Customer-Shopping-Behavior

Business Problem

A leading retail company wants to better understand its customers' shopping behavior in order to improve sales, customer satisfaction, and long-term loyalty. The management team has noticed changes in purchasing patterns across demographics, product categories, and sales channels (online vs. offline). They are particularly interested in uncovering which factors, such as discounts, reviews, seasons, or payment preferences, drive consumer decisions and repeat purchases.

🛍️ Customer Shopping Behavior Analysis

📖 Overview

This project analyzes customer shopping behavior using transactional data from 3,900 purchases across multiple product categories.
The goal is to uncover insights into spending patterns, customer segmentation, product preferences, and subscription behavior to guide strategic business decisions.

Additionally, the project presentation was created using Gamma AI, enabling visually compelling, AI-powered storytelling for business insights.

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🧾 Dataset Summary

• Rows: 3,900
• Columns: 18
• Key Features:
  • 🧍‍♂️ Customer Demographics: Age, Gender, Location, Subscription Type
  • 🛒 Purchase Details: Item Purchased, Category, Purchase Amount, Season, Size, Color
  • 📊 Shopping Behavior: Discount Applied, Promo Code Used, Previous Purchases, Frequency, Review Rating, Shipping Type
• Missing Data: 37 missing values in the Review Rating column

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🧹 Data Cleaning & Preparation (Python)

Performed using Pandas and NumPy for data wrangling and preprocessing.

Steps:

1. Data Loading: Imported dataset using pandas.read_csv().
2. Exploration: Used .info() and .describe() for summary statistics.
3. Handling Missing Data: Filled missing Review Rating values using median rating per product category.
4. Column Standardization: Converted column names to snake_case for readability.
5. Feature Engineering:
   • Created age_group feature:
     • Children (10–20)
     • Young (21–30)
     • Mid-Senior (31–50)
     • Senior (51+)
   • Enabled better analysis of spending, subscriptions, and ratings across segments.
6. Normality Tests (Shapiro–Wilk):
   • age and purchase_amount were not normally distributed, indicating concentrated customer segments and skewed spending behavior.
7. Database Integration: Loaded cleaned dataset into PostgreSQL for SQL analysis.

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🧠 SQL Analysis (Business Insights)

Business questions were analyzed using PostgreSQL to extract actionable insights.

#	Business Question	Insight Focus
1	Revenue & Avg Purchase by Gender	Spending Patterns
2	Customer Count by Age Group	Demographic Analysis
3	Total Spending by Age Group	Revenue Contribution
4	High-Spending Discount Users	Value vs. Discounts
5	Category Performance	Best-Selling & Top-Rated Categories
6	Top 5 Products by Avg Rating	Customer Satisfaction
7	Top 5 Locations by Sales	Regional Sales Insights
8	Avg Purchase by Shipping Type	Delivery Preference Impact
9	Subscribers vs Non-Subscribers	Subscription Impact
10	Top 5 Products by Discount Usage	Promotion Effectiveness
11	Customer Segmentation	Loyalty & Retention
12	Top 3 Products per Category	Category-Level Bestsellers

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📊 Power BI Dashboard

An interactive Power BI dashboard was created to visualize and explore insights intuitively.
Dropdown slicers and filters allow dynamic analysis across multiple dimensions.

Dashboard Highlights:

• 💰 Revenue by Gender & Age Group
• 🏆 Top Performing Categories & Products
• 🚚 Purchase Amount by Shipping Type
• 📍 Sales Distribution by Location
• 🎯 Subscribers vs Non-Subscribers

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🎥 Gamma AI Presentation

To effectively communicate insights and findings, a Gamma AI presentation was designed.

Features:

• AI-generated slide designs for clean, modern visuals.

• Automated content summarization for executive storytelling.

• Integrated charts and visuals from Power BI exports.

• Perfect for portfolio showcasing and data storytelling in interviews or client demos.

  Click below to view the interactive presentation built using Gamma AI 👇

View Presentation

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💡 Business Recommendations

Based on insights from Python, SQL, and Power BI:

1. Boost Subscriptions: Offer exclusive benefits to convert non-subscribers.
2. Loyalty Program: Encourage repeat purchases to nurture loyal customers.
3. Review Discount Policy: Optimize discounts to maintain profit margins.
4. Product Positioning: Promote top-rated and best-selling products.
5. Targeted Marketing: Focus marketing on high-spending age groups and express shipping users.

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🧰 Tech Stack

Tool	Purpose
Python (Pandas, NumPy)	Data Cleaning & Preprocessing
PostgreSQL	Business Querying & Data Storage
Power BI	Visualization & Dashboard
Gamma AI	Presentation & Data Storytelling
VS Code	Development Environment

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