METHODOLOGY
Turning Raw Data Into Valuable Insights
Next Big Shop tracks estimated sales, plus a host of additional metrics, for private and public DTC brands. Here’s how we do it.
Transparency = trust, so we’re breaking down exactly how we gather data and estimate sales. We aim to build the most reliable revenue estimates for DTC online—here is how.
Daily SKU Crawling
We’re building Next Big Shop to answer a question — how does a shop go from obscurity to popularity?
From packing boxes in their apartment to having nationwide distribution or being publicly traded. In order to answer this, we need a measure of success. In the music industry, a proxy for this, is an appearance on a Billboard chart. In the world of e-comm, our closest proxy is estimating DTC sales. And we’ll be continuing to add other proxies to improve our sales model. Since this data is rarely made public, we use checkout pages to check and track changes in inventory.
Most sales estimates of private DTC companies today are not reliable. The standard today for estimating sales is applying a conversion rate to web traffic or observing LinkedIn headcount (yes, really). We think there’s a better way.
We’re deploying a more accurate methodology for measuring sales: Inventory Depletion.
Here’s how it works:
Inventory is checked daily and the difference from the previous day is calculated then multiplied by price of goods sold
SKU data is rolled up to products which are rolled up to stores
Inventory increases and decreases outside expected bounds are interpolated to account for non-sales impacts on revenue like restocks, returns, SKU consolidation, and inventory destruction
Today, we only show sales based on the coverage we do have available. In the future, we will incorporate other signals like web traffic, social, and yes, LinkedIn headcount (cause, why not?) into our sales model.
Data Library
Explore our comprehensive Data Library, where we catalog the growing number of metrics we track for Shops.
Basic Shop Information
Shop Name
Shop Image
Category
Currency
Shop Website
Next Big Shop Profile LinkData Quality & Inventory Coverage
Every shop profile page includes a data quality module with crawling status, crawling history, and inventory coverage from 0 to 100%.
Total Inventory Coverage
We have extensive or substantial coverage for 69% of shops.
Stores that are set up to keep selling after running out of inventory can appear to sell less than they are - we refer to the percentage of inventory we can track (not set to keep selling after running out of stock) as inventory coverage.
Depending on the total percentage of inventory Next Big Shop tracks for a given store, we assign a score from 0 to 100 and label our coverage from minimal to extensive. This shows in a badge below sales estimates at the top of shop profile pages and as a gauge in the data quality section.
We have five inventory coverage levels:
Zero Coverage (0% Coverage)
Minimal Coverage (Less than 25%)
Partial Coverage (25% to 49%)
Substantial Coverage (50% to 89%)
Extensive Coverage (90% or more)
The pie chart above shows where all the shops we’re tracking land within the levels.
Data Quality Communication
Communicating known issues with the inventory depletion methodology.
We’re communicating whenever we detect potential issues that can impact the quality of estimates.
Known potential issues:
Stores continuing to sell after running out of stock
Shown below every estimate via the inventory coverage from 0 to 100%
Stores with bundles appearing to sell more than they are
Shown as a % of bundle sales and the label in the "Best Sellers" section
Shops with more than 5k SKUs
Not being crawled unless requested
Future Improvements
Showing stores that limit items per customer appearing to sell more than they are
Frequency of inventory syncing with third-party systems
Inventory shared across channels beyond DTC appearing high
As we continue to communicate about data quality, we’ll keep this section updated.
FAQ
I’m looking at a shop’s numbers they’re wildly off compared to what I’m seeing or have been told. What’s up?
Are shops okay with this?
Can I help you validate data?
METHODOLOGY
Turning Raw Data Into Valuable Insights
Next Big Shop tracks estimated sales, plus a host of additional metrics, for private and public DTC brands. Here’s how we do it.
Transparency = trust, so we’re breaking down exactly how we gather data and estimate sales. We aim to build the most reliable revenue estimates for DTC online—here is how.
Daily SKU Crawling
We’re building Next Big Shop to answer a question — how does a shop go from obscurity to popularity?
From packing boxes in their apartment to having nationwide distribution or being publicly traded. In order to answer this, we need a measure of success. In the music industry, a proxy for this, is an appearance on a Billboard chart. In the world of e-comm, our closest proxy is estimating DTC sales. And we’ll be continuing to add other proxies to improve our sales model. Since this data is rarely made public, we use checkout pages to check and track changes in inventory.
Most sales estimates of private DTC companies today are not reliable. The standard today for estimating sales is applying a conversion rate to web traffic or observing LinkedIn headcount (yes, really). We think there’s a better way.
We’re deploying a more accurate methodology for measuring sales: Inventory Depletion.
Here’s how it works:
Inventory is checked daily and the difference from the previous day is calculated then multiplied by price of goods sold
SKU data is rolled up to products which are rolled up to stores
Inventory increases and decreases outside expected bounds are interpolated to account for non-sales impacts on revenue like restocks, returns, SKU consolidation, and inventory destruction
Today, we only show sales based on the coverage we do have available. In the future, we will incorporate other signals like web traffic, social, and yes, LinkedIn headcount (cause, why not?) into our sales model.
Data Library
Explore our comprehensive Data Library, where we catalog the growing number of metrics we track for Shops.
Basic Shop Information
Shop Name
Shop Image
Category
Currency
Shop Website
Next Big Shop Profile LinkData Quality & Inventory Coverage
Every shop profile page includes a data quality module with crawling status, crawling history, and inventory coverage from 0 to 100%.
