Patreon Analytics Scraper

Patreon Analytics Scraper collects structured insights about top Patreon creators, turning raw public data into clear, actionable analytics. It helps teams understand earnings, membership trends, and growth signals without manual research. Built for accuracy and consistency, it supports data-driven decisions around the creator economy.

     

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

This project extracts detailed analytics for leading Patreon creators and organizes them into a clean, analysis-ready format. It solves the problem of fragmented, time-consuming creator research by centralizing key metrics in one dataset. It’s designed for analysts, marketers, investors, and teams tracking the creator economy.

Creator Economy Insights

• Tracks earnings and member counts across top creators
• Captures short-term and historical growth changes
• Normalizes ranking data for easy comparison
• Includes timestamps for trend and time-series analysis

Features

Feature	Description
Multiple ranking views	Supports rankings by paid members, free members, earnings, and growth rate.
Earnings analytics	Captures monthly earnings with currency and period context.
Membership metrics	Separates paid and free member counts for clarity.
Growth tracking	Includes change metrics over defined historical periods.
Creator metadata	Extracts name, description, profile image, and direct Patreon URL.
Structured output	Produces clean, consistent records ready for analysis or storage.

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What Data This Scraper Extracts

Field Name	Field Description
creatorName	Name of the Patreon creator or campaign.
description	Short description of the creator’s content.
profileImage	URL to the creator’s profile image.
creatorUrl	Direct link to the creator’s Patreon page.
scrapedAt	Timestamp indicating when the record was collected.
statistics.earnings.amount	Monthly earnings amount.
statistics.earnings.currency	Currency of reported earnings.
statistics.earnings.period	Earnings period, typically monthly.
statistics.paidMembers	Number of active paid members.
statistics.change	Historical change metrics for earnings and members.

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Example Output

[
  {
    "creatorName": "the yard",
    "description": "Creating a podcast",
    "profileImage": "https://c10.patreonusercontent.com/4/patreon-media/p/campaign/6742942/ac9df56840454ad49fccba1f44c2b1fa/4.jpg",
    "creatorUrl": "https://www.patreon.com/theyard",
    "scrapedAt": "2024-12-12T07:52:25.642Z",
    "statistics": {
      "earnings": {
        "amount": 239344,
        "currency": "USD",
        "period": "monthly"
      },
      "paidMembers": 36292,
      "change": {
        "earnings": 11517,
        "members": 969,
        "period": "3 month change"
      }
    }
  }
]

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Directory Structure Tree

Patreon Analytics/
├── src/
│   ├── main.py
│   ├── collectors/
│   │   ├── rankings.py
│   │   └── creator_stats.py
│   ├── processors/
│   │   ├── normalizer.py
│   │   └── trends.py
│   ├── utils/
│   │   ├── dates.py
│   │   └── helpers.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── samples/
│   │   └── creators.sample.json
│   └── outputs/
│       └── latest.json
├── requirements.txt
└── README.md

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Use Cases

• Market analysts use it to compare top creators, so they can spot revenue and membership trends faster.
• Content strategists use it to study growth patterns, so they can refine audience and monetization strategies.
• Investors use it to evaluate creator performance, so they can assess opportunities in the creator economy.
• Agencies use it to benchmark clients against peers, so they can justify strategy and pricing decisions.

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FAQs

What ranking types are supported? The scraper supports rankings by paid members, free members, total earnings, and growth rate, allowing flexible analysis depending on your goals.

How often is the data updated? Each run captures current rankings and metrics, with timestamps included so you can track freshness and changes over time.

Does it include historical comparisons? Yes, change metrics are included to show earnings and member growth over defined periods, enabling trend analysis.

Are all creator details always available? Some creators limit public information, so certain optional fields may be missing depending on availability.

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Performance Benchmarks and Results

Primary Metric: Processes several hundred creator profiles per run with consistent field coverage.

Reliability Metric: Maintains a high success rate across ranking categories with stable repeat runs.

Efficiency Metric: Optimized resource usage keeps runtime predictable even as ranking depth increases.

Quality Metric: Data completeness remains high, with normalized fields ready for direct analysis.

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