworks to analyze consumer hesitation and behavior patterns.

Key findings reveal that shopping cart abandonment is primarily driven by uncertainties in size, unexpected costs, complicated checkout processes, and account creation requirements. High return rates are attributed to sizing issues and discrepancies between product expectations and reality, often due to inaccurate product descriptions. The study also identifies significant age and gender-related differences in online shopping behaviors.

To mitigate these challenges, the study proposes enhancing e-commerce usability, integrating advanced AI tools for personalized size recommendations, and improving customer service and support. Two prototypes were developed: a design prototype focused on being a visual concept for the user experience, and a machine learning model that provides size recommendations based on historical user data.

This research contributes valuable insights for online retailers seeking to optimize their e- commerce systems, reduce shopping cart abandonment and return rates, and ultimately enhance customer satisfaction and retention.