Online Fashion Commerce: Modelling Customer Promise Date
This addresses a critical issue for e-commerce companies by improving customer experience and supply chain efficiency, though it is incremental as it builds on existing methods.
The paper tackles the problem of accurately predicting delivery dates in online fashion e-commerce to avoid lost sales and customer churn, proposing a machine learning approach with non-conventional loss functions that outperforms existing rule-based models and is deployed operationally.
In the e-commerce space, accurate prediction of delivery dates plays a major role in customer experience as well as in optimizing the supply chain operations. Predicting a date later than the actual delivery date might sometimes result in the customer not placing the order (lost sales) while promising a date earlier than the actual delivery date would lead to a bad customer experience and consequent customer churn. In this paper, we present a machine learning-based approach for penalizing incorrect predictions differently using non-conventional loss functions, while working under various uncertainties involved in making successful deliveries such as traffic disruptions, weather conditions, supply chain, and logistics. We examine statistical, deep learning, and conventional machine learning approaches, and we propose an approach that outperformed the pre-existing rule-based models. The proposed model is deployed internally for Fashion e-Commerce and is operational.