The Challenge
Algorithms often decide visibility before users do. Many products are in the right category, properly priced, and functionally correct, yet they fail to appear in relevant searches. The core issue is semantic misalignment between how users express problems and nas algorithms interpret product language. This study analyzes this gap using real marketplace data.
The Process
I conducted an algorithm analysis by grouping products by problem-based intent rather than keywords. By applying TF-IDF and semantic similarity techniques, I mapped how machine learning models cluster meaning. This revealed why small wording changes can dramatically shift organic visibility without touching ads or pricing.
Project Focus
Identified semantic gaps causing invisible product placement.
Technical Mapping
ANALYSIS LOG
Intent-Based Clustering
Visibility Shift Detection