Disruption in the Chinese E-Commerce During COVID-19

TL;DR

This paper analyzes how COVID-19 disrupted Chinese e-commerce by examining behavioral changes, identifying affected product categories, and developing a demand prediction method that incorporates epidemic data for better preparedness.

Contribution

It introduces a novel demand prediction approach that integrates epidemic statistics and behavioral features, improving forecast accuracy for COVID-19 related products in China.

Findings

Behavioral patterns are highly responsive to epidemic development.

The proposed prediction method outperforms existing baselines.

Long-term and province-level forecasts are effectively extended.

Abstract

The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines its impact on the Chinese e-commerce market by analyzing behavioral changes seen from a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns seen in shopping actions are highly responsive to epidemic development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features for COVID-19 related products. Experiment results demonstrate that our predictions outperform existing baselines and further extend to the long-term and province-level forecasts. We discuss how our market analysis and prediction can help better prepare for future pandemics by gaining an extra time to launch preventive steps.