How many stores sell in a category, where they are, how they price, how fast new ones appear, which tools they run and which products are rising. Answer it all from one dataset of 1M+ active stores.
Industry reports give you one big number for a whole sector. Store-level data shows who is actually selling, at what price, with what tools, and how fast new competitors arrive.
Answer these six and you understand a market better than most people already in it.
Count stores in the category and country. Compare with neighboring categories to judge density and how hard it will be to stand out.
Sort by average price. See where the premium end starts, how many stores sit there, and where the gaps are.
Product counts show whether focused brands or big catalogs dominate, which shapes how you should compete.
The share of young domains with strong authority shows whether newcomers can still win attention and rankings.
Sort by popularity and authority. The top 20 define customer expectations on price, range and service.
Product group trends inside the category show what buyers want more of each year, and what they want less of.
A complete market picture combines store, product and technology data. Here is what each part contributes, and which plan includes it.
| Data point | Where it comes from | Plan |
|---|---|---|
| Active store count | Store database filters | Both plans |
| Average price distribution | Store records | Both plans |
| Catalog size distribution | Store records | Both plans |
| Breakout stores | Age filter plus authority sort | Both plans |
| Market leaders | Popularity and authority sort | Both plans |
| Closest competitors to a store | Similar stores | Both plans |
| Rising product groups | Product trend sort | Both plans |
| Low-competition products | Ranking difficulty sort | Both plans |
| Technology adoption | Technology lookups and analytics | Both plans |
| Likely next tools | AI recommender | Both plans |
Repeatable, exportable and fast. Most studies take an afternoon, and the second time you run the same market it takes under an hour.
A category or subcategory plus a country. Or a product keyword if the niche has no category.
Note the number of active stores and compare it with one neighboring market.
Sort by average price and mark the budget, mid and premium bands with their store counts.
Sort by product count to see the catalog mix from specialists to generalists.
Filter to domains under five years and sort by authority to find the breakouts.
Check product group trends for the category over five years.
Excel for analysis, PDF for the deck, both from the same tables.
The same seven steps, run on two or three markets, turn into a clear expansion decision. Score each signal good, neutral or hard, then compare.
| Signal | What a good market looks like | What a hard market looks like |
|---|---|---|
| Store count | Enough stores to prove demand | Very many, or almost none |
| Price bands | Room at your price point | Crowded at your price point |
| Catalog mix | Space for your model | Dominated by the opposite model |
| Young-store share | Newcomers gaining authority | Only old, entrenched leaders |
| Leaders | Leaders with visible weaknesses | Leaders with strong stacks and huge reach |
| Product trends | Rising groups in the category | Flat or falling groups |
| Technology | Modern stacks still spreading | Fully saturated stacks |
Different questions, same data. Each team below starts from the same store database and ends with a different decision.
"Is there room for one more store in this niche?" Density, momentum and price gaps answer it.
"Which country should we sell into next?" Run the same study for each candidate.
"Which categories are producing breakout brands?" Young-store share points the way.
"How many potential customers exist in this vertical?" Store counts by size give the answer.
"How many merchants could we serve in this country?" Country and catalog filters size it.
"What does this market look like, in evidence?" Tables and named examples make the case.
LeadsQuantum also publishes simple top-store pages for countries and categories, a quick first look before you open the full dataset.
A starting list of leading stores in one market, open to anyone without logging in.
For anyone selling to merchants, the useful number is not "all stores" but the stores you can actually serve.
An illustrative study comparing three markets for a premium cookware brand.
Each category has its own shape. These notes tell you which lens matters most when you study it.
Many small labels, a few giants. Momentum among young labels tells you how open it still is to newcomers.
Premium and budget compete very differently. Map both before choosing a position and a price.
Large generalists and design-led specialists coexist. Find the gap between them where buyers are underserved.
Count the high-price stores. Growth often sits at the top of the market where owners spend more.
Technology adoption shows how many stores sell on repeat through subscriptions.
Keyword search finds niche hardware stores that category filters alone would miss.
Pair store counts with product trends by season, sport by sport.
Reviews and support tools are standard. Gaps there are weaknesses you can exploit.
Markets do not open with an announcement. They open with patterns you can see in the data first.
Several domains under five years with strong authority, not just one lucky store.
Positive 5-year trends across several of the category's product groups.
