Fiverr Analytics SQL

A lightweight Python project to analyze Fiverr order data and generate insightful reports and visualizations.

Features

• Loads data from CSV files into an SQLite database
• Generates rich reports (text-based and plots)
• Includes prebuilt scripts for:
  • Account performance
  • Revenue trends
  • Delivery analysis
  • Category insights
• Saves results to outputs/ folder automatically
• Customizable date range support
• Gmail email extractor (Google Apps Script)

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Project Structure

fiverr_analytics_sql/
│
├── build_database.py          # Main pipeline to create DB & generate outputs
├── generate_reports.py        # Report & visualization generation logic
├── data/                      # Input data (converted from Gmail extractor)
│   ├── accounts.csv
│   ├── orders.csv
│   ├── extras.csv
│   └── packages.csv
│
├── data_template/             # CSV templates for manual input
│   ├── accounts_template.csv
│   ├── orders_template.csv
│   ├── extras_template.csv
│   └── packages_template.csv
│
├── outputs/                   # Auto-generated reports and plots
│   ├── report_<timestamp>.txt
│   └── plots/
│       └── <plots>.png
│
├── email_extractor/           # Google Apps Script for Gmail parsing
│   └── extractor.gs
│
├── fiverr.db                  # Auto-created SQLite database
├── requirements.txt           # Required packages
└── README.md                  # This file

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Installation

1. Clone the repo or download the ZIP
2. Install dependencies:

pip install -r requirements.txt

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How to Run

python build_database.py

This will:

• Create the database (fiverr.db)
• Import all CSV data
• Generate summary reports
• Create charts in outputs/plots/

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Gmail Data Extractor

Use the Apps Script in email_extractor/extractor.gs to extract Fiverr order confirmation data from your Gmail inbox.

Full instructions are in email_extractor/README.md

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Formatting the Extracted Data

After using the extractor, you'll get raw Fiverr order details in a Google Sheet.

Before loading into the SQLite database, you must manually:

Clean and format the data to match the structure of the templates in data_template/

Export them as UTF-8 CSV files using semicolons (;) as delimiters

Save the finalized versions into the data/ folder:

accounts.csv

orders.csv

extras.csv

packages.csv

If column names or formats don't match, the database import will fail.

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Reports Included

• Total order summary
• Account activity
• Revenue by service/category
• Ratings distribution
• Monthly trends
• Delayed delivery stats
• Top buyers / top earners
• Pie chart of account statuses
• Rolling average revenue
• More...

All text reports saved to outputs/report_<timestamp>.txt.

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Customization

• Replace CSVs in data/
• Use your own date range in generate_all_reports(...)
• Add more queries or plots in generate_reports.py

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Requirements

• Python 3.8+
• pandas
• matplotlib
• seaborn

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Feedback & Contributions

This is a personal project built for portfolio/data practice purposes.

Feel free to fork or suggest improvements!