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

Yıl 2022, , 1493 - 1514, 30.09.2022

Kevser Şahinbaş

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

Öz

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.

Anahtar Kelimeler

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Abhirami, K., Pani, A. K., Manohar, M., & Kumar, P. (2021). An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 826-831). IEEE.
• Ahmed, S. R. (2004). Applications of data mining in retail business. International Conference on Information Technology: Coding and Computing (pp. 455-459). Las Vegas: IEEE.
• Amari, S. I., & Wu, S. (1999). Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6), 783-789.
• Ballestar, M. T., Grau-Carles, P., & Sainz, J. (2019). Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science, 13(3), 589-603.
• Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937-1967.
• Breiman, L. (2001). Random forests, Machine Learning , 45, 5–32.
• Bonaccorso, G. (2017). Machine Learning Algorithms, pp.167-170.
• Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
• Bradley, A.P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30 (7), 1145–1159. https://doi.org/10.1016/ S0031-3203(96)00142.2.
• Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, (pp. 785-794). San Francisco.
• Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., & Chen, K. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4), 1-4.
• Cortes, C. and Vapnik, V. (1995), Support-vector networks, Machine Learning, 20, 273-97.
• Dhaliwal, S. S., Nahid, A. A., & Abbas, R. (2018). Effective intrusion detection system using XGBoost. Information, 9(7), 149.
• Erkent, E. E. (2006). Elektronik Perakendecilik ve Elektronik Alışveriş. Ege Akademik Bakış, 10-16.
• Fritsch, S., Guenther, F., Wright, M.N., Suling, M., Mueller, S.M. (2019). neuralnet:Training of Neural Networks (Version 1.44.2).
• Gong, J. (2021). In-depth Data Mining Method of Network Shared Resources Based on K-means Clustering. 13th International Conference on Measuring Technology and Mechatronics Automation (pp. 694-698). Beihai: IEEE.
• Güllü, K., & Tarhan, M. (2021). Satış sonrası hizmetler ve tüketicilerin yeniden satın alma niyetleri arasındaki ilişkiye yönelik e-perakende sektöründe bir uygulama. Turkish Journal of Marketing, 192-205.
• Hamed, S., & El-Deeb, S. (2020). Cash on Delivery as a Determinant of E-Commerce Growth in Emerging Markets. Journal of Global Marketing, 242-265.
• Jiang, P., Zhu, K., Shang, S., Jin, W., Yu, W., Li, S., et al. (2022). Application of Artificial Neural Network in the Baking Process of Salmon. Journal of Food Quality, 1-12.
• KAYAKUŞ, M., & ÇEVİK, K. K. (2020). Estimation the Number of Visitor of E-Commerce Website by Artificial Neural Networks During Covid19 in Turkey. Electronic Turkish Studies, 615-631.
• KOÇAL, C. (2012). Uluslararası perakendecilikte rekabet stratejileri ve e-ticaretin önemi. Izmir, Turkey: DEÜ Sosyal Bilimleri Enstitüsü.
• Koehn, D., Lessmann, S., & Schaal, M. (2020). Predicting online shopping behaviour from clickstream data using deep learning. Expert Systems with Applications, 150, 113342, pp. 1-16.
• Liaw, A., Wiener, M. (2002). Classification and regression by randomForest. R News 2 (3), 18–22. [R News]. Retrieved from https://CRAN.R-project.org/doc/Rnews/.
• Liu, C. J., Huang, T. S., Ho, P. T., Huang, J. C., & Hsieh, C. T. (2020). Machine learning-based e-commerce platform repurchase customer prediction model. Plos one, 15(12), e0243105, pp. 1-15.
• Mauritsius, T., Alatas, S., Binsar, F., Jayadi, R., & Legowo, N. (2020, December). Promo abuse modeling in e-commerce using machine learning approach. In 2020 8th International Conference on Orange Technology (ICOT) (pp. 1-6). IEEE.
• Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chih-Chung, C., Chih-Chen, L. (2019). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien (Version 1.7-2). Retrieved from https://CRAN.R-project.org/package=e1071.
• Noor, A., & Islam, M. (2019). Sentiment Analysis for Women's E-commerce Reviews using Machine Learning Algorithms. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
• ÖZCAN, B., & TURNA, C. (2021). KARAR AĞAÇLARI İLE İNTERNET ALIŞVERİŞLERİNDE TÜKETİCİYİ ETKİLEYEN FAKTÖRLERİN ANALİZİ. JOURNAL OF BUSINESS IN THE DIGITAL AGE, 94-105.
• Öztemel, E. (2012). Yapay Sinir Ağları (Vol. 3). İstanbul: Papatya Yayıncılık Eğitim.
• Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of clinical epidemiology, 49(12), 1373-1379.
• Pondel, M., Wuczyński, M., Gryncewicz, W., Łysik, Ł., Hernes, M., Rot, A., & Kozina, A. (2021). Deep learning for customer churn prediction in e-commerce decision support. In Business Information Systems (pp. 3-12).
• Rai, S., Gupta, A., Anand, A., Trivedi, A., & Bhadauria, S. (2019). Demand prediction for e-commerce advertisements: A comparative study using state-of-the-art machine learning methods. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
• Romero Morales, D., Wang, J. (2010). Forecasting cancellation rates for services bookingrevenue management using data mining. Eur. J. Oper. Res. 202 (2), 554–562. https://doi.org/10.1016/j.ejor.2009.06.006.
• Singh, K., Booma, P. M., & Eaganathan, U. (2020). E-Commerce System for Sale Prediction Using Machine Learning Technique. In Journal of Physics: Conference Series (Vol. 1712, No. 1, p. 012042). IOP Publishing.
• Szabó, P., & Genge, B. (2020). Efficient conversion prediction in E-Commerce applications with unsupervised learning. In 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-6). IEEE.
• Vanneschi, L., Horn, D. M., Castelli, M., & Popovič, A. (2018). An artificial intelligence system for predicting customer default in e-commerce. Expert Systems with Applications, 104, 1-21.
• Vapnik, V. N. (1995). The nature of statistical learning theory, 2nd ed., Springer-Verlag New York, USA, pp. 1-279.
• Visa, S., Ramsay, B., Ralescu, A. L., & Van Der Knaap, E. (2011). Confusion matrix-based feature selection. MAICS, 710(1), 120-127.
• Yeung, W. L. (2014). Applications of data mining in online retailing: A case for mining prefix-ordered web site navigation paths. 2nd International Conference on Systems and Informatics (ICSAI 2014) Systems and Informatics (ICSAI) (pp. 943-947). Shanghai: IEEE.
• Yin, X., & Tao, X. (2021). Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning. Scientific Programming, 2021, pp. 1-9.
• Zhao, X. (2018). A Study on the Application of Big Data Mining in e-Commerce. IEEE 4th International Conference on Computer and Communications (pp. 1867-1871). Chengdu: IEEE.