Retail Market Analysis

Paper: arXiv 2502.00024

Authors: Ke Yuan, Yaoxin Liu, Shriyesh Chandra, Rishav Roy

Abstract

This project focuses on analyzing retail market trends using historical sales data, search trends, and customer reviews. By identifying the patterns and trending products, the analysis provides actionable insights for retailers to optimize inventory management and marketing strategies, ultimately enhancing customer satisfaction and maximizing revenue.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 4.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper serves primarily as an introductory data engineering and basic machine learning project report using Spark, rather than an advanced quantitative finance study. It applies standard decision trees and exploratory data analysis to public retail datasets with minimal mathematical depth and low methodological novelty.

Research Flowchart

  flowchart TD
    A[Goal: Optimize Retail Strategy & Revenue] --> B[(Inputs: Sales Data, Search Trends & Reviews)]
    B --> C[Methodology: Trend & Sentiment Analysis]
    C --> D[Computational Processing: Pattern & Sentiment Extraction]
    D --> E[Outcomes: Trend Identification & Sentiment Insights]
    E --> F[Actionable Results: Optimized Inventory & Marketing]