Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Predicting Retail Trends: Integrating Comparative Sales Analysis with Consumer Insights

Authors
Kirti Wanjale1, *, Sanjaesh Pawale2, Tejas Ahire3, Piyush Mathurkar4, Rohit Wakade5, Aditya Labhade6
1Professor, Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
2Professor, Vishwakarma University, Pune, Maharashtra, India
3Student, Department of Electronics and Telecommunications, Vishwakarma Institute of Technology, Pune, Maharashtra, India
4Assistant Professor, Department of Electronics and Telecommunications, Vishwakarma Institute of Technology, Pune, Maharashtra, India
5Student, Department of CSE-DS, Vishwakarma Institute of Technology, Pune, Maharashtra, India
6Student, Department of Electronics and Telecommunications, Vishwakarma Institute of Technology, Pune, Maharashtra, India
*Corresponding author. Email: kirti.wanjale@vit.edu
Corresponding Author
Kirti Wanjale
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_16How to use a DOI?
Keywords
Machine Learning; Linear Regression; Logistic Regression; Sales Comparison
Abstract

A critical component of the retail sector is comparative sales analysis, which offers insightful information about consumer behavior and aids merchants in identifying areas for development and improvement. The purpose of this research paper is to examine the idea of comparative sales analysis and its significance in the retail sector. In the opening paragraphs, the term “comparative sales analysis” and its several subtypes, including “year over year,” “quarter over quarter,” and “month over month,” are defined. Following that, it discusses the significance of comparative sales analysis in the retail sector, including how it may be used to spot patterns, project future sales, and evaluate the success of sales tactics. The following section of the essay looks at the various variables, such as environmental, psychographic, and demographic factors, that affect consumer behavior. Additionally, it covers the numerous sales techniques used by merchants to draw in and keep customers, including product positioning, pricing, advertising, and customer service. Finally, the article draws attention to the significance of applying comparative sales analysis in conjunction with customer behavior and sales techniques to spur development and achievement in the retail sector.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_16How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kirti Wanjale
AU  - Sanjaesh Pawale
AU  - Tejas Ahire
AU  - Piyush Mathurkar
AU  - Rohit Wakade
AU  - Aditya Labhade
PY  - 2026
DA  - 2026/01/06
TI  - Predicting Retail Trends: Integrating Comparative Sales Analysis with Consumer Insights
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 234
EP  - 243
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_16
DO  - 10.2991/978-94-6463-948-3_16
ID  - Wanjale2026
ER  -