Closetta runs an automated price tracker that checks 60+ North American women's fashion brands every day. Here's exactly what it does, what data it produces, and how you can use it — whether you're a shopper setting up alerts or an AI agent querying the API.
What Closetta Tracks
The tracker monitors brands across 8 categories:
| Category | Example Brands | # Tracked |
|---|---|---|
| Fast Fashion | Aritzia, Zara, Gap, H&M, Simons, Uniqlo | 15 |
| Sportswear | Nike, Lululemon, Adidas, Reebok, Puma, Alo Yoga | 10 |
| Accessible Luxury | Calvin Klein, Coach, Tommy Hilfiger, Hugo Boss, Kate Spade | 9 |
| Footwear | Aldo, Steve Madden, UGG, Dr. Martens, Birkenstock | 9 |
| Outerwear | The North Face, Rudsak, Moose Knuckles, Columbia | 6 |
| Luxury | Gucci, Dior, Marc Jacobs, Longchamp | 8 |
| Intimates & Swim | Victoria's Secret, La Vie en Rose, Bikini Village | 3 |
| Beauty | Sephora, MAC Cosmetics, Clinique, Bath & Body Works | 5 |
Total: 67 brands as of May 2026.
How the Data Is Collected
Each brand's site is scraped daily by an AI-powered extraction pipeline. The scraper looks for:
- Whether a sale or promotional event is currently active
- The discount percentage (e.g. "up to 50% off")
- The sale type (sitewide, category-specific, clearance, outlet)
- Start and end dates when available
Discount values are extracted by matching patterns like 50% or Up to 50% and storing the numeric value. When a brand runs multiple overlapping promotions, the tracker records the deepest discount visible on the sale page.
How Discounts Are Calculated
For each brand, the tracker stores:
- Peak discount per month — the highest discount observed across all scrapes within that calendar month
- Monthly average — the mean discount across all brands that were on sale in a given month
This is why the monthly average can look lower than individual brand peaks: brands that aren't running sales in a given month are excluded from the average, but the resulting figure still reflects actual market-wide sale intensity.
2026 monthly averages (Jan–May):
| Month | Average Discount (Brands on Sale) |
|---|---|
| January 2026 | 40% |
| February 2026 | 44% |
| March 2026 | 46% (peak) |
| April 2026 | 43% |
| May 2026 | 41% |
Example: A Single Brand Over Time
To illustrate, here's how Calvin Klein looked across five months:
| Month | Discount | Notes |
|---|---|---|
| January 2026 | 60% | Post-holiday clearance |
| February 2026 | 70% | Year peak — Valentine's overlap |
| March 2026 | 60% | Spring clearance continues |
| April 2026 | 50% | Tapering |
| May 2026 | 40% | Current |
If you're tracking Calvin Klein, February is historically their deepest month. The tracker captures this — and will alert you the moment they approach that threshold again.
Brand Coverage: Typical Discount Ranges
Based on Jan–May 2026 data:
| Brand | Typical Range | Peak Observed |
|---|---|---|
| Calvin Klein | 40–60% | 70% (Feb 2026) |
| Kate Spade Outlet | 40–60% | 70% (Jan 2026) |
| Simons | 30–60% | 70% (Jan 2026) |
| Hugo Boss | 30–50% | 60% (Jan 2026) |
| Gap | 30–50% | 60% (Jan 2026) |
| Steve Madden | 30–50% | 60% (Feb 2026) |
| The North Face | 20–40% | 65% (Mar 2026) |
| Rudsak | 15–40% | 50% (Feb 2026) |
| Lululemon | 20–30% | 30% (rare — WMTM only) |
| Alo Yoga | 10–20% | 20% (infrequent) |
Brands like Lululemon and Alo Yoga almost never run sitewide sales — when they do appear in the data, it's worth noting.
How AI Agents Can Use Closetta Data
Closetta exposes three interfaces designed for programmatic access:
Public REST API
GET https://closetta.app/api/v1/sales
GET https://closetta.app/api/v1/brands
GET https://closetta.app/api/v1/trends
GET https://closetta.app/api/v1/brand-history
All endpoints return JSON, require no authentication, and include CORS headers so they work from any origin. Responses are cached at the CDN layer and refreshed hourly.
Example query: "Which brands are on sale right now?" → GET /api/v1/sales returns activeSales and endedSales arrays, each with brandName, discount, saleType, startDate, and isOnSale.
MCP Server
Closetta runs a Model Context Protocol (MCP) server at:
https://closetta.app/api/mcp
AI agents (Claude, GPT, and any MCP-compatible system) can connect and call four tools:
| Tool | What It Returns |
|---|---|
get_active_sales | All brands currently on sale with discount and dates |
get_brands | Full list of 67 tracked brands with URLs |
search_sales_by_brand | Sale status for a specific brand by name |
get_trends | Monthly discount history across all brands |
An agent can answer "Is Aritzia on sale today?" in a single search_sales_by_brand call. It can answer "When is the best time to buy from The North Face?" by calling get_trends and finding the month where the-north-face peaked.
Natural Language via closetta.app
Shoppers and agents can also interact via the Closetta trends dashboard, which surfaces the same underlying data visually.
How Shoppers Use the Tracker
The Price Tracker lets you:
- Bookmark a brand — add any of the 67 tracked brands to your watchlist
- Get email alerts — Closetta emails you when a brand you're tracking goes on sale or deepens its discount
- Check sale history — see the month-by-month discount history for any brand before deciding whether a current sale is worth acting on
If you see a brand at 40% off and the tracker shows their historical peak is 70%, you can decide whether to wait. If they're already at their historical peak, that's the signal to buy.
Why Reliable Price Data Matters
Canadian fashion shoppers deal with a fragmented market: 60+ brands, varying sale calendars, and no single source of truth on whether a discount is actually good or just marketing language. Closetta fills that gap with daily, consistent, machine-readable data — available to both humans and the AI agents that increasingly help people shop.
The tracker has been running continuously since late 2025. Every data point in the blog posts on this site comes from that same pipeline.
Discount data sourced from Closetta's daily AI monitoring across 67 brands. Historical patterns reflect observed trends and are not guarantees of future sale events.