Comparative Analysis of Personalization Strategies Across E-commerce Segments

Thesis Abstract

This study investigates how personalization strategies influence consumer engagement, trust, and conversion across distinct e-commerce segments, addressing the problem of inconsistent effectiveness of tailored experiences in diverse online shopping contexts. The aim is to identify segment-specific personalization practices that maximize customer value while maintaining privacy and ethical standards. Specific objectives are (1) to compare the impact of personalization dimensions—product recommendations, dynamic pricing, content customization, and message timing—across electronics, fashion, and grocery e-commerce segments; (2) to examine how consumer traits (privacy concerns, tech-savviness, and impulse buying tendency) moderate these effects; (3) to assess the relationship between perceived personalization quality, trust, perceived value, and purchase intention; and (4) to develop a pragmatically grounded framework for segment-tailored personalization strategy. The study adopts a cross-sectional, mixed-methods design combining quantitative and qualitative data to capture both breadth and depth of personalization effects. The population comprises adult online shoppers in the United States who have engaged with at least one e-commerce platform in the past six months. A stratified random sample of 1,200 respondents is targeted, with equal representation across electronics, fashion, and groceries (n=400 per segment). Data collection employs a structured online survey measuring constructs adapted from established scales perceived personalization quality, trust in the platform, perceived value, customer engagement, and purchase intention; privacy concerns and impulsiveness are measured as moderating variables. In-depth interviews with 18 practitioners (six from each segment) supplement the survey data to contextualize results and validate the operationalization of personalization tactics. Quantitative analysis proceeds with confirmatory factor analysis to establish validity and reliability of the measurement model, followed by multi-group structural equation modeling to test hypothesized relationships and segment differences. Moderation analyses assess how privacy concerns and impulsiveness affect the personalization–outcome linkages. A supplementary ANOVA tests mean differences in perceived personalization quality and purchase intention across segments, while regression analyses identify the relative predictive power of each personalization dimension. The qualitative data are analyzed using thematic analysis to extract patterns regarding organizational constraints, technological capabilities, and consumer attitudes that influence the effectiveness of personalization across segments. Expected findings suggest that personalization strategies have differential effects by segment. In electronics, real-time recommendations and dynamic pricing may strongly influence trust and immediate conversion, moderated by higher privacy concerns among power shoppers. In fashion, content customization and timely messaging are anticipated to yield higher perceived value and engagement, aided by higher susceptibility to aesthetic cues. In groceries, simplified, low-friction personalization (quick-reorder prompts and curated bundles) is expected to drive consistent increases in purchase intention, with moderate effects from dynamic pricing. Across all segments, higher perceived personalization quality is anticipated to correlate with greater trust and perceived value, which in turn predict purchase intention, with privacy concerns dampening this pathway in electronics more than in fashion or groceries. Contributions to knowledge include (a) a comparative, segment-specific framework for personalization effectiveness grounded in empirical evidence, (b) refined measurement models for personalization quality across e-commerce contexts, and (c) practical implications for managers on balancing personalization depth with privacy considerations. The study advances theory by integrating the Technology Acceptance Model and Trust Theory with segmentation-based optimization of personalization, highlighting boundary conditions related to consumer privacy attitudes and impulsivity. Policy implications address data handling ethics and transparency in personalization practices. The main conclusion posits that a one-size-fits-all personalization approach is suboptimal; instead, segment-tailored strategies that align personalization depth with consumer privacy orientations and buying behavior yield superior outcomes. Recommendations include developing segment-specific personalization playbooks, investing in transparent data governance, and employing iterative experimentation (A/B testing) to continuously refine tactics with attention to regulatory constraints and evolving consumer expectations.