CUSTOMER SEGMENTATION STRATEGIES IN E-COMMERCE USING DATA ANALYTICS

This research investigates the relationship between demographic factors (age, gender, and occupation) and various aspects of online shopping behavior, including e-commerce platform preference, frequency of online shopping, and beliefs about personalized online shopping experiences.Utilizing a mixed-methods approach, the study combines qualitative and quantitative methodologies to provide a comprehensive understanding of customer segmentation strategies in the e-commerce sector, specifically focusing on data analytics.Data collection involved questionnaire surveys, observations, case studies, and literature review, incorporating both primary and secondary data sources.A sample population of individuals aged 18 to 25, actively engaged in online shopping, was targeted for the study.The research employed the Chi-Square test as a sampling method, dividing the population into clusters to ensure focused data collection.Analysis of the data revealed significant relationships between age, gender, occupation, and various aspects of online shopping behavior.Specifically, age was found to influence e-commerce platform preference, while gender and occupation were associated with both e-commerce platform preference and the frequency of online shopping.However, demographic factors did not significantly impact beliefs about personalized online shopping experiences.These findings contribute to a deeper understanding of customer segmentation strategies in e-commerce and highlight the nuanced influences of demographic factors on online shopping behavior.