We extract product specifications, variant mappings, pricing signals, bundle promotions, and store location data from Lovisa. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from lovisa.com. All fields typed and schema-versioned.
"sku": "51428931", "title": "Gold Plated Sterling Silver Cubic Zirconia Huggie Earrings", "metal_type": "Sterling Silver", "colour": "Gold", "material": "Cubic Zirconia", "style": "Huggie", "image_urls": "['https://cdn.shopify.com/s/files/1/0014/1234/products/51428931_1.jpg']"
| # | sku | title | description | metal_type | colour | material |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Variants objects from lovisa.com. All fields typed and schema-versioned.
"sku": "51428931", "variant_id": "39481726351", "size": "One Size", "price": 25.99, "currency": "USD", "bundle_eligible": true, "in_stock": true
| # | sku | variant_id | size | price | compare_at_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Collections objects from lovisa.com. All fields typed and schema-versioned.
"category_id": "earrings-huggie", "name": "Huggie Earrings", "breadcrumb": "Home > Jewellery > Earrings > Huggie Earrings", "url": "/collections/huggie-earrings", "product_count": 214, "trend_label": "Everyday Essentials"
| # | category_id | name | breadcrumb | parent_category | url | product_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from lovisa.com. All fields typed and schema-versioned.
"review_id": "rev_847192", "sku": "51428931", "rating": 5, "author": "Sarah M.", "date": "2023-10-14", "verified_buyer": true
| # | review_id | sku | rating | author | date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from lovisa.com. All fields typed and schema-versioned.
"store_id": "US_104", "name": "Lovisa Mall of America", "city": "Bloomington", "country": "US", "piercing_available": true, "latitude": 44.8548, "longitude": -93.2422
| # | store_id | name | address | city | country | phone |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Lovisa scraper navigates the fast fashion lifecycle: rapid SKU turnover, complex promotional pricing logic, multi-region storefronts, and piercing studio directories.
Title, materials, dimensions, plating types, and descriptive copy extracted directly from the product detail pages.
Map complex variant structures including metal colours (rose gold, silver, gold) and ring sizes to parent SKUs.
Capture complex bundle logic, multi-buy discounts (e.g., 5 for $15), and clearance tag events across the catalogue.
Extract localized pricing and availability across lovisa.com, .com.au, .co.uk, and .za storefronts.
Track in-stock flags, out-of-stock variations, and low inventory warnings at the variant level.
Extract store coordinates, trading hours, and piercing service availability from the global directory.
Monitor trend edits, seasonal drops, and collaborations to see how products are merchandised.
Run daily or hourly pipelines to capture rapid price changes and clearance markdowns typical of fast fashion.
Extract high-resolution gallery URLs, model shots, and flat-lay imagery for every variant.
Brief in. Clean data out.
Select target regions, categories, or specific collections. We design the schema to match your data requirements.
We configure Playwright crawlers, handle regional proxy routing, and map the Shopify variant JSON structures.
Schema validation, null-rate checks, and promotional logic verification before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed cadence.
Extracting data from high-turnover fashion sites requires specific handling for dynamic inventory, variant structures, and localized pricing.
Fast fashion sites remove dead SKUs entirely rather than marking them out of stock. Our pipeline detects 404s and gracefully updates historical records without breaking the current extraction run.
Lovisa forces regional redirects based on IP geolocation. We use country-specific residential proxies to ensure we capture accurate local pricing and inventory for AU, US, UK, and ZA markets.
Lovisa relies on complex Shopify variant structures for ring sizes and metal platings. We extract data directly from the embedded JSON payload rather than relying solely on DOM parsing, ensuring 100% variant accuracy.
Promotions like 'Buy 3 get 2 free' or '5 for $15' are often stored as raw text tags. We parse these string values into structured discount boolean flags and promotional categories.
To monitor clearance events, we run hourly checks against a hash index of last-seen prices. You only receive data when a price drops or a variant goes out of stock, saving compute and storage costs.
Fashion retailers track Lovisa's price points, clearance velocity, and bundle structures to optimise their own promotional calendars.
Merchandisers analyse new material introductions, colour dominances, and style categorisations to predict seasonal jewellery trends.
Pricing teams monitor the frequency and depth of multi-buy deals to understand fast fashion margin strategies.
Real estate analysts track store density and piercing service rollouts to evaluate market penetration and physical expansion.
Category managers compare SKU counts across departments (e.g., earrings vs necklaces) to understand Lovisa's inventory weighting.
Brands evaluate localized pricing across global markets to determine pricing parity and regional markup strategies.
"Fast fashion jewellery relies on high SKU turnover and complex bundle pricing. Tracking this manually is impossible; you need an automated pipeline to capture the daily catalogue shifts."
Lovisa's catalogue changes rapidly with seasonal trends and clearance cycles. Extracting accurate pricing requires navigating regional storefronts, parsing bundle promotions, and mapping complex variant trees for ring sizes and metal platings. DataFlirt manages this extraction layer entirely.
Everything supported by our lovisa.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering for dynamic pricing and localized inventory checks.
Country-specific residential proxies ensure we bypass geographic redirects and capture accurate local pricing for every targeted region.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About lovisa.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We parse raw promotional text tags (e.g., '3 for $10') into structured boolean fields and discount categories, allowing you to analyse promotional frequency programmatically.
Lovisa uses IP geolocation to force region-specific storefronts. We route requests through residential proxies located in the target country (AU, US, UK, ZA) to capture the correct local currency and inventory state.
Yes. We extract the full store directory, including physical addresses, geographic coordinates, trading hours, and boolean flags for piercing service availability.
For fast fashion, we typically configure daily full-catalogue refreshes. We can also configure hourly pipelines for specific high-priority categories to track clearance velocity.
We capture the exact stock status per variant. If an item is entirely removed from the site (returns a 404), our change detection system logs the item as discontinued without failing the pipeline.
Yes. We extract data from the underlying Shopify variant JSON payload, ensuring that every child SKU is accurately mapped to its parent product with the correct size and material attributes.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across multiple regions - we scope, build, and operate the pipeline. Tell us what you need.
