Consumer Behavior Mapping Through Search Pattern Analysis in Digital Platform - Imam Safei Muslim (Journal of Digital Marketing and Search Engine Optimization, 2025)
This study examines consumer behavior patterns through comprehensive search pattern analysis across three major e-commerce platforms. The research analyzed 127,543 search sessions from 12,847 unique users over six months using Latent Dirichlet Allocation (LDA) and K-means clustering. Data collection involved clickstream analysis, query pattern extraction, and behavioral tracking across mobile, desktop, and tablet devices. Statistical methods included hierarchical linear modeling, ANOVA, and chi-square tests. The analysis identified five distinct consumer segments: Exploratory Browsers (32.4%), Systematic Researchers (23.8%), Direct Purchasers (18.7%), Deal Seekers (15.3%), and Uncertain Seekers (9.8%). Results reveal significant behavioral variations across customer journey stages, with query length increasing from 2.84 to 4.89 words and brand mentions rising from 15.2% to 71.3% from awareness to retention stages. Mobile devices dominated usage (63.4%), with distinct behavioral patterns across demographics and temporal factors. These findings enable businesses to develop targeted marketing strategies, optimize user experience design, and implement personalized recommendation systems. This research contributes original insights by integrating quantitative behavioral analytics with qualitative thematic analysis, providing a comprehensive framework for understanding digital consumer decision-making processes in contemporary e-commerce environments.