Dynamic E-Commerce Pricing: Optimizing Routes and Forecasting Demand with Machine Learning

Sri Ramya Divakarla, Prabina Subedi, Kamatchi S, Giriraja C. V.

2025

Abstract

Dynamic pricing is a vital strategy in e-commerce, enabling retailers to adapt to fluctuating demand and geographic constraints. This paper introduces a novel framework that integrates geographic network analysis, Dijkstra’s algorithm, and machine learning (ML) for dynamic pricing optimization. A geographic network is constructed with cities as nodes and edges representing the shortest paths calculated using Dijkstra’s algorithm, which facilitates location-based price adjustments. ML techniques are used to predict demand across cities using historical retail data, enabling real-time adjustments based on geographic proximity and demand variability. Computational efficiency is achieved through KD-Trees for spatial searches and multiprocessing for large datasets. The proposed approach demonstrates the ability to optimize pricing strategies by accounting for both geographic and demand variability, resulting in enhanced customer satisfaction and increased revenue. This work offers a robust methodology for e-Commerce platforms to personalize pricing and leverage predictive analytics, providing a competitive edge in dynamic and diverse markets.

Paper Citation

in Harvard Style

Divakarla S., Subedi P., S K. and C. V. G. (2025). Dynamic E-Commerce Pricing: Optimizing Routes and Forecasting Demand with Machine Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 444-454. DOI: 10.5220/0013621300004664

in Bibtex Style

@conference{incoft25,
author={Sri Ramya Divakarla and Prabina Subedi and Kamatchi S and Giriraja C. V.},
title={Dynamic E-Commerce Pricing: Optimizing Routes and Forecasting Demand with Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={444-454},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013621300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Dynamic E-Commerce Pricing: Optimizing Routes and Forecasting Demand with Machine Learning
SN - 978-989-758-763-4
AU - Divakarla S.
AU - Subedi P.
AU - S K.
AU - C. V. G.
PY - 2025
SP - 444
EP - 454
DO - 10.5220/0013621300004664
PB - SciTePress