Cettire Brand & Discount Intelligence — luxury markdown analytics per designer

Get one analytics row per designer from Cettire (cettire.com), the ASX-listed luxury marketplace with about 350,000 products. Each row reports how many products are listed, how many are in stock, what share is on sale, how deep the discounts go, and the price range, in any of Cettire's 67 markets.

You don't have to scrape 350k rows and aggregate them yourself: each row already summarises a brand's full Cettire assortment, optionally split by department, category or product type.

Who uses this

Buyer	Question it answers
Brand protection / wholesale teams	"How much of our brand is Cettire discounting this week, and how deep?"
Equity analysts (ASX: CTT)	Catalogue breadth, stock depth and markdown intensity, tracked over time
Luxury buyers & merchandisers	Benchmark designers' price points and markdown levels
Researchers	Market-level dataset on grey-market luxury discounting

What you get (per row)

Field	Example	Meaning
brand	Gucci	designer
department / category / product_type	women / bags / Tote Bags	when grouped
country / currency	US / USD	market analysed
product_count / in_stock_count	1999 / 1999	assortment depth
on_sale_count / pct_on_sale	1810 / 90.5	markdown breadth
avg_discount_pct / median_discount_pct / p90_discount_pct / max_discount_pct	39.3 / 38.8 / 56.4 / 71.5	markdown depth
discount_histogram	{"0-20": 94, "20-40": 835, "40-60": 814, "60+": 67}	distribution
min_price / median_price / avg_price / max_price		selling price range
median_compare_at_price		original price level
total_inventory_units		stock units (representative variant per product)
new_last_30d_count		products published in the last 30 days
top_product_types	[{"product_type": "Jackets", "count": 156}]	assortment mix
top_discounts	title, url, price, compare-at, discount %	the N deepest markdowns

Use cases

1. Weekly markdown monitor. Schedule your brand plus five competitors every Monday and chart pct_on_sale and median_discount_pct over time.
2. Market comparison. Run the same brands for US, GB, AU and HK to see where discounting is deepest.
3. Category deep-dive. Use groupBy: brand_category to see whether a brand's bags or its shoes are the ones being discounted.
4. Full-market snapshot. Leave brands empty to rank all ~2,600 designers by discount depth. This makes a quarterly dataset for CTT research.
5. Alerting. Pair it with a webhook to flag brands whose max_discount_pct crosses your threshold.

Input

Parameter	Description	Default
brands	Designers to analyse (case-insensitive). Empty = all brands, largest first	["Gucci","Prada","Saint Laurent"]
groupBy	brand, brand_department, brand_category, brand_product_type	brand
department / category	Restrict the scan	any
country	Market for prices	US
minProducts	Skip groups smaller than this	1
maxBrands	Cap number of brands (0 = all)	0
includeTopDiscounts	Attach the N most-discounted products per row	3

Example input

{
  "brands": ["Gucci", "Prada", "Saint Laurent", "Bottega Veneta"],
  "groupBy": "brand_category",
  "country": "GB",
  "includeTopDiscounts": 5
}

Example output (one row, real data — Gucci, US, 24 Sep 2026)

{
  "brand": "Gucci", "brand_handle": "gucci", "group_by": "brand",
  "country": "US", "currency": "USD",
  "product_count": 1999, "in_stock_count": 1999,
  "on_sale_count": 1810, "pct_on_sale": 90.5,
  "total_inventory_units": 3913,
  "avg_discount_pct": 39.3, "median_discount_pct": 38.8, "p90_discount_pct": 56.4, "max_discount_pct": 71.5,
  "discount_histogram": {"0-20": 94, "20-40": 835, "40-60": 814, "60+": 67},
  "min_price": 164.66, "median_price": 884.03, "avg_price": 1212.95, "max_price": 7420.07,
  "median_compare_at_price": 1583.05,
  "new_last_30d_count": 390,
  "top_product_types": [
    {"product_type": "Jackets", "count": 156},
    {"product_type": "Knitwear & Sweaters", "count": 147},
    {"product_type": "Pants", "count": 145}
  ],
  "top_discounts": [
    {"title": "Gucci Heavy Satin Pants", "url": "https://www.cettire.com/products/gucci-heavy-satin-pants-958789747",
     "product_type": "Pants", "price": 586.41, "compare_at_price": 2054.21, "discount_percent": 71.5}
  ],
  "scraped_at": "2026-09-24T07:55:00Z"
}

How it works and how reliable it is

For each brand the actor reads the brand's complete product list from Cettire's own catalogue API. It splits large brands into exact slices, so nothing is sampled or double-counted, and then computes the statistics. It needs no browser and no login. Progress is checkpointed after every batch of brands, so an interrupted run resumes without duplicate rows.

Scheduling, webhooks & integrations

• Schedule weekly runs to build a markdown time series.
• Webhooks on ACTOR.RUN.SUCCEEDED push results into your warehouse.
• Export to CSV, Excel or JSON, or use Google Sheets, Looker Studio, Make, Zapier, n8n or S3.

Use with AI assistants (MCP)

Connect the actor to Claude, Cursor or any MCP client via the Apify MCP server. Then ask "Which of Gucci, Prada and Loewe is Cettire discounting most in the UK?" and the assistant runs this actor with country: "GB" and compares the rows.

FAQ

How current is it? Every run reads Cettire's live catalogue.

How long does it take? A handful of brands takes a few minutes. All ~2,600 brands take a few hours; maxBrands caps a run.

What counts as "on sale"? A product whose selling price is below its compare-at (original) price. Discount % = (compare-at − price) / compare-at.

Why is total_inventory_units approximate? Cettire's listing API exposes stock for one representative variant per product. Use the Cettire Product Scraper with product details for exact per-size stock.

Do I need a proxy? No. The proxy setting is only a fallback if Cettire rate-limits a very large run.

Is this affiliated with Cettire? No. This is an independent tool that uses publicly available product information and is not endorsed by Cettire Limited.