Customer_Behaviour_Analysis-Python-MySQL Server
This Project is based on the Customer behaviour of ShopEasy online portal. ShopEasy's Marketing and Customer Experience teams have reached out for a data-driven solution that analyzes customer behavior, reviews, and journey patterns using SQL and Python.
Problem Statement
ShopEasy is struggling with low customer engagement, poor conversion rates, and ineffective marketing strategies.
The business needs a data-driven approach using SQL and Python to analyze:
Customer journey behavior → Identify bottlenecks in the purchase process.
Customer reviews & feedback → Understand sentiment and satisfaction trends.
Marketing effectiveness → Measure the impact of engagement on conversion.
Product & demographic analysis → Identify high-performing products and customer segments.
Technical Tags:
SQL
Python
CSV Handling
Data Cleaning
Database Queries
Customer Behavior
Data Transformation
SQL Joins
Window Functions
CTEs
Basic Sentiment Analysis
Approach
Data Ingestion: Download customer-related data, convert CSVs into SQL tables, and automate insertion using Python (pandas, MySQL-connector).
Data Extraction & Transformation: Use SQL to extract, join, and analyze data with CTEs, window functions, and subqueries.
Customer Journey Analysis: Identify drop-offs, key actions for conversions, and engagement durations.
Reviews & Sentiment Analysis: Find highest/lowest-rated products, perform sentiment analysis, and correlate reviews with performance.
Marketing Effectiveness: Analyze retention rates, first-time vs. repeat buyers, and best-performing products by region.
Business Insights & Recommendations: Extract insights from SQL results and provide data-driven recommendations.
Expected Results:
=> Clean, well-structured SQL tables
=> Automated data insertion scripts
=> Actionable insights from SQL queries
=> Final report on customer trends & marketing impact