# pavangowda547/customer-retention architecture

> Architecture diagram of the GitHub repository pavangowda547/customer-retention, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-09). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/pavangowda547/customer-retention
- Repository: https://github.com/pavangowda547/customer-retention

## Overview

This repository presents a customer-retention analysis: customer data is examined for churn patterns, summarized into metrics and visualizations, and used to inform retention recommendations. The README describes data cleaning, trend analysis, and dashboards, and lists Python, SQL, Excel, and Power BI/Tableau as technologies. The tree includes a dataset workbook, analysis notebook, SQL file, and Power BI report. No source excerpts were provided, so links between these artifacts are shown only where documented or marked unverified.

Main entry points: [`customer_retention.ipynb`](https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.ipynb) (Retention trends), [`Churn Retention.pbix`](https://github.com/pavangowda547/customer-retention/blob/main/Churn%20Retention.pbix) (Retention dashboard), [`customer_retention.sql`](https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.sql) (Metrics and aggregation), [`Churn_Dataset.xlsx`](https://github.com/pavangowda547/customer-retention/blob/main/Churn_Dataset.xlsx) (Customer dataset)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_inputs["Customer data"]
  node_dataset[("Customer dataset<br/>[Churn_Dataset.xlsx]")]
end

subgraph group_analysis["Retention analysis"]
  node_cleaning["Data cleaning"]
  node_trends["Retention trends"]
  node_metrics["Metrics and aggregation"]
end

subgraph group_reporting["Insights and reporting"]
  node_recommendations["Retention recommendations<br/>[README.md]"]
  node_dashboard["Retention dashboard"]
end

node_analyst(("Business analyst"))

node_dataset -.->|"provides data"| node_cleaning
node_cleaning -->|"prepares analysis"| node_trends
node_trends -.->|"summarizes metrics"| node_metrics
node_trends -.->|"visualizes findings"| node_dashboard
node_metrics -.->|"supports reporting"| node_dashboard
node_dashboard -.->|"presents insights"| node_analyst
node_trends -->|"informs strategy"| node_recommendations

click node_dataset "https://github.com/pavangowda547/customer-retention/blob/main/Churn_Dataset.xlsx"
click node_cleaning "https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.ipynb"
click node_trends "https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.ipynb"
click node_metrics "https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.sql"
click node_recommendations "https://github.com/pavangowda547/customer-retention/blob/main/README.md"
click node_dashboard "https://github.com/pavangowda547/customer-retention/blob/main/Churn%20Retention.pbix"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dataset toneBlue
class node_cleaning,node_trends,node_metrics toneAmber
class node_recommendations,node_dashboard toneMint
class node_analyst toneIndigo
```

## Components

### Customer data

- [Customer dataset](https://github.com/pavangowda547/customer-retention/blob/main/Churn_Dataset.xlsx): `Churn_Dataset.xlsx`

### Retention analysis

- [Data cleaning](https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.ipynb): `customer_retention.ipynb`
- [Retention trends](https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.ipynb): `customer_retention.ipynb`
- [Metrics and aggregation](https://github.com/pavangowda547/customer-retention/blob/main/customer_retention.sql): `customer_retention.sql`

### Insights and reporting

- [Retention recommendations](https://github.com/pavangowda547/customer-retention/blob/main/README.md): `README.md`
- [Retention dashboard](https://github.com/pavangowda547/customer-retention/blob/main/Churn%20Retention.pbix): `Churn Retention.pbix`

### Other components

- **Business analyst**

## Connections

- Customer dataset → Data cleaning: provides data
- Data cleaning → Retention trends: prepares analysis (evidence: `README.md`)
- Retention trends → Metrics and aggregation: summarizes metrics
- Retention trends → Retention dashboard: visualizes findings
- Metrics and aggregation → Retention dashboard: supports reporting
- Retention dashboard → Business analyst: presents insights
- Retention trends → Retention recommendations: informs strategy (evidence: `README.md`)