Predicting E-Commerce Revenue Trends: A Fusion of Big Data Analytics and Time Series Analysis

Nayantara Varadharajan, Mukil L D, Sangita Khare, Niharika Panda

2025

Abstract

This paper explores the synergistic potential of big data analytics and time series analysis in unraveling intricate patterns within historical sales data to predict and understand e-commerce revenue trends. The amalgamation of these two methodologies provides a robust framework for businesses to gain actionable insights, enabling strategic decision-making and fostering revenue growth. The utilization of big data analytics enables the processing and analysis of vast datasets, encompassing customer behaviors, market trends, and transactional details. Coupled with time series analysis, which focuses on temporal patterns and trends, this fusion approach offers a comprehensive understanding of the dynamic nature of e-commerce revenue. Through the application of predictive models such as Catboost, XGboost, LightGBM and AdaBoost, businesses can foresee future revenue trends, identifying peak sales periods, seasonal fluctuations, and potential market disruptions. This foresight empowers e-commerce platforms to optimize pricing strategies, capitalize on emerging opportunities, and mitigate risks. Furthermore, the integration of big data analytics and time series analysis facilitates the identification of hidden correlations and customer preferences. By discerning patterns in user interactions, businesses can tailor personalized customer experiences, enhancing satisfaction and loyalty. The strategic insights derived from this fusion approach go beyond mere trend identification. Businesses can implement targeted marketing campaigns, inventory management improvements, and website optimization strategies. This holistic understanding of the e-commerce landscape equips organizations to adapt swiftly to market dynamics and gain a competitive edge.

Paper Citation

in Harvard Style

Varadharajan N., L D M., Khare S. and Panda N. (2025). Predicting E-Commerce Revenue Trends: A Fusion of Big Data Analytics and Time Series Analysis. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 351-359. DOI: 10.5220/0013592300004664

in Bibtex Style

@conference{incoft25,
author={Nayantara Varadharajan and Mukil L D and Sangita Khare and Niharika Panda},
title={Predicting E-Commerce Revenue Trends: A Fusion of Big Data Analytics and Time Series Analysis},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={351-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013592300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Predicting E-Commerce Revenue Trends: A Fusion of Big Data Analytics and Time Series Analysis
SN - 978-989-758-763-4
AU - Varadharajan N.
AU - L D M.
AU - Khare S.
AU - Panda N.
PY - 2025
SP - 351
EP - 359
DO - 10.5220/0013592300004664
PB - SciTePress