Applying Machine Learning To Predict Online Customers Behaviour
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Applying Machine Learning to Predict Online Customers Behaviour
The way of learning continues to develop and gradually changes the way people buy such as online learning sites. This has implications for the strategies used for marketing in order to increase users on their platform. As these changes change, predicting consumer behavior and choices is becoming a topic of interest to researchers and companies alike. Predicting consumer behavior patterns has been conventionally proven, especially among sales, to increase business growth and generate customer loyalty. The study analyzed customer behavior patterns on paid online learning sites using Google Data Analytics, while in February 2022, a digital report by Hootsuite recorded at least 44% of Internet usage in Indonesia for E-learning needs. The use of Machine Learning is increasingly glimpsed to provide convenience in various aspects of work. We use preprocessing oversampling and feature selection steps to improve classifier performance and scalability. In the case of this paper, the authors used Decision Tree, Random Forest, XG Boosting, KNN, and SVM algorithms through a different set of conditions and then proposed an understandable and highly accurate machine learning model. This paper intends to analyze consumer behavior patterns and understand the metrics that determine consumers in making decisions using sensitive analysis. We also provide marketing strategies and further propose the use of machine learning algorithms in predicting customer behavior patterns. Further results can be extended to various departments to evaluate performance on the platform to increase customer loyalty.
Enhancing and Predicting Digital Consumer Behavior with AI
Author: Musiolik, Thomas Heinrich
language: en
Publisher: IGI Global
Release Date: 2024-05-13
Understanding consumer behavior in today's digital landscape is more challenging than ever. Businesses must navigate a sea of data to discern meaningful patterns and correlations that drive effective customer engagement and product development. However, the ever-changing nature of consumer behavior presents a daunting task, making it difficult for companies to gauge the wants and needs of their target audience accurately. Enhancing and Predicting Digital Consumer Behavior with AI offers a comprehensive solution to this pressing issue. A strong focus on concepts, theories, and analytical techniques for tracking consumer behavior changes provides the roadmap for businesses to navigate the complexities of the digital age. By covering topics such as digital consumers, emotional intelligence, and data analytics, this book serves as a timely and invaluable resource for academics and practitioners seeking to understand and adapt to the evolving landscape of consumer behavior.
Neural Computing for Advanced Applications
This book presents refereed proceedings of the Second International Conference Neural Computing for Advanced Applications, NCAA 2021, held in Guangzhou, China, in August, 2021. The 54 full papers papers were thorougly reviewed and selected from a total of 144 qualified submissions. The papers are organized in topical sections on neural network theory, cognitive sciences, neuro-system hardware implementations, and NN-based engineering applications; machine learning, data mining, data security and privacy protection, and data-driven applications; neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling; computational intelligence, nature-inspired optimizers, and their engineering applications; fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences; control systems, network synchronization, system integration, and industrial artificial intelligence; computer vision, image processing, and their industrial applications; cloud/edge/fog computing, the Internet of Things/Vehicles(IoT/IoV), and their system optimization; spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19).