Total Inventory Coverage
We have extensive or substantial coverage for 69% of shops.
Stores that are set up to keep selling after running out of inventory can appear to sell less than they are - we refer to the percentage of inventory we can track (not set to keep selling after running out of stock) as inventory coverage.
Depending on the total percentage of inventory Next Big Shop tracks for a given store, we assign a score from 0 to 100 and label our coverage from minimal to extensive. This shows in a badge below sales estimates at the top of shop profile pages and as a gauge in the data quality section.
We have five inventory coverage levels:
Zero Coverage (0% Coverage)
Minimal Coverage (Less than 25%)
Partial Coverage (25% to 49%)
Substantial Coverage (50% to 89%)
Extensive Coverage (90% or more)
The pie chart above shows where all the shops we’re tracking land within the levels.
Data Quality Communication
Communicating known issues with the inventory depletion methodology.
We’re communicating whenever we detect potential issues that can impact the quality of estimates.
Known potential issues:
Stores continuing to sell after running out of stock
Shown below every estimate via the inventory coverage from 0 to 100%
Stores with bundles appearing to sell more than they are
Shown as a % of bundle sales and the label in the "Best Sellers" section
Shops with more than 5k SKUs
Not being crawled unless requested
Future Improvements
Showing stores that limit items per customer appearing to sell more than they are
Frequency of inventory syncing with third-party systems
Inventory shared across channels beyond DTC appearing high
As we continue to communicate about data quality, we’ll keep this section updated.
FAQ
I’m looking at a shop’s numbers they’re wildly off compared to what I’m seeing or have been told. What’s up?
Are shops okay with this?
Can I help you validate data?
METHODOLOGY
Turning Raw Data Into Valuable Insights
Next Big Shop tracks estimated sales, plus a host of additional metrics, for private and public DTC brands. Here’s how we do it.
Transparency = trust, so we’re breaking down exactly how we gather data and estimate sales. We aim to build the most reliable revenue estimates for DTC online—here is how.
Daily SKU Crawling
We’re building Next Big Shop to answer a question — how does a shop go from obscurity to popularity?
From packing boxes in their apartment to having nationwide distribution or being publicly traded. In order to answer this, we need a measure of success. In the music industry, a proxy for this, is an appearance on a Billboard chart. In the world of e-comm, our closest proxy is estimating DTC sales. And we’ll be continuing to add other proxies to improve our sales model. Since this data is rarely made public, we use checkout pages to check and track changes in inventory.
Most sales estimates of private DTC companies today are not reliable. The standard today for estimating sales is applying a conversion rate to web traffic or observing LinkedIn headcount (yes, really). We think there’s a better way.
We’re deploying a more accurate methodology for measuring sales: Inventory Depletion.
Here’s how it works:
Inventory is checked daily and the difference from the previous day is calculated then multiplied by price of goods sold
SKU data is rolled up to products which are rolled up to stores
Inventory increases and decreases outside expected bounds are interpolated to account for non-sales impacts on revenue like restocks, returns, SKU consolidation, and inventory destruction
Today, we only show sales based on the coverage we do have available. In the future, we will incorporate other signals like web traffic, social, and yes, LinkedIn headcount (cause, why not?) into our sales model.
Data Library
Explore our comprehensive Data Library, where we catalog the growing number of metrics we track for Shops.
Basic Shop Information
Shop Name
Shop Image
Category
Currency
Shop Website
Next Big Shop Profile LinkData Quality & Inventory Coverage
Every shop profile page includes a data quality module with crawling status, crawling history, and inventory coverage from 0 to 100%.
Total Inventory Coverage
We have extensive or substantial coverage for 69% of shops.
Stores that are set up to keep selling after running out of inventory can appear to sell less than they are - we refer to the percentage of inventory we can track (not set to keep selling after running out of stock) as inventory coverage.
Depending on the total percentage of inventory Next Big Shop tracks for a given store, we assign a score from 0 to 100 and label our coverage from minimal to extensive. This shows in a badge below sales estimates at the top of shop profile pages and as a gauge in the data quality section.
We have five inventory coverage levels:
Zero Coverage (0% Coverage)
Minimal Coverage (Less than 25%)
Partial Coverage (25% to 49%)
Substantial Coverage (50% to 89%)
Extensive Coverage (90% or more)
The pie chart above shows where all the shops we’re tracking land within the levels.
Data Quality Communication
Communicating known issues with the inventory depletion methodology.
We’re communicating whenever we detect potential issues that can impact the quality of estimates.
Known potential issues:
Stores continuing to sell after running out of stock
Shown below every estimate via the inventory coverage from 0 to 100%
Stores with bundles appearing to sell more than they are
Shown as a % of bundle sales and the label in the "Best Sellers" section
Shops with more than 5k SKUs
Not being crawled unless requested
Future Improvements
Showing stores that limit items per customer appearing to sell more than they are
Frequency of inventory syncing with third-party systems
Inventory shared across channels beyond DTC appearing high
As we continue to communicate about data quality, we’ll keep this section updated.
FAQ
I’m looking at a shop’s numbers they’re wildly off compared to what I’m seeing or have been told. What’s up?
Are shops okay with this?
Can I help you validate data?
Company
© 2024 Next Big Shop
Company
© 2024 Next Big Shop
Company
© 2024 Next Big Shop