What every column means, how to read it, and which filters and sorts use it. Five datasets, one reference: stores, domains, technologies, products and recommendations.
Five connected datasets. Each one has its own fields, and they link through domains and technologies, so one question can draw on several of them at once.
60,000+ groups from 32M products, with demand, trend and difficulty metrics.
Likely next technologies per site, and likely adopters per technology, each explained.
Every online store in the database carries these fields. Field names in the dashboard may be worded slightly differently, but the meanings below apply everywhere.
| Field | Type | Meaning | Use it to |
|---|---|---|---|
| Domain | text | The store's web address | Identify and visit the store |
| Store name | text | The name the store uses | Keyword search, personalization |
| Description | text | How the store describes itself | Keyword search, positioning |
| Category | label | Main ecommerce category | Market maps, vertical lists |
| Subcategory | label | More specific category, such as Baby Health | Precise niche lists |
| Country | code | Where the store is based, on around 1M stores | Territories, local markets |
| Language | code | The store's main language | Localized outreach |
| Number of products | number | Catalog size | Store size, business model |
| Average price | number, USD | Typical product price | Price positioning, premium filters |
| Domain age | years | Time since the domain was registered | New versus established stores |
| Authority | score | Link strength of the domain | Momentum, strength in search |
| Popularity rank | rank | Position in global traffic rankings, lower is more popular | Reach and scale |
| Technologies | list | Tools installed on the store | Technographic targeting |
| Recommended technologies | list | Tools the store is likely to add next | Add-on selling, store audits |
| Contacts | text | Public contact details where available | Outreach |
| Similar stores | list | Stores with the closest product offering | Competitors, look-alikes |
Single fields describe. Pairs of fields explain. These six pairs answer most of the questions users bring to the platform.
Young and strong is a breakout. Old and weak has stalled. Old and strong is an incumbent to respect.
Few and expensive is a focused brand. Many and cheap is a volume store. Many and expensive is a specialist retailer.
High reach with a thin stack means an under-tooled store with upside for the right vendor.
Together they define a market you can size, compare and export.
What a store runs today and what it will likely buy next, with the reason.
Your real competitors and where you sit among them on price.
The five million most popular domains worldwide, including publishers, SaaS companies, services and other non-ecommerce sites.
| Field | Type | Meaning | Use it to |
|---|---|---|---|
| Domain | text | The website address | Identify the site |
| Tier 1 category | IAB label | Broad vertical, such as Pets or Personal Finance | Vertical targeting |
| Tier 2 category | IAB label | Specific niche, one of 440 categories in total | Niche research, precise lists |
| Domain age | years | Time since registration | Newcomers versus incumbents |
| Authority | score | Link strength | Breakouts, outreach tiers |
| Technologies | list | Tools on the site, including ad networks | Publisher lists, technographics |
| Recommended technologies | list | Likely next tools | Add-on selling beyond ecommerce |
The IAB taxonomy is the shared language of digital advertising, so domain categories map directly onto ad buying and publisher work.
For each of 4,000+ technologies, these views describe its market: who uses it, where, and whether it is growing.
| Output | Type | Meaning | Use it to |
|---|---|---|---|
| Users | list | Stores and domains running the technology | Lead lists, switch campaigns |
| Tier 1 ratio | table + chart | Usage share per category divided by the category's share of all domains | Find over-indexed verticals |
| Tier 2 ratio | table + chart | The same ratio across all 440 Tier 2 categories | Precise vertical targeting, churn risk |
| Baseline share | table | Each category's share of all domains | Read ratios correctly |
| Domain age profile | chart | Usage across domain ages | Rising versus aging technologies |
| Popularity profile | chart | Usage across ten bands of the top 1M sites | Enterprise versus mass-market |
| Country profile | chart | Usage across store countries | Local strongholds and gaps |
| Comparison | chart | Two or more technologies on one chart | Rival audience gaps |
| Churn ranking | list | Users ordered by likelihood of dropping the technology | Customer success priorities |
A ratio of 8.1 means the technology is used about eight times more in that category than the category's size alone would predict. Ratios above 1.0 mark natural markets.
More than 60,000 product groups built from 32 million products. Each group carries these metrics, all sortable inside any category.
| Metric | Type | Meaning | Higher is |
|---|---|---|---|
| Product group | label | A type of product, such as "air purifier" | n/a |
| Vertical | label | The categories the group belongs to | n/a |
| Average price | number, USD | Typical price across products in the group | Better for margins |
| Search volume | number | How often people search for the product | More demand |
| 5-year trend | number | Change in search interest over five years | Rising demand |
| Ranking difficulty | score | Average link strength of the top ten ranking sites | Harder to rank |
| Age of ranking sites | years | Average domain age of the top ranking sites | More entrenched |
| CPC | number | Typical cost per click in paid search | Costlier ads |
| Competition | score | How crowded paid search is | More crowded |
| Seasonality | score | How much demand swings through the year | Less steady |
| Uniqueness | score | How distinct the group is; lower means more unique | Less unique |
| Opportunity rating | score | One combined score across the factors above | Better opportunity |
It rewards high price, high demand, rising trends, uniqueness and steady demand, and penalizes strong incumbents, costly ads and heavy competition.
