Predicting Order Cancellations for E-Commerce Domain: A Proposed Model Based on Retailing Experience

Şahinbaş K.

İnsan ve Toplum Bilimleri Araştırmaları Dergisi, vol.11, no.3, pp.1493-1514, 2022 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 3
  • Publication Date: 2022
  • Journal Name: İnsan ve Toplum Bilimleri Araştırmaları Dergisi
  • Journal Indexes: ERIC (Education Resources Information Center), Index Islamicus, MLA - Modern Language Association Database, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1493-1514
  • Istanbul Medipol University Affiliated: Yes


E-Commerce technologies enable contact between businesses and their suppliers for the aim of exchanging information such as purchase orders, invoices, and payments thank to the rapid development in information technologies. E-Commerce has become a particularly important concept and has revolutionized the retail space. Understanding customer behavior patterns is key to gaining competitive advantage and achieving business goals. Predicting the probability of order cancellations has become a very urgent need as it causes loss of revenue for the retailer. When dealing with day-to-day operations such as order processing, tracking and order cancellations, finding enough time to grow the business is difficult. Cancellations are an important aspect of retail industry revenue management. In fact, little is known about the factors that cause customers to cancel or how to avoid them. The aim of this study is to propose a model that predicts the tendency to cancel an order and the parameters that affect the cancellation of the order. This solution can identify key factors that cause orders to be canceled by analyzing historical transaction data. A custom modeling application has been created that helps automate the process of tracking order cancellations in real time and predict the probability of an order being cancelled. For this purpose, machine learning techniques (ML) such as Artificial Neural Network, Support Vector Machine, Linear and Logistic Regression, XGBoost, Random Forest are applied to provide a tool for predicting order cancellations. The Random Forest algorithm achieves the best performance with 86% accuracy and 88% F1-Score compared to the other algorithm. This work will help firms manage their inventories well and strengthen their actions regarding customer behavior.