# Cettire Brand & Discount Intelligence (`axlymxp/cettire-brand-intelligence`) Actor

Luxury markdown analytics from Cettire: one row per designer with product count, stock, share on sale, average/median/max discount and price range, in 67 markets. Track grey-market discounting of any brand over time. Pay only per analytics row.

- **URL**: https://apify.com/axlymxp/cettire-brand-intelligence.md
- **Developed by:** [axly](https://apify.com/axlymxp) (community)
- **Categories:** E-commerce, Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 dataset items

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 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

```json
{
  "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)

```json
{
  "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](https://mcp.apify.com). 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](https://apify.com/axlymxp/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.

# Actor input Schema

## `brands` (type: `array`):

Designers to analyse, as named on Cettire (case-insensitive), e.g. "Gucci", "Saint Laurent". Leave empty to analyse every brand on Cettire (~2,600), largest first.

## `groupBy` (type: `string`):

One row per brand, or split each brand by department, category or product type.

## `department` (type: `string`):

Restrict to one department.

## `category` (type: `string`):

Restrict to one top-level category.

## `country` (type: `string`):

Which Cettire storefront to price products for. Prices, currency and availability are localised per market.

## `minProducts` (type: `integer`):

Skip groups with fewer products than this (useful with product-type grouping).

## `maxBrands` (type: `integer`):

Analyse at most this many brands (0 = all). With an empty brand list, the largest brands come first.

## `includeTopDiscounts` (type: `integer`):

Attach the N most heavily discounted products to each row (0 = none).

## `proxyConfiguration` (type: `object`):

Cettire's API needs no proxy. It is used only if Cettire rate-limits the run's IP on very large crawls. The run then switches to these proxy sessions automatically.

## Actor input object example

```json
{
  "brands": [
    "Gucci",
    "Prada",
    "Saint Laurent"
  ],
  "groupBy": "brand",
  "department": "",
  "category": "",
  "country": "US",
  "minProducts": 1,
  "maxBrands": 0,
  "includeTopDiscounts": 3,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `dataset` (type: `string`):

One aggregated analytics row per brand (or brand × group): product count, share on sale, discount distribution and price range.

## `summary` (type: `string`):

Totals for the run (rows pushed, API requests).

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "brands": [
        "Gucci",
        "Prada",
        "Saint Laurent"
    ],
    "proxyConfiguration": {
        "useApifyProxy": true
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("axlymxp/cettire-brand-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "brands": [
        "Gucci",
        "Prada",
        "Saint Laurent",
    ],
    "proxyConfiguration": { "useApifyProxy": True },
}

# Run the Actor and wait for it to finish
run = client.actor("axlymxp/cettire-brand-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "brands": [
    "Gucci",
    "Prada",
    "Saint Laurent"
  ],
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}' |
apify call axlymxp/cettire-brand-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,axlymxp/cettire-brand-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/WYBZk2gbvYZ9UPFhT/builds/cVUlWQdfkRbNBNpra/openapi.json