The recommender answers in two directions. Both outputs share the same structure: a ranked list, with the reason for each row.
| Output | Type | Meaning | Limit |
|---|---|---|---|
| Site to technologies | ranked list | Technologies a site is likely to add, most likely first | Up to 1,000 per site |
| Technology to sites | ranked list | Sites likely to adopt a technology, most likely first | 100 rows per report on Advanced, 5,000 on Enterprise |
| Driver technology | label | The installed technology most responsible for each pick | One per recommendation |
| Bulk run | file | Recommendations for your own list of domains | Up to 500 domains on request |
Most result tables share the same controls. This is what each control does, and once you know one table you know them all.
Main category and subcategory for stores, Tier 1 and Tier 2 for the five million domains.
Where a store is based and what language it mainly uses.
Ranges for number of products and average price, to match your offer.
Minimum or maximum domain age and authority, for breakouts or incumbents.
Sites that run a chosen technology, on both plans.
Words in store names, descriptions and domains, all searched at once.
Popularity, price, products, age, authority and more, ascending or descending.
Trend, difficulty, price or opportunity rating, inside any category.
Exports contain the rows and columns of the table you are looking at, with your filters and sort applied.
Everyone uses the same data, but each role leans on a different handful of fields. Start with yours and add others as questions grow.
| Role | Key fields | Typical question |
|---|---|---|
| SaaS sales | Technologies, recommended technologies, products, country | Who should we call this week? |
| Agency | Category, country, technologies, contacts | Which list fits this client? |
| Store owner | Similar stores, average price, technologies | What do my peers do differently? |
| Product researcher | Average price, 5-year trend, ranking difficulty, opportunity rating | What should we sell next? |
| Niche builder | Tier 2 category, domain age, authority, ad technologies | Which niche can we win? |
| Investor | Domain age, authority, popularity rank, category | Which brands are breaking out? |
| Product manager | Ratios, age profile, popularity profile | Which audiences are we missing? |
| Customer success | Tier 2 ratio, churn ranking | Which customers might leave? |
When you are not sure where to start, find the closest question here and use those fields as your first filters.
| Question | Fields |
|---|---|
| Which stores sell premium pet products in the US? | Category, country, average price |
| Which young stores are growing fast? | Domain age, authority |
| Who uses a rival app? | Technologies |
| Who will likely buy our app next? | Recommended technologies, driver |
| Which verticals suit our product? | Tier 1 and Tier 2 ratios |
| Is our category growing? | Domain age profile |
| Which products are rising in home and garden? | Vertical, 5-year trend |
| Which products can a new site rank for? | Ranking difficulty, age of ranking sites |
| Which finance sites run ads? | Tier 1 category, technologies |
| Who are my closest competitors? | Similar stores |
Knowing the edges of a dataset makes you use it better and keeps your conclusions honest.
Around one million stores carry a country. Country filters return that subset, which covers all major ecommerce markets.
Public contact details appear when a store publishes them on its site.
Stores and stacks change. Re-run important searches regularly and work the new rows first.
Average price describes a store's range, not any single product it sells.
Recommender order is for prioritizing outreach, not a promise of purchase.
Before big campaigns, open ten records and confirm the fit with your own eyes.
An illustrative record for a premium pet furniture store, read field by field the way an experienced user would.
The link strength of a domain. Higher authority usually means more and better links from other sites, and an easier time ranking in search.
Yes. Rank 1 is the most popular site. A store ranked 50,000 is far more visited than one ranked 900,000, so sort ascending for reach.
So stores in different countries can be compared on the same scale without currency conversion.
Around one million stores carry a country. Some stores do not, so country filters return a subset.
Store categories describe what an online store sells. IAB categories describe what any website is about and are used for the five million popular domains and for technology ratios.
A lower uniqueness score means a more distinct product group, which counts in favor of its opportunity rating. Distinct products are easier to stand out with.
Close, but the dashboard may use slightly different wording. The meanings on this page apply, whatever the exact column label says.
Write to [email protected] and describe what you need and how you would use it.
The technology a site already runs that contributed most to a recommendation. It explains each pick and makes a natural opening line.
The top one million sites by global popularity, split into ten bands of 100,000 each.
The average link strength of the ten sites ranking at the top for a product keyword. Lower means a newer site has a better chance of reaching page one.
All of them. Both plans include every field; Enterprise raises report sizes and seats.
Yes. Export each table and join them on the domain in Excel or your own tools. The domain is the shared key across datasets.
The platform is dashboard-first with exports. For automated delivery, contact us to discuss options.
Keep each export with its date and the filters you used. Comparing exports month by month shows new stores, stack changes and stores that disappeared.
It depends on the goal: popularity for reach, authority for momentum, average price for positioning, recommender order for sales fit, and opportunity rating for products.
Yes, for internal use. Load CSV or Excel exports into your BI tool. Publishing or reselling the data needs our written consent under the terms of service.
Stores, domains, technologies, products and recommendations in one platform. From $999 per year.