Graphtreon Scraper

Graphtreon Scraper is a high-performance data extraction tool that collects creator earnings, subscriber counts, rankings, and long-term growth trends from Graphtreon. It helps analysts and researchers turn public creator economy data into structured datasets for insights, forecasting, and competitive analysis.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

Graphtreon Scraper extracts detailed creator analytics and historical performance data from Graphtreon profiles. It solves the challenge of manually tracking creator growth, monetization, and rankings over time. This project is built for analysts, marketers, investors, and creators who need reliable creator economy intelligence.

Creator Economy Intelligence at Scale

• Collects financial, audience, and ranking metrics in a single run
• Supports historical daily and monthly trend analysis
• Designed for bulk creator analysis with consistent data structures
• Produces analytics-ready datasets for reporting and modeling

Features

Feature	Description
Creator Profile Extraction	Retrieves core metadata such as creator name, category, URLs, and profile image.
Earnings & Revenue Metrics	Captures estimated monthly earnings, earnings per patron, and currency details.
Subscriber Analytics	Extracts total and paid member counts with growth indicators.
Ranking Insights	Provides overall and category-based rankings with historical changes.
Historical Trend Tracking	Collects daily and monthly time-series data for long-term analysis.
Scalable Processing	Handles large creator lists efficiently with stable performance.

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What Data This Scraper Extracts

Field Name	Field Description
creator_id	Unique Graphtreon identifier for the creator.
creator_name	Display name of the creator.
created_at	Account creation date when available.
patreon_url	Direct link to the creator’s Patreon page.
graphtreon_url	Direct link to the Graphtreon profile.
total_members	Total number of members.
paid_members	Number of paying subscribers.
earnings_per_patron	Average monthly revenue per patron.
earnings_per_month	Estimated total monthly earnings.
patreon_rank	Overall ranking across the platform.
category	Creator content category.
category_rank	Ranking within the category.
paid_members_daily	Daily historical subscriber counts.
paid_members_monthly	Monthly historical subscriber counts.
earnings_daily	Daily historical earnings estimates.
earnings_per_patron_monthly	Monthly revenue per patron trends.

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Example Output

[
    {
        "creator_id": "theyard",
        "creator_name": "the yard",
        "category": "Podcasts",
        "total_members": 66165,
        "paid_members": 42148,
        "earnings_per_patron": 6.69,
        "earnings_per_month": 282095,
        "earnings_currency": "USD",
        "patreon_rank": 7,
        "category_rank": 4,
        "is_hot_creator": true,
        "profile_image_url": "https://c10.patreonusercontent.com/sample.jpg"
    }
]

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Directory Structure Tree

Graphtreon Scraper/
├── src/
│   ├── main.py
│   ├── collectors/
│   │   ├── creator_profile.py
│   │   ├── earnings_history.py
│   │   └── rankings.py
│   ├── processors/
│   │   ├── timeseries_builder.py
│   │   └── data_normalizer.py
│   ├── exporters/
│   │   ├── json_exporter.py
│   │   ├── csv_exporter.py
│   │   └── xlsx_exporter.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── input.sample.json
│   └── output.sample.json
├── requirements.txt
└── README.md

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Use Cases

• Marketing teams use it to benchmark creators and identify high-performing partnership opportunities.
• Data analysts use it to model revenue growth and audience trends across creator categories.
• Investors use it to evaluate creator sustainability and long-term monetization potential.
• Content strategists use it to study competitor performance and category dynamics.

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FAQs

What inputs are required to run the scraper? You can provide creator usernames or profile URLs. The scraper resolves them and collects all available analytics automatically.

Can I limit how much historical data is collected? Yes, you can specify a custom timespan such as days, months, or years to control historical depth.

Does it support bulk creator analysis? The scraper is designed for bulk processing and can handle large creator lists efficiently.

What formats can the data be exported in? Data can be exported in structured formats suitable for analytics workflows, including JSON, CSV, and spreadsheet-compatible outputs.

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Performance Benchmarks and Results

Primary Metric: Processes hundreds of creator profiles per run with consistent extraction speed.

Reliability Metric: Maintains a high success rate across long historical time ranges.

Efficiency Metric: Optimized data collection minimizes redundant requests and resource usage.

Quality Metric: Delivers complete, normalized datasets with accurate historical time-series coverage.

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