We extract fragrance catalogues, scent profiles, brand portfolios, and review corpora from Scentbird. 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 Fragrance Catalogue objects from scentbird.com. All fields typed and schema-versioned.
"fragrance_id": "sb_94821", "name": "Santal 33", "brand_name": "Le Labo", "gender_target": "Unisex", "rating": 4.6, "review_count": 14205, "full_price": 230.0, "subscription_eligible": true
| # | fragrance_id | name | brand_name | gender_target | description | main_accords |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Scent Notes objects from scentbird.com. All fields typed and schema-versioned.
"fragrance_id": "sb_94821", "top_notes": "['Violet', 'Cardamom']", "middle_notes": "['Iris', 'Ambrox']", "base_notes": "['Cedarwood', 'Leather', 'Sandalwood']", "scent_family": "Woody", "season_tags": "['Fall', 'Winter']"
| # | fragrance_id | top_notes | middle_notes | base_notes | scent_family | season_tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Brand Profiles objects from scentbird.com. All fields typed and schema-versioned.
"brand_id": "br_104", "name": "Le Labo", "country_of_origin": "USA", "founded_year": 2006, "fragrance_count": 19, "average_rating": 4.5
| # | brand_id | name | description | country_of_origin | founded_year | fragrance_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for User Reviews objects from scentbird.com. All fields typed and schema-versioned.
"review_id": "rv_8849201", "fragrance_id": "sb_94821", "star_rating": 5, "review_text": "Signature scent. Lasts 10+ hours on skin.", "longevity_rating": "Long Lasting", "sillage_rating": "Strong"
| # | review_id | fragrance_id | user_id | star_rating | review_text | longevity_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Recommendations objects from scentbird.com. All fields typed and schema-versioned.
"source_fragrance_id": "sb_94821", "similar_fragrance_ids": "['sb_1102', 'sb_4491']", "algorithm_score": 0.92, "users_also_liked": "['Maison Margiela Jazz Club', 'Byredo Gypsy Water']", "alternative_brands": "['Maison Margiela', 'Byredo']", "scent_family_match": "Woody"
| # | source_fragrance_id | similar_fragrance_ids | matching_notes | algorithm_score | users_also_liked | alternative_brands |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Scentbird scraper parses complex scent profiles, brand metadata, and user reviews, handling dynamic React rendering and infinite scroll pagination automatically.
Extract every perfume and cologne listing, including title, brand, description, and high-resolution bottle imagery.
Parse top, middle, and base notes alongside scent family classifications and complexity scores for precise taxonomy mapping.
Aggregate fragrance counts, average ratings, and historical metadata for every brand listed on the platform.
Capture user reviews with specific metrics for longevity and sillage, paginated across thousands of entries.
Track subscription eligibility, premium upcharges, and full retail bottle pricing across the catalogue.
Extract recommendation algorithms and 'users also liked' data to build competitor similarity graphs.
Collect metadata on recommended seasons (Fall, Summer) and occasions (Date Night, Office) for each fragrance.
Run continuous pipelines to track new fragrance additions, rating changes, and review volume velocity.
Optimised Playwright routines handle React hydration quickly, ensuring fast turnaround for complete catalogue sweeps.
Brief in. Clean data out.
Specify target brands, scent families, or full catalogue extraction requirements. We map the schema.
We configure crawlers to handle Scentbird's SPA architecture, pagination, and anti-bot protections.
Data is checked for schema compliance, note taxonomy completeness, and review pagination depth.
Clean JSON, CSV, or Parquet records are pushed to your warehouse on your specified schedule.
Modern SPAs require sophisticated rendering and proxy strategies. Here is how we maintain reliable pipelines.
Scentbird relies heavily on client-side rendering. We use Playwright to execute JavaScript, wait for network idle states, and capture data only after scent notes and pricing widgets fully hydrate.
User reviews and category pages use infinite scroll rather than standard pagination. Our crawlers simulate human scrolling behaviour to trigger XHR requests, capturing the complete review corpus without missing records.
To avoid rate limits and IP bans, we route requests through residential proxy pools, rotating IPs and spoofing TLS fingerprints to mimic legitimate consumer traffic.
Frontend frameworks generate dynamic class names. We rely on XPath structural patterns and intercepted API responses rather than brittle CSS selectors, ensuring pipelines survive UI updates.
For ongoing monitoring, we hash fragrance records and only deliver diffs, highlighting new reviews, rating shifts, or catalogue additions without redundant data transfer.
Fragrance houses monitor Scentbird curation to benchmark brand visibility, pricing, and consumer ratings against competitors.
Market researchers analyse the velocity of specific scent notes (e.g., Oud, Vanilla) to predict upcoming seasonal fragrance trends.
Machine learning teams use structured top/mid/base note taxonomies and user rating correlations to train predictive scent recommendation engines.
Retailers track subscription upcharges versus full retail bottle prices to optimise their own direct-to-consumer pricing models.
Brands mine review text and specific longevity/sillage ratings to understand product performance and reformulate underperforming scents.
Analysts compare the review volume and rating trajectories of niche indie brands versus established designer houses.
"Scentbird holds the most structured taxonomy of fragrance notes and consumer scent preferences on the web - but querying it requires dedicated extraction infrastructure."
Extracting Scentbird's catalogue demands handling heavy JavaScript payloads, infinite scroll review pagination, and dynamic pricing models. DataFlirt manages the residential proxies and DOM parsing, delivering clean fragrance graphs directly to your warehouse so your team can focus on trend analysis.
Everything supported by our scentbird.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, while Playwright manages JavaScript rendering and infinite scroll interactions required by Scentbird's frontend.
We route requests through high-quality residential proxy pools, rotating IPs per request to mimic genuine consumer traffic and bypass bot detection.
Pipelines are deployed on Kubernetes and AWS Lambda, orchestrated by Apache Airflow for strict scheduling, dependency management, and SLA compliance.
Data delivered to where your team already works — no new tooling required.
About scentbird.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible catalogue and review data is generally permissible. DataFlirt extracts only unauthenticated, public information. We do not bypass login walls to extract personal subscriber data or private queues. Clients must consult their legal counsel regarding specific data usage.
We utilise Playwright to execute a headless browser environment, allowing React components to hydrate fully before we extract the DOM or intercept the underlying JSON API responses.
Yes. Our crawlers handle the infinite scroll pagination logic to load and extract the complete history of reviews, including specific ratings for longevity and sillage.
Yes. We capture the structured taxonomy of top, middle, and base notes, along with scent family classifications and recommended seasons.
We typically scope engagements starting with full catalogue extraction (all brands and fragrances) delivered on a weekly or monthly cadence. Contact us for volume-based pricing.
Yes. We can provide a sample extraction of 50-100 fragrances, including their associated notes and a subset of reviews, to validate schema compliance before contract signature.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous tracking of fragrance trends and reviews - we handle the infrastructure. Tell us your requirements.
