Cohort Analysis + Revenue growth Analysis + Customer segmentation for Target Marketing + User retention for a product company

📊 Project Overview :

A comprehensive data analysis of a product company's platform's transaction data and user interactions for June-July 2024. This analysis provides insights into user behavior, transaction patterns, and platform performance metrics.

🎯 Key Objectives

• Analyze transaction patterns and user behavior
• Evaluate payment gateway performance
• Identify user interaction trends
• Generate actionable insights for platform improvement

📈 Data Analysis Highlights

Dataset Overview

• Total Transactions: 13,515 records analyzed
• Time Period: June-July 2024
• Data Points: 12 key metrics per transaction

Key Metrics

Check the code for detailed Analysis

Transaction Statistics

• Total Transaction Amount: ₹964,850
• Average Transaction: ₹154.57
• Median Transaction: ₹30.00
• Transaction Range: ₹1.0 - ₹3000.0

User Segmentation

• Analysis of Guruji vs Non-Guruji users
• Payment mode preferences (App, iOS, Web)
• Gateway utilization patterns

Payment Analysis

• Primary Gateway: Razorpay
• Transaction Modes:
  • Mobile App
  • iOS Platform
  • Web Interface
• GST Analysis: Detailed tax implications

📊 Visualizations

1. User Retention by Cohort

• [0,1,2] indicate first , second and third months from the start date

Key Insights:

• Shows how well different user cohorts are retained over time
• Darker colors indicate higher retention rates
• Helps identify which user acquisition periods produced the most loyal customers
• Reveals patterns in user engagement and loyalty across different time periods

2. Average Transaction Amount by Cohort

• [0,1,2] indicate first , second and third months from the start date

Key Insights:

• Displays the distribution of transaction amounts across different user cohorts
• Box plots show median, quartiles, and outliers for each cohort
• Helps identify trends in spending patterns over time
• Reveals which cohorts have higher average transaction values

3. Customer Segmentation Analysis

Key Insights:

• Visualizes customer segments based on recency, frequency, and monetary value
• Scatter plot shows relationship between customer recency and transaction amounts
• Color intensity indicates customer value score
• Helps identify high-value customers and their behavioral patterns

4. Average Transaction Value per Segment

Key Insights:

• Shows the average transaction values across different customer segments
• Helps identify most valuable customer segments
• Reveals spending patterns across different user groups
• Useful for targeting and personalization strategies

Check the code for detailed Analysis and key recommendations

💡 Recommendations Based on Analysis

User Engagement Strategy

1. Cohort-Based Targeting

   • Focus on replicating success factors from high-retention cohorts
   • Develop specific engagement strategies for different user segments
   • Implement early intervention for cohorts showing lower retention

2. Value-Based Segmentation

   • Tailor services and communications based on customer value segments
   • Create targeted upgrade paths for promising segments
   • Design retention programs for high-value customers

Transaction Optimization

1. Pricing Strategy

   • Optimize pricing based on cohort spending patterns
   • Create segment-specific offers and packages
   • Develop value-added services for high-spending segments

2. Customer Journey Enhancement

   • Focus on converting users to higher-value segments
   • Improve experience for segments showing growth potential
   • Implement loyalty programs based on segment characteristics

🎯 Next Steps

1. Short-term Actions

   • Implement segment-specific engagement campaigns
   • Optimize pricing for different user segments
   • Enhance retention strategies for valuable cohorts

2. Medium-term Goals

   • Develop predictive models for customer value
   • Create automated segment-based marketing programs
   • Implement personalized user experiences

3. Long-term Strategy

   • Build advanced customer lifetime value models
   • Develop AI-driven personalization
   • Create segment-specific product offerings

🛠️ Technical Implementation

Technologies Used

• Python: Primary programming language
• Libraries:
  • Pandas: Data manipulation and analysis
  • NumPy: Numerical computations
  • Matplotlib & Seaborn: Data visualization
  • Jupyter Notebook: Development environment

Methodology

1. Data Collection & Cleaning
2. Exploratory Data Analysis
3. Statistical Analysis
4. Visualization Generation
5. Insight Extraction

Check the code for detailed recommendations

🔗 Resources

• open the code in collab

🚀 Getting Started

1. Clone the repository

git clone [repository-url]

2. Run Jupyter Notebook

jupyter notebook "Deep Analysis.ipynb"

📫 Contact

For questions or feedback about this analysis, please contact:

• LinkedIn: [https://www.linkedin.com/in/mahikshith]