Enterprise
You run a hundred waiting rooms. One dashboard covers them.
The per-property ranking, per-operator scorecard, and early-warning signals your operations team has asked for — running on the same platform a single-branch shop runs on.
Live ops, scoped to your org
Five numbers at the top of the dashboard that you can glance at between meetings. Computed on every load — no caching, no staleness window.
- Customers waiting right now across your properties
- Customers served today
- Longest current wait
- Properties live right now
- Operators on duty in the last fifteen minutes
Every property, every day, side-by-side
The owner’s view ranks every property you run on the same set of measurements. You can see which branch has the long tail in its wait times before a customer complains, which location keeps hitting capacity, and which operators run the tightest service times.
What every row carries
- Daily joins, daily served, and daily left-before-served counts
- Mean, median, and 95th-percentile wait time (spots the tail before a customer does)
- Mean service time
- Share of joined customers who were served end-to-end
- Share of customers who abandoned the queue
- Share of customers who were called but did not arrive
- How often the queue filled to capacity
- Peak simultaneous waiters and busiest hour of the day
- Walk-ins added by operators (customers without the app)
- Hours returned to your customers for the day
Operator performance, at a glance
Every staff member’s daily scorecard. Two exceptions surface automatically, so you see the signal without scanning the noise:
- Operator-performance signals you can act on.
- Property-level abandonment trends.
- Properties that hit queue capacity on a given day.
Per-operator scorecard
- Customers handled today
- Customers missed (so you can see service quality at a glance)
- Seconds per customer
- Share of assigned customers completed
- The hour of the day this operator is most productive
Trend charts across any date range
Four time-series panels, with per-property overlays when you pick a subset:
- Wait metrics. Mean and tail wait time over time.
- Throughput. Customers joined vs customers served.
- Abandonment. Left-before-served and no-show rates.
- Value returned. Hours saved per day.
Early warning: spot a problem location before the revenue does
The platform maintains a monthly risk indicator per organisation, paired with a repeat-customer signal. Together they surface which locations are starting to lose momentum before the revenue number catches up.
For a chain, this is the difference between noticing a problem at the quarterly review and noticing it on the fifteenth of the month.
The pilot offer
We will set up your first three properties, train your first three operators, and run with you for thirty days before we decide together whether this is a fit.
If it is a fit, we sit down and work out the SLA and the commercial terms. If it is not, we hand you your data export, shut down the environment, and shake hands.
Ready for a briefing?
enterprise@intimeq.com reaches a human. A 30-minute call is enough to tell you whether InTimeQ fits your operation.