Find the store-level actions that improve sell-through.
Start inside each store with POS, inventory, in-store sales, online sales attributed to that location, and local demand. Compare performance across relevant stores, then roll results up by city, state, region, or the entire retail operation. Path Analytics explains what is happening, why it is happening, and which demand-led action the team should review next.
Start in-store. Compare across stores. Roll up performance.
Give store managers, inventory teams, and warehouse operations one path from local demand to coordinated action. Ask questions in familiar retail terms without needing to be a data analyst or AI specialist.
1. Understand In-Store Performance
See in-store sales, online sales attributed to that location, sell-through, available inventory, and local demand together. Where footfall data is connected, include visits and POS conversion to understand what is working and what needs attention.
2. Compare Across Stores
Compare each location with relevant stores and expected performance. Find where demand is converting, where inventory is sitting, and which gaps a chain average can hide.
3. Roll Up Performance
Move from stores to city, state, region, and company-wide views. Compare aggregated demand, inventory, sell-through, and revenue, then drill back to the locations behind the result.
Explain Revenue Gaps
Connect weak sell-through with inventory, pricing, local demand, and customer signals to understand why revenue is below expected performance.
Coordinate Inventory with Demand
Show store managers where demand is active. Give inventory and warehouse operations the evidence to review replenishment, transfer, or online-listing decisions.
Use AI Without Technical Expertise
Ask about a store KPI, trend, product, or action in familiar retail language. See what changed, why it matters, and the recommended course of action without building a report or model.
Why is Store #114 missing demand while stock is available?
A store manager sees the local demand gap in plain English. Inventory and warehouse operations see the supporting stock context and the action to review.
Performance gap
Sell-through is below expected performance for comparable locations while local demand and stock are available.
Connected coverage: POS & store sales, local demand & inventoryPath Analytics recommendation
Review a DC transfer to Store #114 after checking DC availability and the needs of other stores.
Built-in AI: explains the store gap with expected performance & evidenceTeam review and checkpoint
Inventory Operations confirms availability. Store Operations executes the transfer, then leadership checks sell-through after 7 days.
Team decision: store + inventory operations, reviewed action & checkpointFrom a store performance gap to a reviewed action.
Path Analytics surfaces the gap and recommends the next action. Your team decides, acts, and reviews.
Connect
Connect POS, inventory, in-store sales, and store-attributed online sales for one location-level operating view.
Detect
Compare inventory with demand inside each store, across relevant locations, and in city, state, or regional rollups.
Recommend
Explain what happened and why in plain retail language, then surface the best-supported action and estimated opportunity.
Act & Review
Store, inventory, and warehouse teams decide and execute, then review the observed impact at the agreed checkpoint.
In-depth retail analytics with a clear operating hierarchy. Start with inventory and demand inside one store, compare it with relevant locations, then roll up to city, state, region, and company performance before drilling into categories, products, and SKUs.
Give every store manager a clearer next action.
Walk through what is happening at a store, why performance differs from similar locations, and the action your team can review to improve sell-through and revenue.