Retail
How does store profitability compare across the network?
Revenue and profitability are not the same ranking -- a store can look strong on sales while quietly losing money once real cost is included. FactSmith puts every store's margin, not just revenue, side by side, so the comparison that actually matters is the first thing you see.
Why this matters
Assembling a true profitability view store by store today usually means combining sales, cost and overhead data from separate systems by hand.
What data is needed
Store sales, cost of goods, and -- where available -- allocated overhead by store.
The evaluation copy runs against PostgreSQL. SQL Server is on the adapter path for a customer warehouse; that path is not what the package proves end to end today.
How the analysis works
Margin is computed per store with the same cost logic applied everywhere, rather than each store manager's own spreadsheet definition, so the comparison is apples to apples.
What can go wrong
Stores with different overhead-allocation methods can look artificially better or worse. A recently opened store needs a ramp-up period excluded, or it will always rank last.
Example
A regional retail group asks which stores rank lowest on margin this quarter, not just revenue. Ask returns the ranked list with the revenue-vs-margin gap called out.
How FactSmith approaches it
Ask is what is available now. Easy Data -- turning messy sources into that dimensional foundation -- is in early development.
FactSmith calls this the Fact Layer: a data integration (ETL) step, a knowledge base, and a business ontology, working together underneath the answer. On screen it stays simple -- ask a question, get a trusted answer -- with the layer-by-layer detail available to anyone who wants to see how the answer was built.
Permissions are enforced beneath the AI, not by it -- see Trust and security.