Customer Shopping Behavior Analysis: A Deep-Dive into Retail Analytics

1. Project Overview

This repository showcases a complete, end-to-end data analytics project designed to transform raw transactional data into a powerful tool for strategic decision-making in a retail environment. The project focuses on creating a robust Power BI solution for analyzing customer purchasing patterns, segmenting high-value demographics, and evaluating product performance.

The entire project was developed from a foundational dataset of 3,900 transactions. It began with a rigorous data cleaning and feature engineering process in Python, where the raw data was prepared for advanced analysis. This cleaned data was then loaded into a PostgreSQL database for structured querying before being ingested, modeled, and transformed within Power BI to create a suite of insightful, interactive dashboards.

The final solution moves beyond static sales reports, providing stakeholders with dynamic, actionable insights into who their most valuable customers are, which products drive the most revenue, and how promotions influence buying behavior. It empowers managers to refine marketing campaigns, optimize inventory, and ultimately, drive sustainable growth through a deeper understanding of their customer base.

2. The Business Problem & Problem Statement

A retail company possessed a wealth of transactional data but lacked a clear, data-driven strategy to understand its customer base. The company's marketing and product decisions were being made without a deep understanding of purchasing patterns, leading to inefficient ad spend, suboptimal inventory management, and missed opportunities for customer retention. They needed to move from simply collecting data to generating actionable intelligence.

The core challenge was broken down into four key problem statements:

• Lack of Customer Segmentation: The company had no systematic way to identify its most valuable customer segments. They were unable to answer critical questions like: Which demographic groups (age, gender) generate the most revenue? What distinguishes a one-time buyer from a loyal, repeat customer?
• Ineffective Marketing and Promotion Strategy: Marketing efforts were broad and untargeted. The company struggled to understand the real impact of discounts, seasonal trends, and subscriptions on customer spending, making it difficult to design effective promotional campaigns.
• Unclear Drivers of Customer Loyalty: While the company knew repeat business was vital, they had no clear insight into what drives it. They needed to understand the journey from a new customer to a returning, and eventually, a loyal one.
• Suboptimal Product and Inventory Management: Decisions about which products to promote and stock were based on simple sales volume, not deeper insights. The company could not identify which items were not only top-sellers but also highly rated by customers, or which product categories held the most strategic value.

The overarching business objective was to answer the question: "How can the company leverage consumer shopping data to identify trends, improve customer engagement, and optimize marketing and product strategies?"

3. How It Was Solved: An End-to-End Approach

To address the business challenges, I designed and executed a multi-phased data analytics workflow, transforming raw data into a strategic asset.

Phase 1: Data Foundation and Preparation (Python)