Demand exists but few stores sell at the top of the price range.
Top stores running older stacks and slow sites that newcomers can outdo.
Modern tools adopted mostly by young stores, a sign of a new generation.
Product keywords where weak sites still reach page one in search.
Research is only useful if it ends a debate. These are the decisions the six lenses are built to settle.
Momentum and price gaps decide timing more than total market size does.
Compare supply, premium room and newcomers across the candidates.
Pick the band with clear demand and relatively few sellers.
Catalog mix shows what the market currently rewards.
Rising, low-difficulty product groups that fit your brand.
Stores that complement, not copy, your product range.
What leaders and breakouts already run and rely on.
Set the next quarterly review date now, before you forget.
Use this structure for internal decisions or client deliverables. It fits on one page and every line traces back to a filter.
| Section | Content | Source in LeadsQuantum |
|---|---|---|
| Market definition | Category, subcategory, country | Store filters |
| Supply | Number of active stores | Row count |
| Pricing | Budget, mid and premium bands | Average price sort |
| Structure | Specialists versus large catalogs | Product count sort |
| Momentum | Breakout stores and their share | Age filter, authority sort |
| Leaders | Top 10 with notes | Popularity sort, store records |
| Demand | Rising product groups | Product trend sort |
| Technology | Common tools and gaps | Store stacks, technology lookup |
| Recommendation | Enter, wait, or avoid, and why | Your judgment |
Good data can still lead to bad conclusions. Watch for these six traps in every study you run.
A Tier 1 category hides several different markets. Go one level down before drawing conclusions.
Store count says nothing about demand. Pair it with product trends and price bands.
A crowded market can still have an empty premium tier waiting for the right brand.
Leaders win on reach you do not have. Study the breakouts instead, they show what works now.
Markets move. Repeat the study each quarter with the same filters.
Decisions stick when backed by real stores people can visit and judge for themselves.
The terms used in this page and in market briefs built from LeadsQuantum data, so everyone reading a brief reads it the same way.
| Term | Meaning |
|---|---|
| Supply | The number of active stores selling in a market. |
| Price band | A range of average product prices, such as budget, mid or premium. |
| Catalog mix | The spread of store sizes by number of products. |
| Young-store share | The share of strong stores with domains under five years old. |
| Leaders | The stores with the highest popularity and authority. |
| Serviceable market | Stores you can actually serve given country, language and size. |
| Demand trend | The 5-year search trend of the market's product groups. |
Filter the store database by category and country and note the row count. Then narrow by size and stack to get the serviceable and reachable markets, each as an exportable list.
Advanced covers stores, niches, product trends and technology adoption, which is enough for most market studies. Enterprise adds larger reports and more seats.
No private revenue figures. Product count, average price, popularity and authority together describe each store's scale well enough for market decisions.
Yes. Run the same filters for each country. Around one million stores carry a country, covering all major ecommerce markets.
Yes, to Excel for analysis or PDF for presentations. Publishing raw data needs our written consent first.
Filter to domains under five years and sort by authority. Many strong young stores mean a market that is still opening up to new brands.
Yes. Product groups carry a 5-year trend. See trending products.
The store database covers more than a million active online stores. Sites on other platforms appear through technology lookups across five million popular domains.
An afternoon for one market using the seven steps, a day for a three-market comparison including the written brief.
Yes, for analysis and insights in your deliverables. Contact us before sharing raw exports with clients so the right terms are in place.
The part of the total market you can actually serve, given your countries, languages and the store sizes your offer fits. Filters turn it into a real, named list.
Yes. Save your filters and repeat the study each quarter. Compare store counts, young-store share and leaders against last time to see the direction of travel.
Filter stores by country and category, keep the price band that matches your brand, and use similar stores to find more like the best ones you discover.
Yes. Keyword search across store names and descriptions finds every store selling it, and product groups show its demand trend over five years.
Yes. Each query and each page of results counts. A full single-market study uses a small share of the Advanced allowance.
Few young stores with strong authority, flat or falling product trends, and leaders with modern stacks and huge reach. Entering is still possible, but it needs a sharp angle.
The data covers independent online stores and popular websites, not listings inside marketplaces. It is ideal for direct-to-consumer and independent retail markets.
Store counts, prices, catalogs, momentum, stacks and product trends. Included in both plans, from $999 per year.