View Restaurant Performance Report
Use the restaurant performance report to review dining-room activity for Harbor Soho across selected periods: account and cover totals, reservation mix, new versus returning reservation customers, weekday and hour seasonality, and party-size patterns.
In this article
This guide explains how to read the restaurant performance page inside Antylogic's reporting dashboard. In the inspected app, the report page and blocks still use Spanish UI labels such as Tu restaurante, Restaurante, Reservas / sin reserva, Clientes nuevos vs. clientes recurrentes, Perspectivas sobre la estacionalidad and Tamaño del grupo.
The page is read-only. It loads the aggregated reporting dashboard for the selected location and then shows the restaurant section, so managers can compare service periods without changing tickets, reservations or service sessions.
Before you start
- Select Harbor Demo Group > Harbor Soho before opening the report.
- The user must have report access for the location through at least one accepted permission: reports:view, tpv:view, tpv:use, catalog:view, reservation:view or staff:view.
- Open the page from Reportes > Rendimiento > Tu restaurante, or use /:orgId/:locationId/reporting/performance/restaurant.
- To see populated metrics, the selected period must contain service sessions and active non-void tickets. Reservation and customer blocks also need linked reservations with CustomerID.
- For the GB demo capture plan, start with periods=2026-06-01_2026-06-07 because the runtime service occurrence starts on 2026-06-02.
Instructions
- 1.
Open Tu restaurante
From Harbor Soho, open the reporting area and choose the implemented page Tu restaurante under performance. The direct contextual route is /:orgId/:locationId/reporting/performance/restaurant.
- 2.
Choose the reporting period
Use the period filter to compare one or more date ranges. If the route has no periods query, the app starts from the current week and writes the selected range back into the route.
- 3.
Read the Restaurante summary
Start with the summary block. It shows total accounts, total seats, average table time, average account value, average per seat and average seats per account for each selected period.
- 4.
Compare reservations and customer mix
Use Reservas / sin reserva to compare accounts linked to reservations with walk-ins. Use Clientes nuevos vs. clientes recurrentes to understand whether reservation customers had previous reservation history.
- 5.
Review seasonality
Scroll to Perspectivas sobre la estacionalidad to compare performance by weekday and hour. The default view uses total restaurant accounts, and local controls can narrow the view to reservations or walk-ins.
- 6.
Review group size
Scroll to Tamaño del grupo to compare party-size buckets. The default tab shows average time, with additional tabs for average account and average per person.
Validations and limits
- If no location is selected, the app asks the user to select a location instead of loading the report.
- If the user lacks all accepted report-access permissions, the app does not query the dashboard and shows a permissions message.
- The period query format is YYYY-MM-DD_YYYY-MM-DD, with multiple periods separated by ~; invalid segments are ignored.
- Backend calculations use the location timezone. Harbor Soho uses Europe/London.
- There is no GET /reports/restaurant endpoint. The frontend loads GET /api/app/{orgId}/{locationId}/reports and reads pages.restaurant from the dashboard response.
- Restaurant calculations include active tickets only; tickets with status void are excluded.
- New versus returning customers only counts reservations with CustomerID and excludes cancelled, waitlist and hold reservations.
- The heatmap omits cells with no tickets or values of zero or less, so an empty cell can be a valid data result.
- The group-size bucket labelled 7-8 also includes parties larger than six.
- Report export is available on web; native platforms show an export-unavailable alert.
Expected result
Managers can read how Harbor Soho performed across selected periods, compare reservation and walk-in behaviour, spot weekday and hour patterns, and review party-size trends without changing operational data.