Leveraging customer learning and time-based targeting for fast fashion new arrivals

Abstract

The fast fashion industry rapidly introduces new products, making early adoption among fashion innovators crucial for sustaining return on investment and setting fashion trends for the diffusion process. This study aims to identify fashion innovators by assessing their learning maturity and to determine the optimal timing for commercializing fashion new arrivals. By leveraging large-scale e-commerce data, we operationalized learning maturity based on customers' past purchase experiences and employed survival analyses to examine how different types of customer learning affect new product adoption across various times of the day and days of the week. Our findings reveal that learning about product features increases customers' likelihood of adopting new products, whereas learning about pricing decreases it. These learning effects are most pronounced for customers shopping at bedtime and on weekends due to variations in consumers’ cognitive resources. By demonstrating the significant effects of customer learning, this research uncovers new, time-varying, experience-based antecedents of new product adoption. Our results provide novel insights into the success of new fashion products, offering readily actionable guidance on targeting the right customers at the right time.