Data Mining and Predictive Analytics for Business Forecasting: Emerging Trends and Applications in Digital Commerce

Authors

  • M. Arunkumar Victor
  • M. Manida

Keywords:

Artificial intelligence, Big data analytics, Business forecasting, Customer Behaviour, Data mining, Demand forecasting, Digital commerce, E-Commerce, Machine learning, Predictive analytics

Abstract

The rapid growth of digital commerce has generated vast volumes of structured and unstructured data from online transactions, customer interactions, digital payments, social media, and e-commerce platforms. Data mining and predictive analytics have emerged as important tools for transforming these data into meaningful business insights and improving forecasting accuracy. This paper examines the emerging trends and applications of data mining and predictive analytics in business forecasting, with particular reference to digital commerce. The study focuses on the application of data-driven techniques for sales forecasting, customer behaviour prediction, demand estimation, market trend analysis, inventory planning, customer segmentation, and revenue management. It highlights the growing use of machine learning, artificial intelligence, big data analytics, and real-time data processing in supporting strategic and operational decision-making. The paper also discusses how predictive models can help digital businesses identify emerging market patterns, anticipate customer requirements, manage business risks, and improve resource allocation. Particular attention is given to the integration of transactional, behavioural, and digital interaction data for developing more responsive forecasting models. The study adopts a conceptual and descriptive approach based on secondary sources, including research articles, industry reports, and relevant academic literature. The findings indicate that the integration of data mining and predictive analytics can enhance forecasting capabilities and support evidence-based decision-making in digital commerce. However, issues relating to data quality, privacy, cybersecurity, algorithmic bias, technological capability, and analytical skills remain important challenges. The paper concludes that responsible adoption of advanced analytics can strengthen business agility, competitiveness, and sustainable growth in the evolving digital commerce environment.

Published

2026-10-19

How to Cite

M. Arunkumar Victor, & M. Manida. (2026). Data Mining and Predictive Analytics for Business Forecasting: Emerging Trends and Applications in Digital Commerce. Recent Trends in Data Mining and Business Forecasting (P-ISSN: 3139-3888), 7(2), 26–40. Retrieved from https://www.matjournals.net/engineering/index.php/JTDMBF/article/view/4325