Multi-Store Performance Analysis
How to compare performance across multiple retail locations, identify your best and worst-performing stores, and use data to drive investment decisions.
Why Multi-Store Comparison Is Complex
No two stores are identical — they differ in size, location, footfall, demographics, competition, and team. A straightforward revenue comparison is often misleading: a store in a premium location may generate more revenue but less profit than a smaller store with lower costs.
Effective multi-store analysis accounts for these differences by using metrics that normalise for store size and cost — and by comparing stores against their own history as well as against each other.
Setting Up Multi-Store in AskBiz
If each store has its own POS connection, AskBiz automatically groups transactions by store ID and displays them in Sales → By Store.
For stores with the same POS platform (e.g. multiple Square or Shopify POS locations), the integration automatically separates data by location.
For stores with different POS systems, connect each separately and tag them by store name. AskBiz consolidates all store data into a unified multi-store view.
You can also assign team members to specific stores under Settings → Team → Store Assignment, so store managers see only their store's data by default.
Key Metrics for Store Comparison
Revenue per sq ft (or per m²): the standard retail density metric. Revenue ÷ store floor area. Normalises for store size.
Revenue per staff hour (RPSH): revenue ÷ total staff hours. Normalises for staffing levels.
Gross margin %: compare across stores — if one store consistently has lower margin, check for different product mix, pricing variances, or higher shrinkage.
Conversion rate (if footfall data available): compare how effectively each store converts traffic.
Average transaction value: differences often reflect local demographics, store layout, or staff upsell capability.
Like-for-like growth (LFL): compare each store's growth against the same period last year, using the same store perimeter. This is the standard way to strip out the effect of opening new stores.
Acting on Multi-Store Insights
High revenue, low margin store: investigate COGS (different product mix?), shrinkage (higher in this location?), or labour costs (overstaffed relative to sales?).
Low footfall, high conversion store: the team converts well but doesn't have enough traffic. Consider local marketing, better signage, or a pop-up activation to drive awareness.
High footfall, low conversion store: you have the traffic but aren't converting it. Review store layout, product range, pricing, and staff performance.
Underperforming store with no obvious fix: run a full contribution margin analysis (revenue minus all directly attributable costs including rent and staff). If the store is cash-negative even at full capacity, the lease economics may not work — plan an exit before the lease renewal.