https://github.com/sayande01/consumer_shopping_trend_analysis

This project analyzes consumer shopping patterns using transaction data, demographics, and product categories to identify high-value customer segments, popular products, and retention trends. Insights will help optimize inventory, enhance customer satisfaction, and boost revenue through data-driven strategies.
https://github.com/sayande01/consumer_shopping_trend_analysis

Last synced: about 2 months ago
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This project analyzes consumer shopping patterns using transaction data, demographics, and product categories to identify high-value customer segments, popular products, and retention trends. Insights will help optimize inventory, enhance customer satisfaction, and boost revenue through data-driven strategies.

• Host: GitHub
• URL: https://github.com/sayande01/consumer_shopping_trend_analysis
• Owner: sayande01
• Created: 2024-12-01T15:05:43.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-12-01T15:07:16.000Z (6 months ago)
• Last Synced: 2025-02-13T02:38:33.794Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 469 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### **Title:**  

**Consumer Shopping Trends Analysis: Unveiling Insights for Strategic Retail Growth**

### **Description:**  

This project delves into the analysis of consumer shopping patterns to extract actionable insights that can drive strategic decision-making in the retail sector. By leveraging data from transaction records, customer demographics, and product categories, the analysis aims to identify high-value customer segments, highlight popular product categories, and predict customer retention. The insights gained will empower retail businesses to enhance customer satisfaction, optimize inventory, and improve retention strategies, thereby driving revenue growth and operational efficiency.

### **Objective:**  

1. **Identify High-Value Customer Segments:**  

   Segment customers based on their spending patterns, frequency of purchases, and average transaction value to focus on the most profitable groups.

2. **Uncover Popular Product Categories:**  

   Analyze product sales data to determine which categories contribute the most to revenue, helping retailers optimize inventory and marketing efforts.

3. **Predict Customer Retention:**  

   Build predictive models to assess customer churn risk, enabling proactive retention strategies and enhancing customer lifetime value.

4. **Support Data-Driven Decision-Making:**  

   Provide actionable insights to help retail stakeholders make informed decisions on customer engagement, marketing campaigns, and product offerings.