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Harnessing Data Science and Machine Learning for Strategic Business Decision-Making in Multi-Channel Retail Environments

by Rasel Sordar, Iffat Ara, Sadia Afrin Khan, Khaled Bin Showkot Tanim, Nazmul Hossain, MD Zihad Monsur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 66
Year of Publication: 2025
Authors: Rasel Sordar, Iffat Ara, Sadia Afrin Khan, Khaled Bin Showkot Tanim, Nazmul Hossain, MD Zihad Monsur
10.5120/ijca2025926108

Rasel Sordar, Iffat Ara, Sadia Afrin Khan, Khaled Bin Showkot Tanim, Nazmul Hossain, MD Zihad Monsur . Harnessing Data Science and Machine Learning for Strategic Business Decision-Making in Multi-Channel Retail Environments. International Journal of Computer Applications. 187, 66 ( Dec 2025), 9-16. DOI=10.5120/ijca2025926108

@article{ 10.5120/ijca2025926108,
author = { Rasel Sordar, Iffat Ara, Sadia Afrin Khan, Khaled Bin Showkot Tanim, Nazmul Hossain, MD Zihad Monsur },
title = { Harnessing Data Science and Machine Learning for Strategic Business Decision-Making in Multi-Channel Retail Environments },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2025 },
volume = { 187 },
number = { 66 },
month = { Dec },
year = { 2025 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number66/harnessing-data-science-and-machine-learning-for-strategic-business-decision-making-in-multi-channel-retail-environments/ },
doi = { 10.5120/ijca2025926108 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-12-18T17:50:24.488990+05:30
%A Rasel Sordar
%A Iffat Ara
%A Sadia Afrin Khan
%A Khaled Bin Showkot Tanim
%A Nazmul Hossain
%A MD Zihad Monsur
%T Harnessing Data Science and Machine Learning for Strategic Business Decision-Making in Multi-Channel Retail Environments
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 66
%P 9-16
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the cutthroat modern retail environment, strategic decision-making is increasingly driven by intelligent data usage. This research investigates how data science and ML can be leveraged to enhance critical business processes in multi-channel retail settings that include online, in-store, and social commerce platforms. An integrated framework comprising predictive modeling, customer segmentation, promotion response analysis, and dynamic pricing has been used in this research to demonstrate how machine learning enhances business intelligence and improves operational performance. A thorough analysis pipeline in Python was developed with models such as Random Forest, XGBoost, K-Means, LSTM, and Q-learning. Results showed significant enhancement in the accuracy of forecasts, R² = 0.93; efficiency of marketing, AUC = 0.91; and inventory optimization, MAPE = 6.2%. Feature importance analysis further showed that customer engagement and discount sensitivity are key drivers of revenue performance. The study concludes that integrating analytics driven by ML into strategic retail management empowers better-informed, agile, and profitable decision-making, placing data-driven intelligence at the heart of sustainable retail competitiveness.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Science; Machine Learning; Multi-Channel Retail; Predictive Analytics; Customer Segmentation; Dynamic Pricing; Business Intelligence; Strategic Decision-Making; Big Data; Artificial Intelligence

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