Year: 2025
Type: article
Abstract: In today's rapidly growing e-commerce market, many businesses face a major challenge: understanding a customer's behaviour and predicting its fulfilment based on various attributes. To address this challenge, we propose an approach using deep learning techniques. By leveraging advanced neural network architectures such as Artificial Neural Networks (ANN), Multi Layer Perceptrons (MLP), and Deep Neural Networks (DNN), we aim to uncover complex patterns and relationships within the dataset that traditional methods might overlook. This approach involves data collection, preprocessing data, correlation analysis, implementing and fine-tuning deep learning models, and assessing their performance using evaluating metrics. Among the implemented models, the DNN outperformed others, achieving 72.2% accuracy, 78.4% precision, 77.3% recall, and a 77.8% F1 score. The results demonstrate the DNN efficiency and accuracy of enhancing customer satisfaction in the e-commerce sector, ultimately fulfilling the products and driving sales growth.
Authors: Revelle Akshara, Kranthi Reddy S, Ajay Jain
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4411028197