Device Analysis: Monitor Every PC in Your Club
Device Analysis: Monitor Every PC in Your Club
Section titled “Device Analysis: Monitor Every PC in Your Club”The Device Analysis section in IZI CRM gives you per-device visibility into sessions, revenue, and behavioral patterns — without having to review every transaction manually. Open it via Analytics → Suspicious Activity → Devices. For each registered PC or console, the system displays session count, total paid gaming time, revenue, guest-login share, and any activity logged outside your club’s operating hours. The core logic is simple: identical devices in the same zone should produce similar numbers. A systematic outlier usually means either a hardware issue (frequent crashes, uncomfortable location) or a process violation (sessions opened outside the billing system). Device Analysis is your entry point — it tells you where the anomaly is; the linked sections tell you who and how.
What the Device Table Shows
Section titled “What the Device Table Shows”The main screen is a table with one row per registered device. Use the period filter at the top to set your window; the default is the current week. You can also filter by zone to compare like-for-like equipment within a single segment.
| Column | What it means | What to look for |
|---|---|---|
| Device | PC name or seat number | Identifies the specific unit |
| Zone | Pricing zone of the device | Enables same-zone comparison |
| Sessions | Number of sessions in the period | Below-average count signals downtime or process bypass |
| Time (h) | Total paid gaming hours | Primary utilization metric |
| Revenue | Realized revenue from the device | Should be comparable to neighboring PCs in the same zone |
| Guest logins | Sessions without a registered account | A few is normal; a consistent pattern is a signal |
| Off-hours activity | Sessions outside the operating schedule | Any non-zero value requires explanation |
What Is Normal vs. a Signal
Section titled “What Is Normal vs. a Signal”There is no universal threshold — it depends on your tariff matrix, seat count, and typical traffic. Three relative criteria work across most club setups:
- Device revenue < 0.6× the zone median — systematic underutilization or revenue loss
- Guest-login share > 15% of that device’s sessions — unusually high for a staffed club
- Off-hours activity > 0 — any event outside operating hours needs a documented explanation
Utilization formula for a single device:
Utilization (%) = Total paid hours ÷ Club operating hours in the period × 100Calibrate your baseline using your own best four weeks — that is your realistic ceiling, not an abstract industry figure.
Three Patterns Worth Watching
Section titled “Three Patterns Worth Watching”1. Systematically Low Utilization
Section titled “1. Systematically Low Utilization”A PC in the middle of the floor generates half the sessions of its neighbors. Two likely explanations:
- Hardware issue: slow boot, frequent crashes, or an uncomfortable location (high-traffic aisle, noisy equipment nearby)
- Process bypass: some sessions are being run outside the system — payment collected without logging a session
What to do: break the device’s utilization down by day of week and shift. If the drop recurs in specific shifts, cross-reference with the shift report and map to the responsible employee.
2. Guest Logins Without a Clear Pattern
Section titled “2. Guest Logins Without a Clear Pattern”A guest session — opened without a registered account — is a legitimate tool for walk-in customers who prefer not to sign up. Normal level: 3–5% of sessions per device. If one PC shows 20–30% guest sessions while neighboring devices show 3–5%, investigate. Likely scenarios: an employee opens guest sessions for acquaintances to avoid debiting real balances, or some payments are not flowing through the system at all.
Check: open the guest-session detail view for that specific PC and look at the timestamps and the shift active at the time.
3. Activity Outside Operating Hours
Section titled “3. Activity Outside Operating Hours”If the club operates 10:00–02:00 and the system logs a session on a specific PC at 04:00, that is an anomaly requiring immediate review. Possible causes: an unclosed session (employee forgot to end it), unauthorized device access, or a misconfigured operating schedule.
IZI timestamps every event and ties it to the active shift. Any activity outside the schedule appears in the off-hours column; from there you can navigate to the technical log for the full timeline.
How to Read Device Analytics: Step-by-Step
Section titled “How to Read Device Analytics: Step-by-Step”Recommended cadence: weekly review every Monday, covering the previous week. Takes 5–10 minutes.
Step 1. Open Analytics → Suspicious Activity → Devices. Set the filter to “last week.”
Step 2. Sort the table by Revenue (ascending). The bottom rows are your investigation candidates.
Step 3. For each device in the bottom third, compare its metrics to the median for the same zone. A deviation above 40% warrants detailed review.
