Real-Time Analytics Dashboard for a Retail Chain
Multi-location analytics dashboard with live sales, inventory, staff performance and customer metrics. Replaced end-of-day reports with real-time operational visibility.
Retail chain — 8 stores across metropolitan Perth
A WA retailer operating 8 stores across Perth. Each store ran its own POS system, and data was reconciled nightly. The operations team received end-of-day sales summaries the following morning. Inventory counts were done weekly. Staff performance was assessed monthly.
The CEO described the experience as "driving with the rear-view mirror." Every decision — stock transfers, staff adjustments, promotional changes — was based on data that was 12–24 hours old at best.
What needed to change
No real-time visibility across stores. Each store operated as a data island. The head office did not know how stores were performing until end-of-day reports arrived the next morning. A store could be running out of a key product all day with nobody aware.
Inventory management was reactive. Weekly stocktakes meant 7-day gaps in accuracy. Stock transfers between stores were based on gut feel. Popular items sold out while sitting unsold in other locations. The company estimated $200K/year in lost sales from stock-outs.
Labour scheduling was disconnected from demand. Rosters were set weeks in advance based on historical averages. Busy periods were understaffed and quiet periods were overstaffed. No correlation between foot traffic patterns and labour allocation.
What we built
A real-time analytics dashboard aggregating POS, inventory, labour and customer data across all 8 stores — delivering live operational intelligence to managers and executives.
Sales Dashboard
Live sales data across all stores — revenue, transactions, average basket, product mix and hourly trends. Compare stores, track against targets and identify top/bottom performers in real time.
Inventory Intelligence
Real-time stock levels by store and product. Automated stock transfer recommendations when one store is low and another is overstocked. Reorder alerts based on sales velocity.
Labour Analytics
Sales per labour hour by store and shift. Traffic patterns correlated with staffing levels. Recommendations for roster adjustments based on predicted demand.
Customer Insights
Transaction patterns, repeat purchase rates, basket composition and customer segment analysis. Identify trends and opportunities across the chain.
How it works
POS data streams in real time
Every transaction across all 8 stores is captured and streamed to the analytics platform. Data is processed and available on the dashboard within seconds.
Dashboards update live
Store managers see their store performance. Area managers see regional comparisons. Executives see the whole chain — all updating in real time throughout the day.
Alerts trigger on anomalies
System detects unusual patterns — sales significantly below forecast, stock levels critically low, unusual void/refund activity. Alerts sent to the relevant manager immediately.
Stock transfers recommended
When Store A is running low on a fast-selling item and Store B has surplus, the system recommends a transfer with quantities and logistics. Manager approves with one click.
Weekly insights report generated
End-of-week report compiles trends, top performers, stock recommendations, labour efficiency and customer insights. Auto-generated and emailed to the leadership team.
Measurable outcomes
We went from driving with the rear-view mirror to having a live GPS. I can see every store right now — sales, stock, staffing. Yesterday a store was running low on our top seller and we had surplus 10km away. One click transfer, no lost sales.
How we delivered it
Data Architecture
2 weeksAudited all data sources across 8 stores — POS systems, inventory tools, rostering software. Designed the real-time data pipeline and decided on event streaming for POS data and batch sync for inventory and labour.
Pipeline & Backend
4 weeksBuilt the data ingestion pipeline with Kafka for real-time POS events and batch connectors for other sources. Created the analytics data model and pre-computed metrics for dashboard performance.
Dashboard & Alerts
3 weeksBuilt the dashboard interface with role-based views (store manager, area manager, executive). Configured alerts, anomaly detection and stock transfer recommendations.
Launch
1 weekConnected all 8 stores. Validated data accuracy against the existing end-of-day reports for 1 week. Launched with a morning briefing format — executives start each day with the live dashboard.
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