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Unpacking Retail Customer Retention Through Cohort Analysis and Demographic Segmentation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Understanding customer retention in retail presents a challenge that this research addresses by leveraging cohort analysis and demographic segmentation. The relevance lies in its ability to enhance retail competitiveness by uncovering how different customer segments exhibit varying retention behaviors over time, thus informing more effective retention strategies. The methodology combines cohort analysis with demographic segmentation to examine retention rates among diverse customer groups. This approach facilitates efficient detection and analysis of retention trends that reveal significant differences in customer retention patterns across demographic segments. Female customers exhibit higher loyalty than males across various cohorts, while Baby Boomers and Gen X display distinct engagement levels. These insights suggest that tailored retention strategies are necessary to address the unique behaviors of different demographic segments. The study’s managerial implications are substantial, providing actionable insights for business stakeholders to optimize marketing efforts and enhance customer relationship management practices based on demographic and cohort analysis.

Original languageEnglish
Title of host publicationSmart Computing Paradigms
Subtitle of host publicationAdvanced Data Mining and Analytics - Proceedings of 7th International Conference on Smart Computing and Informatics, SCI 2025
EditorsVikrant Bhateja, Angela Lee Siew Hoong, Jude Dzevela Kong, Jasmine Jena Junali
Place of PublicationCham
Pages145-158
Number of pages14
Volume1683
ISBN (Electronic)978-3-032-08243-5
DOIs
Publication statusPublished - 2026
Event7th International Conference on Smart Computing and Informatics, SCI 2025 - Kuala Lumpur, Malaysia
Duration: 8 Apr 2025 → 9 Apr 2025

Publication series

NameLecture Notes in Networks and Systems
Volume1683 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference7th International Conference on Smart Computing and Informatics, SCI 2025
Country/TerritoryMalaysia
CityKuala Lumpur
Period8/04/25 → 9/04/25

Keywords

  • Cohort Analysis
  • Customer Retention
  • Demographic Segmentation

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