https://github.com/mahikshith/user-retention-segementation-analysis-for-a-product-based-company

Cohort Analysis + Revenue growth Analysis + Customer segmentation for Target Marketing + User retention
https://github.com/mahikshith/user-retention-segementation-analysis-for-a-product-based-company

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

• Host: GitHub
• URL: https://github.com/mahikshith/user-retention-segementation-analysis-for-a-product-based-company
• Owner: mahikshith
• License: gpl-3.0
• Created: 2024-12-08T17:55:18.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-08T18:05:44.000Z (about 1 month ago)
• Last Synced: 2024-12-08T18:32:05.211Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
## 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

![User Retention Analysis](assets/user_retention_by_cohort.png)

- [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

![Average Transaction Analysis](assets/Average_transaction_amount_cohort.png)

- [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

![Customer Segmentation](assets/Customer_segmentation_analysis.png)

**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

![Transaction Value by Segment](assets/average_transaction_value_per_segement.png)

**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](https://drive.google.com/file/d/1ajyspG5ZHpMSWG_uOXonEWJpZzHmB1VA/view?usp=sharing)

## 🚀 Getting Started

1. Clone the repository

```bash

git clone [repository-url]

```

2. Run Jupyter Notebook

```bash

jupyter notebook "Deep Analysis.ipynb"

```

## 📫 Contact

For questions or feedback about this analysis, please contact:

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