Retail
Which stores are underperforming, and why?
A single store's numbers are easy to read; comparing every store against every other store, consistently, is usually what needs a spreadsheet marathon. FactSmith ranks stores against each other and against their own trend, then lets you ask why -- by category, by promotion, by region -- without leaving the conversation.
Why this matters
Underperformance often stays buried inside a per-store report nobody cross-compares regularly. By the time it is obvious, it has cost a quarter.
What data is needed
Store-level sales, and product and regional facts. Footfall or transaction counts too, where the source system has them.
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
Stores are ranked on a like-for-like basis where format or age differs, rather than raw revenue, so a smaller store is not unfairly compared against a flagship.
What can go wrong
A temporary closure or renovation can look identical to genuine underperformance in a raw revenue comparison. Comparing stores of very different formats without normalising produces the same distortion.
Example
A multi-store group asks which stores are trending below their own prior-year comparable. Ask returns them ranked by the size of the gap, with the category driving it.
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.