E-Ticaret Alanı İçin Sipariş İptallerini Tahmin Etme: Perakendecilik Deneyimine Dayalı Önerilen Bir Model

Year 2022, Volume: 11 Issue: 3, 1493 - 1514, 30.09.2022

Kevser Şahinbaş

https://doi.org/10.15869/itobiad.1127578

Abstract

E-Ticaret teknolojileri, bilgi teknolojilerindeki hızlı gelişme sayesinde, işletmelerin satın alma siparişleri, faturalar, ödemeler gibi bilgi alışverişi amacıyla tedarikçileri ile iletişim kurmasını sağlamaktadır. E-Ticaret özellikle önemli bir kavram haline gelmiştir ve perakende alanında devrim yaratmıştır. Müşteri davranış kalıplarını anlamak, rekabet avantajı elde etmenin ve iş hedeflerine ulaşmanın anahtarıdır. Perakendeci için gelir kaybına neden olduğu için sipariş iptallerinin olasılığını tahmin etmek çok acil bir ihtiyaç haline gelmiştir. Sipariş işleme, takip ve sipariş iptalleri gibi günlük işlemlerle uğraşırken, işi büyütmek için yeterli zaman bulmak zordur. İptaller, perakende sektörü gelir yönetiminin önemli bir yönüdür. Aslında, müşterilerin iptal etmesine neden olan faktörler veya bunlardan nasıl kaçınılacağı hakkında çok az şey bilinmektedir. Bu çalışmanın amacı, bir siparişi iptal etme eğilimini ve siparişin iptalini etkileyen parametreleri tahmin eden bir model önermektir. Bu çözüm, geçmiş işlem verilerini analiz ederek siparişlerin iptal edilmesine neden olan temel faktörleri belirleyebilir. Sipariş iptallerini gerçek zamanlı olarak izleme sürecini otomatikleştirmeye ve bir siparişin iptal edilme olasılığını tahmin etmeye yardımcı olan özel bir modelleme uygulaması oluşturulmuştur. Bu amaçla Yapay Sinir Ağı, Destek Vektör Makinesi, Doğrusal ve Lojistik Regresyon, XGBoost, Rastgele Orman gibi makine öğrenme teknikleri uygulanarak sipariş iptallerini tahmin etme aracı sağlanmıştır. Rastgele Orman algoritması diğer algoritmaya göre %86 doğruluk oranı ve %88 F1-Score ile en iyi performansı elde etmektedir. Bu çalışma, firmaların envanterlerini iyi yönetmelerine ve müşteri davranışlarıyla ilgili eylemlerini güçlendirmelerine yardımcı olacaktır.

Keywords

E-Ticaret İptalinde Sınıflandırma, Pazarlama Stratejileri, Veri Yönetimi, YSA, SVM, XGBoost, Lojistik Regresyon, Parametre Ayarlaması, Nitelik Önemi

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

Abstract

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.

Keywords

Classification in E-Commerce Cancellation, marketing strategies, data management, ANN, SVM, XGBoost, Logistic Regression., Parameter Tuning, Feature Importance

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