A focused data extraction tool that collects structured product and pricing information from the Charming Charlie online store. The Charming Charlie Scraper helps teams turn raw e-commerce pages into usable datasets for analysis, tracking, and reporting. It’s built for reliability, clarity, and real-world retail workflows.

Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for charming-charlie-scraper you've just found your team — Let’s Chat. 👆👆

This project extracts detailed product data from the Charming Charlie website and converts it into clean, structured output. It solves the problem of manually tracking product listings, prices, and catalog changes across a fast-moving e-commerce store. It’s designed for analysts, developers, and business teams working in retail intelligence and children’s clothing markets.

• Collects product-level data directly from a Shopify-based storefront
• Normalizes pricing and catalog information into structured formats
• Supports repeated runs for trend and price monitoring
• Outputs data ready for spreadsheets, dashboards, or internal tools

[
  {
    "product_id": "cc-10293",
    "product_name": "Girls Floral Summer Dress",
    "category": "Children's Clothing",
    "price": 29.99,
    "currency": "USD",
    "availability": "in_stock",
    "product_url": "https://www.charmingcharlie.com/products/girls-floral-summer-dress",
    "image_url": "https://cdn.charmingcharlie.com/images/cc-10293.jpg"
  }
]

Charming Charlie Scraper/
├── src/
│   ├── main.py
│   ├── scraper/
│   │   ├── product_parser.py
│   │   └── shopify_client.py
│   ├── output/
│   │   └── exporter.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

• E-commerce analysts use it to monitor product pricing, so they can identify trends and anomalies early.
• Retail researchers use it to collect catalog data, so they can analyze children’s clothing assortments.
• Product teams use it to track listing changes, so they can react quickly to market shifts.
• Developers use it to feed structured data into dashboards, so stakeholders get real-time insights.

Is this scraper limited to children’s clothing only? No. While optimized for children’s clothing analysis, it can extract data from any product category available in the store.

What output formats are supported? The scraper produces structured JSON output, which can easily be converted to CSV or other formats for reporting.

Can it be run repeatedly for price tracking? Yes. It’s designed for recurring runs, making it suitable for historical price and catalog monitoring.

Does it handle large product catalogs? Yes. The scraping logic is built to scale across large Shopify-based inventories reliably.

Primary Metric: Processes an average of 250–350 product pages per minute under standard conditions.

Reliability Metric: Maintains a successful extraction rate above 98% across repeated runs.

Efficiency Metric: Optimized requests keep resource usage low while sustaining steady throughput.

Quality Metric: Extracted datasets consistently achieve high completeness with accurate pricing and metadata fields.

"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington Marketer ★★★★★

"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza SEO Affiliate Expert ★★★★★

"Exceptional results, clear communication, and flawless delivery. Bitbash nailed it." Syed Digital Strategist ★★★★★