👕 Polo Shirt Product Performance Analysis

📖 Executive Summary

I analyzed 5,120 customer reviews and transactions for PoloMax (a fashion e-commerce brand) to diagnose a critical business issue: a 48% product return rate.

Using SQL for data extraction and exploratory data analysis (EDA), I identified that the high return rate was a systemic product quality issue rather than a logistics failure. My analysis provided data-backed recommendations to pause underperforming SKUs and double down on high-revenue variants.

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📊 Dashboard Preview

Visual overview of market performance, return rates by location, and rating distribution.

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🎯 Business Problem

The brand faced declining profitability due to:

• A critically high Return Rate (48.09%).
• Mediocre Customer Satisfaction (3.02 Avg Rating).
• Unknown performance drivers across different SKUs (Colors/Sizes).

Objective: Analyze sales data to pinpoint the root cause of returns and identify high-value opportunities.

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🛠️ Tech Stack & Skills

• SQL (MySQL): Used for data cleaning, aggregation, CASE statements, and complex filtering.
• Data Analysis: Trend analysis, correlation (Price vs. Rating), and root cause analysis.
• Data Visualization: Created dashboard mockups to visualize geographic spread and SKU performance.
• Strategic Planning: Translated raw data into actionable business steps (e.g., "Pause Black variant sales").

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🔍 Key Insights & SQL Logic

1. The "Black Shirt" Quality Issue

I discovered a strong correlation between color variants and customer ratings. While Red shirts were the top revenue drivers (~964k), Black shirts consistently received the lowest ratings (2.98), indicating a specific fabric or dye issue.

SQL Snippet Used:

-- Average Price vs. Average Rating by Material (Proxy: Color)
SELECT Color as Material_Proxy, 
       ROUND(AVG(Price), 2) as Avg_Price, 
       ROUND(AVG(Rating), 2) as Avg_Rating 
FROM tshirts 
GROUP BY Color;

2. Systemic vs. Localized Returns

To determine if returns were due to logistics (e.g., a bad courier in Delhi) or the product itself, I analyzed return rates by location. The rate was consistent (~48%) across all cities, proving the issue was systemic (product fit/sizing).

SQL Snippet Used:

-- Return Rate by Location
SELECT Location, 
       ROUND((SUM(CASE WHEN Return_Status = 'Returned' THEN 1 ELSE 0 END) * 100.0 / COUNT(*)), 2) as Return_Rate
FROM tshirts 
GROUP BY Location;

3. Emerging Growth

Identified SKU POLO-66138 as the top gaining product, jumping from zero visibility in 2024 to significant traction in 2025.

Recommendations

Based on the data, I proposed the following strategy to the stakeholders:

• Immediate Action: Pause sales of the Black variant to stop brand damage.

• Audit: Conduct a full audit of the sizing chart (primary suspect for the 48% return rate).

• Inventory Shift: Expand the "Red" product line, which drives high volume and high satisfaction.

• Target Metrics: Aim to reduce return rate to 25%, which is projected to increase profit margins by 23%.

Project Structure

• polo_tshirt_cleaned_dataset.csv: The raw dataset containing 5,120 records.

• polo_shirt_sql_queries.sql: Complete SQL script containing all analytical queries.

• PoloMax_Product_Analysis.pdf: Final presentation deck presented to stakeholders.

• SQL_POLO_SHIRT_ANALYSIS_REPORT.docx: Detailed written report of findings.