SmartScout Data Lake
Amazon marketplace data, in your warehouse, query-ready.
The SmartScout Data Lake delivers structured Amazon datasets to your Snowflake environment as a native share. No pipelines to build, no scrapers to maintain, no API rate limits to work around. You write SQL against billions of rows the same way you query your own tables.
Native Snowflake share Core tables refresh 2× weekly 6–24 months of backfilled history
Existing enterprise subscription?
Talk to your account managerInterested to learn more?
Talk to salesworksheet — smartscout.public Snowflake
select
b.Name, h.Date,
sum
(h.EstimatedUnitSales)
as
Units,
sum
(h.EstimatedUnitSales * h.BuyBoxPrice)
as
Revenue
from
smartscout.public.ProductHistories h
join
smartscout.public.Products p
on
p.Id = h.ProductId
join
smartscout.public.Brands b
on
p.BrandId
where
b.Name = 'Your brand'
group
by
1, 2
order
by
h.Date;
Returns
Name Date Units Revenue
02
At a glance
The full specification, in one screenshot.
Delivery method
Snowflake native data share (Secure Data Sharing)
Warehouses supported
Snowflake only
Cloud providers
AWS, Azure, GCP, etc.
Refresh cadence
Core tables refresh 2× weekly. All other tables refresh at least weekly.
Historical depth
6–24 months on the historical tables. Current-state tables always reflect the most recently tracked values.
Time series grain
Weekly for ProductHistories, BrandCoverageHistory, and SearchTermHistories.
Marketplaces
All marketplaces covered in SmartScout: Amazon US, Canada, Mexico, UK, Germany, France, Italy, Spain, Japan, India, Australia, and UAE.
Query limits
None. Snowflake sharing gives direct query access against your own compute.
Portal access included
Yes. Data Lake clients receive SmartScout web app seats and access at the enterprise level.
Setup time
Typically within two business days, depending on your Snowflake region and cloud provider.
04
What's in the data
Seventeen tables in five domains. Select a card to open that table in the field reference.
Join map Five domains and the keys that connect them
Catalog and products
What is listed, at what price, selling how much, from how many sellers.Products
Current state of every tracked ASIN: price, rank, sellers, revenue estimate.
One row per ASIN, current state 38 fields
ProductHistories
The workhorse for any trendline.
One row per ASIN per period 8 fields
Subcategories
The category hierarchy. PathById rolls results up without hardcoding.
One row per browse node 5 fields
Sellers and ownership
Who is selling this, are they authorized, how much of the buy box do they hold.Brands and market share
How is a brand performing, who controls its revenue, is it growing.Brands
Aggregate performance and growth per brand.
One row per brand 22 fields
BrandCoverages
Current revenue contribution of each seller to each brand.
One row per seller per brand, current 4 fields
BrandCoverageHistory
Watch a distributor's share of a brand erode or grow.
One row per seller per brand per period 5 fields
Search and advertising
What shoppers search, who ranks organically, who buys the placement.SearchTerms
Estimated volume, estimated CPC, intent classification.
One row per search term 8 fields
SearchTermHistories
Seasonality work lives here.
One row per search term per period 3 fields
SearchTermIntents
The intent lookup.
One row per intent value 2 fields
SearchTermProductOrganics
Organic rank per term per ASIN.
One row per term per ASIN, current 4 fields
SearchTermProductOrganicHistories
Organic rank over time.
One row per term per ASIN per period 4 fields
SearchTermProductPaids
Paid placement win rates and estimated spend, per ASIN.
One row per term per ASIN 8 fields
SearchTermBrandPaids
The same paid metrics rolled up to brand.
One row per term per brand 8 fields
05
Field reference
Every field in the share, with its table context. 146 fields across 17 tables in five domains. Select a table to expand it.
06
What clients build with it
Organized by who is reading.
Brands and manufacturers
- Market share and category tracking against a defined competitive set
- Unauthorized seller and MAP monitoring using
SellerProductsandBrandCoverages - Product launch tracking from
DateLaunchedand earlyProductHistories - Assortment and white-space analysis by subcategory
- Share-of-voice on priority search terms, organic and paid
Agencies and retail media
- Client reporting at scale, one query pattern across every brand you manage
- Competitive ad spend and placement win rates by term
- New business pitches built on real category data
- Category benchmarks clients cannot get from their own Seller Central
Platforms and data teams
- Embed marketplace intelligence into your own product — this requires a white-label license, so speak with your SmartScout rep to learn more
- Enrich internal records with revenue estimates, fulfillment mix, and seller type
- Build the dashboards and models your customers ask for without maintaining scrapers
Investors and corp dev
- Diligence on brands and sellers using
Sellers,Brands, and the history tables - Portfolio monitoring across holdings
- Category growth analysis from
MonthGrowthandMonthGrowth12
Sales and revenue teams
- Enrich CRM records with brand, seller, and category data so reps open with something the prospect does not already know
- Set market-shift triggers for outbound — a competitor's revenue climbing, a brand losing buy box share, a new seller appearing on a listing
- Generate account-level reports automatically and drop them into outbound sequences
- Prioritize territories and target lists by category growth rather than by guesswork
- Build competitive displacement lists from seller and brand coverage data
07
Query library
Twelve queries, each with a note on what to change for your own use. Every snippet assumes the share was created as smartscout.
