Leveraging unsupervised machine learning to optimize customer segmentation and product recommendations for increased retail profits
- Title
- Leveraging unsupervised machine learning to optimize customer segmentation and product recommendations for increased retail profits
- Creator
- Upreti G.; Natarajan A.K.
- Description
- The retail sector's success hinges on understanding and responding adeptly to diverse consumer behaviours and preferences. In this context, the burgeoning volume of transactional data has underscored the need for advanced analytical methodologies to extract actionable insights. This research delves into the realm of unsupervised machine learning techniques within retail analytics, specifically focusing on customer segmentation and the subsequent recommendation strategy based on clustered preferences. The purpose of this study is to determine which unsupervised machine learning clustering algorithms perform best for segmenting retail customer data to improve marketing strategies. Through a comprehensive comparative analysis, this study explores the performance of multiple algorithms, aiming to identify the most suitable technique for retail customer segmentation. Through this segmentation, the study aims not only to discern and profile varied customer groups but also to derive actionable recommendations tailored to each cluster's preferences and purchasing patterns. 2024, IGI Global. All rights reserved.
- Source
- Intersection of AI and Business Intelligence in Data-Driven Decision-Making, pp. 257-282.
- Date
- 2024-01-01
- Publisher
- IGI Global
- Coverage
- Upreti G., Christ University, India; Natarajan A.K., Samarkand International University of Technology, Uzbekistan
- Rights
- Restricted Access
- Relation
- ISBN: 979-836935289-2; 979-836935288-5
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Upreti G.; Natarajan A.K., “Leveraging unsupervised machine learning to optimize customer segmentation and product recommendations for increased retail profits,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/17582.