Size recommender: a data-driven approach to fashion sizing
Online shopping has transformed fashion retail, offering convenience but posing challenges. At the forefront of it is the persistent problem of size returns, stemming from the limitations of online shopping where customers are unable to try before they buy.
We would like to introduce a data-driven size recommender to address this dilemma and enhance not only the cost efficiency but also customer satisfaction. By leveraging historical return data, we analyzed and labeled articles, discerning size patterns, and then employed machine learning to predict size labels. The performance was validated through rigorous A/B testing, and it showed remarkable success by a promising reduction in returns.
Vorkenntnisse
A general understanding of data science, machine learning, and data-driven solutions in the context of retail would enable attendees to fully comprehend the content.
Lernziele
In this presentation, we delve into the journey of developing and implementing our data-driven size recommender. From the initial data analysis and labeling to the application of machine learning techniques for prediction, we outline a comprehensive process to improve the online shopping experience.