Step 4. Check the Guest Logins column. If the share is above your normal baseline, open the detail view: what time, whose shift.
Step 5. Check the Off-Hours Activity column. Any non-zero value — read the event detail with timestamps.
Step 6. Log the anomalies. If the same pattern appears for a second consecutive week, escalate: first review that employee’s shift report, then move to Suspicious Staff for a full operations breakdown.
Parametric Benchmarks for Device Comparison
Section titled “Parametric Benchmarks for Device Comparison”There is no single “correct” weekly session count — it depends on operating hours, seat count, tariff structure, and seasonality. Calculate from your own data:
Target revenue per device per week = Total club revenue for the week ÷ Number of devices
This is a rough floor-level average. Devices in a premium zone will naturally exceed it; standard-zone devices should land near it. Always compare within a zone, not across zones.
Utilization benchmark:
Normal device utilization (%) ≈ Zone average utilization (%) × (1 ± 15%)Where zone average utilization comes from the session load report. A device deviating more than 30% from its zone’s average is an anomaly that needs an explanation.
How Device Analysis Connects to Other Tools
Section titled “How Device Analysis Connects to Other Tools”Device Analysis works in sequence with the rest of the Suspicious Activity section and the broader analytics suite. Recommended path from anomaly to resolution:
| What you found | Next step |
|---|---|
| Low utilization on a specific PC | Session load report — confirm the pattern on the heat map |
| Guest logins concentrated in certain shifts | Shift report — map to the specific employee |
| Anomaly repeats for one employee | Suspicious staff — drill into that shift’s operations |
| Activity outside operating hours | Technical log — full event timeline with timestamps |
| Unusual balance movements on the device | Balance operations — detail view on credits and debits |
Device Analysis shows where. The other sections answer who and how.
Frequently Asked Questions
Section titled “Frequently Asked Questions”How often should I review device analytics?
Section titled “How often should I review device analytics?”Weekly is sufficient for most clubs — 5–10 minutes on Monday covers the previous week. For day-to-day monitoring, the daily analytics view is enough; a weekly device-level pass will catch the majority of risks before they compound.
What if a device has zero off-hours activity but revenue is still below average?
Section titled “What if a device has zero off-hours activity but revenue is still below average?”Low revenue with a clean behavioral profile points to a hardware or UX issue rather than a process violation. Check whether players frequently move away from that PC during sessions, and whether there are recurring complaints about freezes or lag. If confirmed, this is a maintenance task — not a security investigation.
Can device data be used in a conversation with an employee?
Section titled “Can device data be used in a conversation with an employee?”Yes. System data is objective evidence. Present specific rows: device name, date, time, session type. Concrete data is more effective than general statements. Keep in mind that analytics records facts — the interpretation (intentional vs. mistake) should be established through conversation.
Frequently asked questions
Where do I find Device Analysis in IZI?
Go to Analytics → Suspicious Activity → Devices tab. At the top you can select the period and club. The default view shows the current week.
What does the Device Analysis section show?
A table with one row per registered device (PC, console). Each row shows session count, total paid gaming time, revenue generated, guest login share, and any activity outside the club's operating schedule.
How do I tell if something is wrong with a specific PC?
Compare that device's metrics to the median for other devices in the same zone. A device with revenue below 0.6× the zone median, or a guest-session share above 15%, or any off-hours activity warrants investigation.
What is a guest session on a device?
A session opened without a registered client account. Occasional guest logins are normal. A pattern where one PC consistently has a high share of guest sessions — while neighboring devices do not — is a signal to investigate.
Can I filter devices by zone?
Yes. The section's filter lets you select specific zones, so you can compare like-for-like equipment — for example, only VIP-zone PCs or only console stations.
How do I calculate a normal utilization rate for a device?
Formula: (total paid session hours for the period) ÷ (club operating hours for the same period) × 100%. Set your baseline from your own best four weeks — not an abstract benchmark.
What counts as off-hours activity on a device?
Any session or balance movement that occurs when the club is officially closed or in a maintenance break. IZI timestamps every event, so deviations from the operating schedule appear in the dedicated column.
How does Device Analysis connect to Staff Analysis?
Every device event is linked to the shift that was active at the time. If an anomaly on a specific PC repeats during one employee's shifts, move to the Suspicious Staff section to drill into that shift's operations.
Can I export device data?
Yes. An Export to CSV button is available in the top-right corner of the section. Useful for comparing multiple periods side by side or sharing data with a manager.