01
Full price and revenue history for one ASIN
Change the ASIN in the where clause.
selecth.Date, h.BuyBoxPrice, h.EstimatedUnitSales, h.BuyBoxPrice * h.EstimatedUnitSalesasEstimatedRevenue, h.SalesRankfromsmartscout.public.ProductHistories hjoinsmartscout.public.Products ponp.Id = h.ProductIdwherep.Asin ='B00XXXXXXX'orderbyh.Date;
02
Weekly performance for one brand
Change the brand name. Revenue here is computed from price times units, not MonthlyRevenueEstimate.
selecth.Date,sum(h.EstimatedUnitSales)asUnits,sum(h.EstimatedUnitSales * h.BuyBoxPrice)asRevenue,count(distinctp.Id)asAsinsfromsmartscout.public.ProductHistories hjoinsmartscout.public.Products ponp.Id = h.ProductIdjoinsmartscout.public.Brands bonb.Id = p.BrandIdwhereb.Name ='Your brand'groupbyh.Dateorderbyh.Date;
03
Weekly brand performance split by subcategory, date-bounded
Change the brand and the two dates. Bounding the date range first is what keeps this query cheap.
selects.NameasSubcategory, h.Date,sum(h.EstimatedUnitSales)asUnits,sum(h.EstimatedUnitSales * h.BuyBoxPrice)asRevenuefromsmartscout.public.ProductHistories hjoinsmartscout.public.Products ponp.Id = h.ProductIdjoinsmartscout.public.Brands bonb.Id = p.BrandIdjoinsmartscout.public.Subcategories sons.BrowseNodeId = p.BrowseNodeIdwhereb.Name ='Your brand'andh.Datebetween'2025-01-01'and'2025-06-30'groupbys.Name, h.Dateorderbys.Name, h.Date;
04
Top 50 brands in a subcategory by trailing revenue
Change the subcategory name. Swap Trailing12Months for MonthlyRevenue to rank on the trailing 30 days instead.
selectb.Name, b.MonthlyRevenue, b.Trailing12Months, b.MonthGrowth, b.TotalProductsfromsmartscout.public.Brands bjoinsmartscout.public.Subcategories sons.BrowseNodeId = b.BrowseNodeIdwheres.Name ='Your subcategory'orderbyb.Trailing12Monthsdesclimit50;
05
Every seller on a brand, with buy box share and revenue contribution
Change the brand name. This is the starting point for unauthorized-seller work.
selectse.NameasSeller, se.BusinessName, se.Country, bc.CoveragePercentage, bc.MonthlyRevenuefromsmartscout.public.BrandCoverages bcjoinsmartscout.public.Sellers seonse.Id = bc.SellerIdjoinsmartscout.public.Brands bonb.Id = bc.BrandIdwhereb.Name ='Your brand'orderbybc.CoveragePercentagedesc;
06
Brand revenue share within a subcategory over time
Change the subcategory. The window function gives each brand its share of the period total.
withperiodsas(selectb.NameasBrand, h.Date,sum(h.EstimatedUnitSales * h.BuyBoxPrice)asRevenuefromsmartscout.public.ProductHistories hjoinsmartscout.public.Products ponp.Id = h.ProductIdjoinsmartscout.public.Brands bonb.Id = p.BrandIdjoinsmartscout.public.Subcategories sons.BrowseNodeId = p.BrowseNodeIdwheres.Name ='Your subcategory'groupbyb.Name, h.Date )selectBrand, Date, Revenue, Revenue /sum(Revenue)over(partitionbyDate)asShareOfPeriodfromperiodsorderbyDate, ShareOfPerioddesc;
07
New ASIN launches in a subcategory within a date window
Change the subcategory and the two dates. DateLaunched is the listing launch, ListedSince is when the listing first appeared to us.
selectp.Asin, p.Title, b.NameasBrand, p.DateLaunched, p.MonthlyRevenueEstimate, p.ReviewCountfromsmartscout.public.Products pjoinsmartscout.public.Subcategories sons.BrowseNodeId = p.BrowseNodeIdleftjoinsmartscout.public.Brands bonb.Id = p.BrandIdwheres.Name ='Your subcategory'andp.DateLaunchedbetween'2025-01-01'and'2025-06-30'orderbyp.MonthlyRevenueEstimatedesc;
08
Organic rank movement for a set of ASINs across a set of search terms
Change the two lists. Positive Change means the rank number rose, which is worse.
selectst.SearchTermValue, p.Asin, o.HistoryDate, o.AvgRank, o.AvgRank -lag(o.AvgRank)over(partitionbyst.Id, p.Idorderbyo.HistoryDate )asChangefromsmartscout.public.SearchTermProductOrganicHistories ojoinsmartscout.public.SearchTerms stonst.Id = o.SearchTermIdjoinsmartscout.public.Products ponp.Id = o.ProductIdwherep.Asinin('B00XXXXXXX','B00YYYYYYY')andst.SearchTermValuein('term one','term two')orderbyst.SearchTermValue, p.Asin, o.HistoryDate;
09
Estimated competitor ad spend by search term for a brand
Change the brand name.
