# Customer Shopping Behavior Analysis & Retail Insights
> Explore retail data insights using Python and Power BI. Learn about customer segmentation, revenue by demographics, and strategic business recommendations.

Tags: data-analysis, customer-behavior, retail-analytics, power-bi-dashboard, python-data-science, business-intelligence, marketing-strategy
## Customer Shopping Behavior Analysis
* Analysis of spending patterns, customer segments, and product preferences using Python, SQL, and Power BI.

## Dataset Summary
* **Volume:** 3,900 Rows, 18 Columns.
* **Variables:** Demographics (Age, Gender, Location), Purchase Details (Category, Amount, Season), and Behavior (Frequency, Reviews, Shipping).

## Data Cleaning & Preparation (Python)
* **Missing Data:** Imputed 37 nulls in 'Review Rating' using category medians.
* **Feature Engineering:** Created age groups and standardized column names to snake_case.
* **Integration:** Data loaded into PostgreSQL after dropping redundant features.

## Revenue Analysis by Gender
* Male customers contributed significantly more revenue (over 2x) than female customers in this dataset.

## Top 5 Products by Average Rating
* Highest satisfaction items: Gloves (3.86), Sandals (3.84), Boots (3.82), Hats (3.80), and Skirts (3.78).

## Subscribers vs. Non-Subscribers
* Non-subscribers: 2,847 customers ($170,436 total revenue).
* Subscribers: 1,053 customers ($62,645 total revenue).
* Average spend is nearly identical at ~$59 per transaction for both groups.

## Customer Segmentation Profile
* **Loyal Segment:** Over 80% (3,116 customers).
* **Returning:** 701 customers.
* **New:** 83 customers.

## Revenue Contribution by Age Group
* Young Adults are the primary contributors ($62,143), followed by Middle-aged ($59,197), Adults ($55,978), and Seniors ($55,763).

## Power BI Dashboard Overview
* Key metrics include: Total Customers (3.9K), Avg Purchase ($59.76), and Avg Rating (3.75).

## Strategic Business Recommendations
* **Boost Subscriptions:** Create exclusive benefits to improve low conversion rates.
* **Enhance Loyalty:** Implement rewards for the 'Returning' segment.
* **Targeted Marketing:** Focus on 'Young Adults' and 'Express Shipping' users.
* **Product Positioning:** Use high-rated items like Gloves and Sandals in hero campaigns.
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