DATA ANALYTICS / POWER BI

Customer Shopping Behavior Analysis

Understanding customer purchasing patterns, product preferences, discounts, and satisfaction through data-driven analysis.

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01 / PROJECT OVERVIEW

From Raw Data to Business Insights

An end-to-end analytics project focused on transforming customer shopping records into meaningful business insights.

The project explores purchasing behavior, product category performance, customer reviews, and subscription patterns. Python and SQL support data exploration and analysis, while Power BI presents key metrics and findings in a visual format.

Business Questions

Which product categories contribute the most to recorded purchase amounts?
How do purchasing patterns differ across customer groups?
What do customer ratings tell us about satisfaction?
How many customers subscribe, and how can engagement be improved?
Are missing values affecting the reliability of the analysis?

02 / METHODOLOGY

Project Workflow

A structured process from data preparation to business recommendations.

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01. Data Source

Import customer shopping records from CSV files.

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02. Data Cleaning

Inspect missing values, data types, and data quality issues.

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03. Analysis

Explore purchasing patterns and customer behavior using Python and SQL.

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04. Visualization

Present important metrics and patterns through Power BI visuals.

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05. Recommendations

Identify opportunities for customer engagement and business improvement.

03 / DATA SNAPSHOT

Key Metrics

A snapshot of the primary customer shopping dataset.

Total Purchase Amount

$233,081

Average Purchase Amount

$59.76

Average Review Rating

3.75 / 5

Unique Customers

3,900

These figures describe the primary dataset. Confirm the currency convention before publishing the purchase amounts. Average purchase amount is not necessarily average order value, and recorded purchase amount is not necessarily accounting revenue.

04 / FINDINGS

Key Business Insights

Clothing Leads Purchase Value

Clothing contributes approximately $104,264, representing 44.7% of the total recorded purchase amount.

Clothing Has the Most Records

Clothing accounts for 1,737 records, followed by Accessories with 1,240 records.

Customer Satisfaction

The average review rating is approximately 3.75 out of 5, excluding records with missing ratings.

Missing Review Ratings

37 records have missing review ratings. These should be considered when interpreting customer satisfaction.

Subscription Patterns

The dataset contains 1,053 subscribed records and 2,847 non-subscribed records.

Compare Customer Groups Carefully

Male customer records total $157,890 in recorded purchase amount, compared with $75,191 for female customer records. These totals should be evaluated alongside group sizes and average purchase amounts.

Business Recommendations

Review Clothing inventory and product-level demand before making stocking decisions.
Investigate missing review ratings to improve the quality of customer satisfaction reporting.
Compare purchasing patterns between subscribers and non-subscribers before designing engagement campaigns.
Use average purchase amounts and customer counts alongside total amounts to make fair comparisons.

05 / TECHNOLOGY