Cap Cantons
Centralized dashboard and customer traffic forecasting
Data centralization across 8 establishments, unified dashboard creation, and predictive traffic model development to optimize scheduling and inventory.
Client information
Life before Connect it
Cap Cantons is a group of 8 establishments in the restaurant, hospitality, and microbrewery industries, located in Quebec's Eastern Townships.
One of the biggest challenges in the restaurant industry is forecasting customer traffic. Depending on the season, day of the week, time, weather, and events, customer numbers vary greatly. Underestimation means insufficient staff, affecting service quality. Overestimation leads to unnecessary payroll expenses that eat into profit margins.
At Cap Cantons, sales report data from Maître D was manually compiled into Excel files to measure establishment performance and evaluate budget targets. This was extremely time-consuming, increased error risk, limited analysis scope, and made document management complex. Sharing analyses with managers was done via email.
Key challenges
The solution
We first centralized information from 5 different applications (Libro, Maître D, Google Calendar, Agendrix, and Weather Network) into a centralized data warehouse, automatically updated and serving as a single source of truth.
We then created a unified dashboard using a modern business intelligence tool, accessible to managers across all establishments. The tool provides performance indicators, cross-establishment comparisons, and data visualizations.
Finally, we used the collected data to create a predictive customer traffic model, identifying shifts at risk of being overstaffed or understaffed and adjusting schedules accordingly.
Centralized data warehouse
Automatic centralization of data from Libro, Maître D, Google Calendar, Agendrix, and Weather Network into a single source of truth.
Unified dashboard
Business intelligence tool accessible to all managers with performance indicators, cross-establishment comparisons, and data visualizations.
Predictive traffic model
Customer traffic prediction to identify shifts at risk of overstaffing or understaffing and adjust schedules accordingly.
Cross-establishment comparison
Ability to easily compare performance across the group's 8 establishments for better information sharing.
Results after implementation
Data centralization and the dashboard completely eliminated manual compilation work, and the accounting department recovers 3 hours per week. Managers now have access to real-time analytics and traffic forecasts to optimize scheduling and inventory.
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