We extract apparel and beauty product listings, sale and clearance pricing, Star Rewards member pricing, size and colour variant data, brand intelligence, and review corpus from Macy's. 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 macys.com. All fields typed and schema-versioned.
"product_id": "MCY-16284039", "title": "Lauren Ralph Lauren Ponte Blazer — Navy", "brand": "Lauren Ralph Lauren", "department": "Women", "regular_price": 189.00, "sale_price": 113.40, "currency": "USD", "discount_pct": 40, "clearance_flag": false, "rating": 4.4, "review_count": 847
| # | product_id | title | brand | department | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Pricing & Sales Events objects from macys.com. All fields typed and schema-versioned.
"product_id": "MCY-16284039", "regular_price": 189.00, "sale_price": 113.40, "discount_pct": 40, "sale_event_name": "Friends & Family", "sale_end_date": "2026-05-19", "star_rewards_extra_pct": 5, "gift_with_purchase": false, "price_timestamp": "2026-05-12T09:00:00Z"
| # | product_id | regular_price | sale_price | discount_pct | discount_abs | clearance_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Size & Colour Variants objects from macys.com. All fields typed and schema-versioned.
"product_id": "MCY-16284039", "colour_name": "Lauren Navy", "colour_hex": "#1B2A4A", "size_label": "10", "size_type": "Missy", "fit_type": "Regular", "in_stock": true, "variant_sale_price": 113.40
| # | product_id | variant_id | colour_name | colour_hex | size_label | size_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from macys.com. All fields typed and schema-versioned.
"review_id": "mcy_rv_7284193", "product_id": "MCY-16284039", "star_rating": 5, "verified_purchase": true, "review_title": "Perfect office blazer — runs true to size", "fit_feedback": "True to size", "size_purchased": "10 Regular", "helpful_votes": 84, "review_date": "2026-04-15"
| # | review_id | product_id | reviewer_name | verified_purchase | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Macy's is a department store where product intelligence requires fashion-specific dimensions: full size and colour variant mapping, fit feedback from reviews, sale event classification, clearance depth tracking, and brand-level pricing across hundreds of national and house brands.
Title, brand, department, category, material, care instructions, fit type, and every metadata field Macy's surfaces — scraped at product and variant level.
All available colour options (with hex codes), size labels, size types (Petite, Regular, Plus, Tall), fit types, and per-variant stock status and pricing — fully mapped.
Capture regular price, sale price, discount percentage, sale event name (Friends & Family, One Day Sale, etc.), and sale end date — with clearance depth and flag per product.
Reviews on Macy's include structured fit feedback (Runs Small, True to Size, Runs Large) and size purchased — transforming reviews into fit-intelligence data for sizing algorithms.
Macy's carries 500+ brands across apparel, beauty, and home. We extract brand-level pricing, discount intensity, and promotional treatment — enabling brand-by-brand competitive analysis.
Full beauty product data: shade options, formula type, size variants, gift set structures, and GWP (gift with purchase) offers — across cosmetics, skincare, and fragrance.
Identify gift set products, their component items, gift-with-purchase offers, and promotional bundle pricing — critical for beauty category seasonal analysis.
Track product position for any keyword or department-level browse on Macy's — capturing sale badge, clearance flag, Star Rewards extra, and variant count in every result.
One-off catalogue exports or continuous pipelines at daily or real-time cadences — aligned to Macy's frequent sale event calendar.
Brief in. Clean data out.
Specify departments, brand lists, category paths, or product IDs. We design the extraction schema including variant mapping, sale events, and fit feedback fields.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and colour/size variant navigation for macys.com.
Variant completeness audits, sale event classification validation, fit feedback null-rate checks, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fashion e-commerce data complexity peaks at Macy's: dozens of sale event types, hundreds of brand pricing tiers, and thousands of colour-size variant combinations per product page.
Macy's fashion products can have 20+ colour options and 15+ size options, creating variant matrices of 100+ combinations. Our Playwright pipeline systematically navigates colour and size selectors on each product page — capturing per-variant stock status, price, and image URL rather than just the default-display variant.