selectst.SearchTermValue, st.EstimateSearches, st.EstimatedCPC, bp.TopSpotWinRate, bp.SponsoredBrandSpend + bp.TopSpotSpend + bp.TopGroupSpend + bp.SponsoredVideoSpendasEstimatedSpendfromsmartscout.public.SearchTermBrandPaids bpjoinsmartscout.public.SearchTerms stonst.Id = bp.SearchTermIdjoinsmartscout.public.Brands bonb.Id = bp.BrandIdwhereb.Name ='Competitor brand'orderbyEstimatedSpenddesclimit100;
10
Out-of-stock and price-change detection, period over period
Change the brand. Adjust the 0.05 threshold to set how large a price move you want flagged.
withmovesas(selectp.Asin, h.Date, h.BuyBoxPrice,lag(h.BuyBoxPrice)over(partitionbyp.Idorderbyh.Date)asPriorPrice, p.OutOfStockNowfromsmartscout.public.ProductHistories hjoinsmartscout.public.Products ponp.Id = h.ProductIdjoinsmartscout.public.Brands bonb.Id = p.BrandIdwhereb.Name ='Your brand')select*, (BuyBoxPrice - PriorPrice) / nullif(PriorPrice,0)asPctChangefrommoveswhereabs((BuyBoxPrice - PriorPrice) / nullif(PriorPrice,0)) >0.05orOutOfStockNow = trueorderbyDatedesc;
11
Search volume seasonality for a term set
Change the term list. Compare each period against the term’s own average to see the seasonal shape.
selectst.SearchTermValue, sh.HistoryDate, sh.EstimateSearches, sh.EstimateSearches /avg(sh.EstimateSearches)over(partitionbyst.Id)asIndexToAveragefromsmartscout.public.SearchTermHistories shjoinsmartscout.public.SearchTerms stonst.Id = sh.SearchTermIdwherest.SearchTermValuein('term one','term two','term three')orderbyst.SearchTermValue, sh.HistoryDate;
12
Join reference — the whole graph in one query
Nothing to change. Read it once and the rest of the schema follows.
-- Products is the hub: every fact table carries ProductId,-- BrandId, SellerId, or SearchTermId back to it.selectp.Asin, b.NameasBrand,-- Products.BrandId -> Brands.Ids.NameasSubcategory,-- Products.BrowseNodeId -> Subcategories.BrowseNodeIdse.NameasSeller,-- SellerProducts.SellerId -> Sellers.Idsp.BuyBoxPercentage, h.Date, h.EstimatedUnitSalesfromsmartscout.public.Products pjoinsmartscout.public.Brands bonb.Id = p.BrandIdjoinsmartscout.public.Subcategories sons.BrowseNodeId = p.BrowseNodeIdleftjoinsmartscout.public.SellerProducts sponsp.ProductId = p.Idleftjoinsmartscout.public.Sellers seonse.Id = sp.SellerIdleftjoinsmartscout.public.ProductHistories honh.ProductId = p.Idwherep.Asin ='B00XXXXXXX'orderbyh.Date;
08
Connecting your tools
Because the data lands in Snowflake, anything that already reads Snowflake reads this. No connector to install and no separate credential to manage.
Power BI Looker Tableau Sigma Hex dbt Salesforce CDP and reverse ETL
The common pattern is worth naming: most clients build a thin dbt layer over the share, materialize the two or three aggregates their business asks about weekly, and point BI at that instead of at raw history.
Which SmartScout product fits
| Data Lake | API | MCP server | |
|---|---|---|---|
| Best for | Warehouse-native analytics, large historical scans, embedding into your own product | Application integration, targeted lookups, real-time-ish workflows | Ad hoc analysis in Claude and other AI tools, no code required |
| Delivery | Snowflake share | REST endpoints | MCP connector |
| Typical user | Data engineer, analytics lead | Application developer | Analyst, account team, executive |
They compose. Teams commonly query the Data Lake for scale, hit the API in-product, and use the MCP server for questions nobody wants to write SQL for.
09
FAQ
How fresh is the data?
Core tables refresh twice weekly.
Do we need to ingest anything?
No. The share is native to Snowflake and nothing gets copied into your storage.
Can we choose specific marketplaces?
Yes, scope is custom.
Do we get web app access?
Yes. Data Lake clients receive SmartScout Portal seats.
How much history is included?
It depends on the table. Most historical tables carry 6–24 months.
Are there query limits?
None from us. You query on your own compute.
What if we do not use Snowflake?
That's fine. Your SmartScout rep can scope a delivery path that works for your stack.
Do you offer implementation help for the first queries?
Yes. If you need it, your SmartScout rep can help you map out a path forward.
10
Next step
Existing enterprise clients
Your account manager can adjust scope, add marketplaces, or walk your team through the schema on a working call.
Contact your account manager