Online Fashion Commerce: Modelling Customer Promise Date

TL;DR

This paper introduces a machine learning approach for accurately predicting customer promise dates in online fashion commerce, addressing uncertainties like traffic and weather to improve customer satisfaction and supply chain efficiency.

Contribution

It proposes a novel loss function-based machine learning model that outperforms existing rule-based methods for delivery date prediction in e-commerce.

Findings

The model reduces prediction errors compared to rule-based systems.

It effectively handles uncertainties like traffic and weather disruptions.

The approach is successfully deployed in a real-world fashion e-commerce setting.

Abstract

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.