Jewellery E-commerce Data Scraping: New Designs, Prices and Bestsellers with Jewelry Data Scraping

Introduction

Jewellery is one of the hardest categories to benchmark online. Two rings can look almost identical in a thumbnail but differ in metal purity, stone quality, weight and making charges, which explains a price gap of several hundred dollars. At the same time, design trends move quickly: a new pendant style can go from Instagram to every competitor's homepage within weeks.

In 2026, iWeb Data Scraping received jewellery requests from both sides of the world. A US jewellery store owner wanted "sales data on competitors, to see the trends and new designs." An Indian brand wanted e-commerce data extraction from jewellery websites, "covering in-depth product details, images and videos." This guide explains how Jewelry Data Scraping answers these questions and what makes jewellery data different from other retail data.

What Jewellery Brands Want to Know

• What is new? Which designs competitors launched this week, and in which collections.
• What is selling? Which products show bestseller labels, low stock or rising reviews.
• How is it priced? Price per gram, making charges, discount depth and price bands by category.
• What is trending? Popular metals, stones, colours and styles across the market.
• How is it presented? Image styles, video use, model shots and description length.

Why Jewellery Data Is Different

A useful jewelry data scraping feed captures these attributes as separate fields, not just a title and a price.

Fields to Collect

Sample Record

The record below is illustrative.

{
  "brand": "Example Jewels",
  "product_id": "EJ-RG-10442",
  "title": "Solitaire Halo Engagement Ring",
  "category": "Ring",
  "collection": "Everlasting",
  "metal_type": "Gold",
  "metal_purity": "18K",
  "metal_colour": "White",
  "net_weight_g": 3.2,
  "stone_type": "Diamond",
  "stone_origin": "Lab-grown",
  "centre_stone_carat": 1.0,
  "total_stones": 19,
  "price_usd": 1890,
  "original_price_usd": 2290,
  "discount_pct": 17.5,
  "labels": ["Bestseller"],
  "rating": 4.8,
  "review_count": 312,
  "image_count": 7,
  "video_available": true,
  "certificate": "IGI",
  "first_seen": "2026-08-21",
  "collected_at": "2026-10-06"
}

Tracking New Designs

New-design tracking is the most requested jewellery use case. It works by comparing each collection with the previous one:

1. Baseline. Collect every product on each competitor site.

2. Daily or weekly diff. Identify product IDs that did not exist before.

3. Classify. Assign category, metal, stone and style tags to each new product.

4. Summarise. Send a weekly "new arrivals" report with images and key attributes.

Style tags such as "halo", "solitaire", "tennis", "huggie", "paperclip chain" or "temple jewellery" are added using the product title, description and, where helpful, image analysis. This turns hundreds of new listings into a readable trend summary.

Estimating What Is Selling

Most jewellery sites do not publish sales numbers. Jewelry data scraping uses public signals instead:

• Bestseller and trending labels where the site shows them.
• Review count growth week over week.
• Low-stock messages and variants going out of stock.
• Position in "sort by popularity" listings.
• Homepage and collection placement, which shows what the brand is promoting.

Combined, these signals give a directional view of demand. They should be presented as estimates, not reported sales.

Pricing Analysis in India: Weight and Making Charges

Indian jewellery listings often show a price breakup: metal value (weight × gold rate), making charges, stone value and tax. Collecting these components allows a fair comparison between brands:

All values illustrative for a 10 g necklace.

Because gold rates change daily, prices should be compared on the same collection date or normalised to a fixed gold rate.

Images and Videos

Product media matter in jewellery. iWeb Data Scraping collects image and video URLs, counts and formats so brands can benchmark presentation, for example "competitors average 7 images and 60% have video." Image files themselves remain the property of the original sites; links are provided for analysis, not for reuse.

US versus India: Two Different Jewellery Markets

Any jewelry data scraping project should reflect these differences. In India, collecting the price breakup and the hallmark status is essential. In the US, collecting certificate type and discount history matters more.

A Simple Weekly Report for Merchandisers

Most jewellery teams do not need a complex dashboard. A weekly email or PDF with four sections is often enough:

5. New arrivals by competitor, with images and key attributes.

6. Price moves, showing products whose price changed by more than 5%.

7. Bestseller changes, listing products that gained or lost bestseller labels.

8. Trend summary, with the top styles, metals and stones among new launches.

Who Uses Jewellery Data

• D2C jewellery brands planning new collections and pricing.
• Retail chains comparing their online range with competitors.
• Marketplaces benchmarking sellers and spotting trending categories.
• Lab-grown diamond brands tracking the shift from natural stones.
• Investors and analysts studying online jewellery growth.

Key Takeaways

• Jewellery pricing depends on metal, purity, weight, stones and making charges, so these must be separate fields.
• Jewelry data scraping turns competitor catalogues into weekly new-design and trend reports.
• Sales are estimated from public signals, not reported figures.
• Media benchmarks help brands improve how products are presented online.

Get a jewellery competitor dataset. iWeb Data Scraping delivers AI-ready jewelry data scraping feeds for brands and retailers in the US, India and beyond. Share your competitor list and categories, and we will send a sample new-arrivals and pricing report.

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