Data-Driven Consumer Behaviour Prediction: Key Factors and Machine Learning Approaches

Yiling Wang

2025

Abstract

In today's data-driven market environment, consumer behaviour prediction has become an indispensable tool for companies seeking to develop more targeted marketing strategies, optimise supply chains, and enhance customer experience. This article systematically explores the internal and external factors that influence consumer behaviour prediction, including psychological characteristics, demographic factors, market trends, advertising campaigns, and social media influences. It also examines the intricate interplay between these elements, highlighting how they can collectively shape purchasing decisions. In addition, the article analyses in detail different prediction methods, ranging from traditional statistical models (e.g., linear regression and decision trees) to emerging machine learning techniques (e.g., integrated learning and clustered classification models), and assesses their respective strengths and weaknesses. Addressing challenges such as data privacy, data imbalance, model interpretability, and real-time performance, this paper proposes a series of practical solutions while looking ahead to future trends in consumer behaviour prediction. Ultimately, the research provides valuable insights for enterprises, marketers, and researchers to optimise data-driven marketing strategies and business decisions.

Paper Citation

in Harvard Style

Wang Y. (2025). Data-Driven Consumer Behaviour Prediction: Key Factors and Machine Learning Approaches. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 247-252. DOI: 10.5220/0013685900004670

in Bibtex Style

@conference{icdse25,
author={Yiling Wang},
title={Data-Driven Consumer Behaviour Prediction: Key Factors and Machine Learning Approaches},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={247-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013685900004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Data-Driven Consumer Behaviour Prediction: Key Factors and Machine Learning Approaches
SN - 978-989-758-765-8
AU - Wang Y.
PY - 2025
SP - 247
EP - 252
DO - 10.5220/0013685900004670
PB - SciTePress