| dc.description.abstract | During the pandemic, lockdown measures forced many stores to close, rapidly shifting consumer shopping from offline to online. This shift not only accelerated the development of e-commerce but also led to changes in consumer payment methods. Buy Now, Pay Later service emerged rapidly, offering consumers a way to defer or installment payments. This payment method does not require consumers to pay the full amount immediately, reducing their financial stress and increasing their willingness to purchase, especially in the economically uncertain post-pandemic era. Additionally, this payment method psychologically reduces the stress of payment at the time of purchase, thereby enhancing the shopping experience and consumer satisfaction.
Due to the rapid global growth in the usage of Buy Now, Pay Later service, it is crucial for e-commerce website to be able to accurately identify and predict potential users of this service and promote its use. By reducing payment transparency to increase impulse purchase, which can help increase customer transaction value and consumer satisfaction. Therefore, this study uses machine learning techniques to establish a predictive model for Buy Now, Pay Later consumers to provide e-commerce website with more effective promotion and precision marketing to increase service usage rates.
The study utilizes consumer transaction data from e-commerce website, including purchase frequency, amount, product preferences, and payment preferences. Given the class imbalance problem in the original dataset, the research employs six data imbalance techniques, such as Random Over Sampling, Random Under Sampling, SMOTE, ADASYN, ENN, and SMOTE+ENN, to improve the model prediction ability for minority samples. During the model building phase, six types of machine learning, including Logistic Regression, Random Forest, Gradient Boosting, KNN, Naive Bayes, and Neural Networks, along with 10-fold cross-validation methods to evaluate the results of each model. Comparing different data imbalance techniques and machine learning models, the model preprocessed with SMOTE+ENN and applied with Gradient Boosting performs best overall and in terms of stability. | en_US |