About Sneakerblend
Independent by nature
No sponsored placement. No affiliate revenue. A source and a date on every record.
We combine market data, retail observations and cultural signals to provide a clearer view of what is happening in the sneaker industry.
Who it is for
Built for people who decide
Buyers
Choosing what to order. Is this colourway spreading, which models are moving in resale, and what do they sell for?
Retailers
Watching the competition. Who else stocks a pair, and what are others adding to their range?
Brands
Reading a model across channels: resale, retail assortment, search interest and culture.
Analysts and press
Looking for something to cite. Every figure comes with its dataset, period and method.
Coverage, live
What is covered today
- 574SKUs trackedthe tracked list, not the whole market
- 42retailers monitoredin 12 countries
- 98weeks of sales historycompleted sales, StockX and GOAT
- 12editions, reports and articles published2 editions · 6 search reports · 4 articles
Last updated 15 Nov 2026. Counts are read when this page is built, at most an hour ago.
Retailers are monitored in: BE, CH, CZ, DE, DK, ES, FR, GB, IT, JP, NL, SE.
We do not name retailers publicly; members see the directory. The tracked list is chosen by an editor, and it is not the whole market.
Foundation & mandate
What we are
Sneakerblend is an independent intelligence layer for retailers, buyers, brands and industry professionals. It brings marketplace data, retail observations, search interest, social signals and written research together in one place, and labels each of them with what they actually measure.
The platform does not sell placement and does not take affiliate commission. Observations are collected outside the platform, then reviewed by an editor before anything is published.
Methodology
Core principles
Four pillars of analytical objectivity
Independent
No brand sponsorships, no retail affiliate commissions. Nothing on the platform is placed.
Pillar 01
Multi-source
Marketplace data, monitored retailer assortment, search interest and manually logged social posts, cross-referenced.
Pillar 02
Provenance first
Every value keeps its provider, source link, currency and the moment it was observed. History is never overwritten.
Pillar 03
Honest by default
Where there is no data, the page says so. An empty state beats a number nobody can stand behind.
Pillar 04
Where it comes from
Data sources
- Marketplace data
- Asking prices and daily sales history from monitored sneaker marketplaces. Every value is stored with its market, currency, size and the moment it was observed.
- Retail observations
- A selection of retailers is monitored for new arrivals and assortment changes. Observations are imported by CSV or entered by hand, then classified by colour and material.
- Search interest
- Google Trends exports, stored per export batch. Values are relative within one export, which is why terms from different exports are never charted on the same axis.
- Social observations
- Individual posts logged manually with their link, creator, publication date and what was observed. Nothing is scraped and nothing is scored.
- Independent research
- Analysis written and reviewed in house, published with its sources and the date it was written.
Reading the data
What the numbers mean
- An asking price is not a sale
- Marketplace listings show what a seller is asking. Unless a record is explicitly labelled as a completed sale, it is an ask, and it is labelled that way everywhere it appears.
- Search interest is not search volume
- Google Trends reports relative interest on a 0–100 scale, normalised inside a single export. A score of 100 is the peak of that export, not a number of searches.
- Assortment is not sell-through
- A product appearing at a monitored retailer means it was offered, not that it sold. Colour and material trends describe what retailers carried, nothing more.
- A ranking states its method
- Every ranking shows the dataset it used, the period it covers, how it was calculated and which filters were applied. Where the data is too thin, it says so instead of ranking.
Review
How observations are reviewed
Nothing reaches the platform automatically. Imported and manually entered records arrive as a draft. An editor links each one to the right brand, model, product, retailer, colour and material, corrects what the automatic matching got wrong, and checks the source. Only then is a record published.
Records are never overwritten: edits and removals keep the original observation and its date, so the history stays intact.
Corrections and contact
Found a mistake?
Every record keeps its source and its date. If you think one is wrong, tell us which record and why, and an editor will check the source. A corrected record keeps its history; nothing is overwritten.