Customer_behaviour_analysis

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Problem Statement

Understanding customer behavior is essential for optimizing business strategies and improving customer satisfaction. ShopEasy aims to analyze customer interactions, purchase patterns, sentiment trends, and marketing effectiveness to gain actionable insights. The objective is to enhance engagement, optimize marketing spend, and boost conversions by leveraging data-driven decisions.

Project Overview

This project focuses on analyzing customer behavior, sentiment, and purchase patterns to improve customer experience and retention at ShopEasy. The analysis leverages SQL and Python to derive meaningful insights from customer reviews, ratings, and purchase history.

Objectives

• Perform sentiment analysis on customer reviews.
• Identify key complaints and areas for improvement.
• Analyze purchase patterns and their correlation with product performance.
• Find relationships between negative reviews and low retention rates.
• Provide recommendations to enhance customer satisfaction.

Tech Stack Used

• SQL: Data extraction, cleaning, and transformation.
• Python: Data analysis and visualization.
  • Pandas: Data manipulation
  • NLTK/TextBlob: Sentiment analysis
  • Matplotlib & Seaborn: Data visualization
• Jupyter Notebook: For interactive analysis and reporting.

Project Workflow

1. Data Collection: Customer reviews, ratings, and purchase history.
2. Data Preprocessing: Cleaning and transforming data using SQL & Python.
3. Sentiment Analysis: Identifying positive, neutral, and negative sentiments.
4. Exploratory Data Analysis (EDA): Understanding customer behavior.
5. Pattern Recognition: Identifying key insights and trends.
6. Recommendations: Strategies to improve customer retention and satisfaction.

Installation & Usage

1. Clone this repository:

   git clone https://github.com/yourusername/customer-behavior-analysis.git
   cd customer-behavior-analysis

2. Install dependencies:

   pip install -r requirements.txt

3. Run the analysis notebook:

   jupyter notebook Customer_Behavior_Analysis.ipynb

Results & Insights

• Key pain points in customer experience.
• Products with low repeat purchases and negative feedback.
• Patterns in seasonal trends and customer preferences.
• Actionable recommendations to enhance retention and engagement. **

Conclusion

This analysis provides valuable insights into customer behavior, purchase patterns, and marketing effectiveness. Implementing the recommendations can lead to better engagement, improved customer satisfaction, and increased revenue.

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