Macy's runs dozens of named sale events annually — Friends & Family, One Day Sale, VIP Sale, Lowest Prices of the Season. Our parser identifies and tags the sale event name per product alongside the sale price and end date — building an event-annotated pricing history that enables promotional intensity analysis by brand and category.
Macy's reviews include structured fit feedback fields — Runs Small, True to Size, Runs Large — and the size purchased by the reviewer. These fields transform reviews into sizing intelligence data, directly informing size curve decisions, fit model training, and return rate reduction analysis.
Clearance products on Macy's often carry multiple markdown layers. Our pipeline captures clearance flag status and the full discount depth per product — enabling markdown progression analysis across seasons and departments.
Every run emits structured logs to our observability stack. We alert on variant coverage drops, sale event classification failures, fit feedback null-rates, and schema drift — and respond before you notice.
Apparel brands track their own and competitor product pricing, sale event treatment, clearance depth, and discount frequency on Macy's — monitoring MAP compliance and brand equity signals.
Fashion brands and sizing platforms use Macy's fit-feedback review data — Runs Small / True to Size / Runs Large, with size purchased — to train sizing algorithms and calibrate size curve decisions.
Beauty brands use Macy's product data across cosmetics, skincare, and fragrance to map pricing, shade range, gift set structures, and GWP offer prevalence across competing brands.
ML teams use Macy's product datasets — apparel descriptions, colour names, material fields, fit tags, and review corpora — to train fashion AI for style classification, fit prediction, and recommendation.
Analysts evaluating Macy's competitive position use brand-level pricing data, promotional intensity signals, and clearance depth trends as indicators of merchandising health and inventory management.
Trade marketing and retail media teams correlate Macy's sale event types, discount depths, and event frequency with review velocity — assessing which promotional structures drive sustained demand versus one-time spike volume.
"Macy's carries over 500 brands across fashion, beauty, and home — and its sale event calendar, variant pricing, and fit-contextualised reviews make it one of the richest and most complex department store datasets in US retail."
Reliable Macy's scraping requires full colour-size variant matrix navigation, named sale event classification, fit feedback extraction from reviews, and clearance depth tracking across departments. DataFlirt builds the fashion-domain-specific pipeline so your brand, research, and sizing teams get complete, structured data — not just the headline price.
Everything supported by our macys.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, deduplication, and retry logic. Playwright handles Macy's JavaScript-rendered colour selectors, size variant navigation, and review tab pagination.
We maintain pools of US ISP residential proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About macys.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data from Macy's is generally permissible under applicable US law — reinforced by the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated data. We do not extract personal purchase history or loyalty account data. We recommend clients review Macy's ToS independently and consult legal counsel for specific use cases.
Yes. Our Playwright pipeline systematically navigates the colour and size selector on each product page — capturing every available combination with its individual stock status, price, and variant image URL. This full variant matrix is delivered as a nested structure within each product record.
Yes. Macy's applies named sale event labels — Friends & Family, One Day Sale, VIP Sale, etc. — per product during promotional periods. Our parser captures the sale event name alongside the sale price and end date, building an event-annotated pricing history across your defined product set.
Yes. Macy's review forms include structured fit feedback fields — Runs Small, True to Size, Runs Large — and the size purchased by the reviewer. These are extracted as separate structured fields per review record, enabling sizing algorithm training and fit model development.
Yes. Clearance flag and discount depth are captured per product per run. For products that enter clearance, we track markdown progression — from initial clearance price through subsequent markdowns — building a season-end clearance cadence dataset.
Yes. Beauty and fragrance products have distinct variant structures — shade names, formula types, and size options. Our pipeline handles beauty variant navigation separately from apparel, capturing shade name, shade hex code, size, and per-variant availability.
Our smallest packages start at a defined product set or department (typically 2,000–20,000 products) with weekly delivery. For brand-level pricing monitoring, seasonal sale event coverage, or fit intelligence programmes, we price based on volume and cadence.
Absolutely. We provide a sample run of up to 300 products with full variant mapping, sale event classification, and review fit feedback as part of the pre-engagement scoping process.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a brand pricing monitor, a full variant catalogue, a sale event history, or a fit-intelligence review corpus — we scope, build, and operate the pipeline.
