Club Load Heatmap: Hourly Occupancy Analysis

Club Load Heatmap: Hourly Occupancy Analysis

The load heatmap in IZI shows what percentage of your club’s seats were occupied during each hour — day by day. Found under Analytics → Session Report → Heatmap, it plots time of day (00:00–23:59) on the horizontal axis and dates or zones on the vertical axis. Cell color intensity reflects occupancy: light means few customers, dark means a peak. Hover over any cell to see the exact percentage. The heatmap updates in real time — an opening session is counted immediately. Its primary use is to surface hours where the club is systematically under-earning: either attract traffic with a lower tariff, push it with marketing, or — when peak hours consistently hit 90%+ — consider raising the price or expanding capacity. Think of it as the bridge between daily analytics (what happened yesterday) and your tariff strategy (what to do tomorrow).

How the heatmap is structured

Axes and cells

Each cell represents the intersection of an hour and a day (or zone). The value:

Occupancy (%) = occupied seats in that hour ÷ total seats × 100

A seat counts as occupied if at least one active session ran during that hour. A session that starts at 19:30 and ends at 21:15 occupies its seat in the 19:00–20:00, 20:00–21:00, and 21:00–22:00 buckets.

Color scale

Occupancy range	Visual signal	Meaning
0–25%	Very light	Dead hour, almost no customers
25–50%	Light	Low demand
50–75%	Medium	Normal working load
75–90%	Saturated	Active period
90–100%	Dark / maximum	Peak — almost no free seats

Filters

IZI offers several heatmap filters:

• Period — any custom date range. For a reliable typical-week picture, use at least 4 weeks to smooth out random outliers.
• Zone — if your club has multiple zones (VIP, standard, consoles, simulators), view the heatmap for each zone individually.
• Day-of-week aggregation — collapses all Mondays in the period into one row, all Tuesdays into another, and so on. This reveals the “typical weekday” profile without noise from specific dates.

Common load patterns and what to do about them

Patterns vary by club type, city, and audience — but several structural shapes repeat across most venues.

Pattern 1: Classic evening peak

Morning (09–14)  ░░░░░  10–20%
Afternoon(14–18) ▒▒▒▒▒  30–50%
Evening (18–23)  █████  70–90%
Night   (23–06)  ▒▒░░░  20–40% (if night tariffs exist)

This is the most common profile. Morning and early afternoon are empty — customers are at work or school. Growth starts after 14:00; the peak runs from 18:00 to 23:00.

What to do:

• For morning hours — a discounted daytime tariff (guideline: 0.5–0.7× your standard hourly rate).
• For the evening peak — monitor whether customers are turned away due to full capacity; if occupancy sits at 90%+, consider a prime-time rate above the base.

Pattern 2: Double peak (lunch + evening)

Common in clubs inside shopping malls or office districts. Occupancy rises at 12:00–14:00, dips, then climbs again in the evening. A lunch tariff or “lunch combo” is a natural response.

Pattern 3: Flat load with no clear peaks

If the map is nearly uniform, interpret it in both directions:

• Flat at 60–70% all day — good: the club is stable.
• Flat at 20–30% all day — bad: no traffic in any slot. The problem is customer acquisition, not tariff structure.

Pattern 4: Specific weekday dips

Monday and Tuesday being lighter than Friday and Saturday is normal. But if Wednesday suddenly becomes lighter than Tuesday with no obvious cause, check for technical issues, staff changes, or a competitor event specifically on that day.

How to read the heatmap: step-by-step scenarios

Weekly review (10–15 minutes)

1. Open Analytics → Session Report → Heatmap.
2. Set the period: last 4 weeks.
3. Enable day-of-week aggregation — the map shows your “typical Monday,” “typical Tuesday,” etc.
4. Identify the lightest columns (hours) — these are your chronically underloaded slots.
5. Assess whether pulling traffic there is realistic: 04:00–07:00 is unlikely; 10:00–13:00 on weekdays has potential.
6. Identify the darkest columns — if occupancy is 90%+, check whether you have a corresponding tariff above the base rate.
7. Compare occupancy across zones (if multiple exist) — a chronically empty zone is a candidate for repurposing.

Situational analysis: why yesterday was slow

1. Set the date to yesterday.
2. Look at the heatmap hour by hour — is it a single-hour dip or the whole day?
3. Sharp drop in one hour, then back to normal → likely a technical issue or brief external cause.
4. Whole day lighter than usual → external factor (holiday, competitor, weather) or an acquisition problem.
5. Cross-reference with daily analytics — there you’ll see revenue, session count, and AOV for the same day in detail.

Connecting the heatmap to tariff strategy

The heatmap answers “when”; the tariff sales report answers “what people buy.” Together they give a complete picture.

Finding the right price for an off-peak slot

Use a parametric formula instead of guesswork:

Target occupancy for the slot = X%  (you decide, e.g. 40%)
Current occupancy              = Y%
Gap                            = X − Y  (extra seats you need to fill)

Approximate discount from base tariff:
  (X − Y) / X × 100%  ≈ % discount from base

Example without absolute numbers: if you want 40% occupancy in morning hours but currently sit at 15%, the gap is 25 percentage points — 63% of the target. A first step is a 30–40% discount from the base hourly tariff in that slot, then observe the heatmap again after 2 weeks.

IZI lets you assign different tariffs to different times of day directly in the tariff matrix settings — changes take effect on the next session immediately.

Signal to raise the prime-time rate

If peak hours show 90%+ occupancy consistently for 3+ weeks in a row, the market is ready to pay more. A practical guideline: try raising the tariff for those hours by 10–15% from the current rate and monitor occupancy for another 2 weeks. If it drops below 70%, revert. If it stays above 80%, the increase is justified and revenue has grown.

Heatmap by zone: comparing demand across the floor

When your hall is divided into zones, the heatmap becomes a space-management tool.

Situation	Conclusion	Action
VIP zone 90%, standard 40%	High demand for VIP	Expand VIP or raise VIP pricing
Consoles 20%, PCs 80%	Weak demand for consoles	Revisit console tariffs or promotions
All zones 90%+	Overall seat shortage	Consider expansion or dynamic pricing
One zone at 10% consistently	Zone is not needed in its current format	Repurpose or remove

Run this zone analysis monthly to make floor-reconfiguration decisions from data, not intuition.

What the heatmap does not show

It is important to understand the tool’s limits:

• The cause of a dip or peak — the heatmap records the fact, not the reason. Look for causes in the shift report (what was the staff doing?), in daily analytics (revenue and sessions that day), or through direct customer feedback.
• Session quality — the heatmap counts occupied seats but does not show how much a customer spent or whether they bought from the bar. AOV and sessions per player metrics live in the Session Report metrics section.
• Future demand — the heatmap works with historical data only. Trend-based forecasting is your own interpretation; there is no automatic prediction.

Setting your own thresholds: parametric benchmarks

There is no single “normal” occupancy for all clubs. Calculate your own baseline:

Step 1. Pull the heatmap for the last 8 weeks.

Step 2. For each hour of the day, find the average occupancy across that period — this is your baseline for that hour.

Step 3. Set a downside alert threshold: baseline × 0.7. If a specific hour drops below this, investigate.

Step 4. Set an upside opportunity threshold: if an hour is consistently above baseline × 1.15, revisit its tariff.

Recalculate baselines quarterly — seasonality, audience shifts, and new tariffs all move the norms.

Related sections

• Daily Analytics — drill into a specific day: revenue, sessions, and AOV alongside the heatmap for the same date.
• Tariff Sales Report — which tariffs sell in which hours; connects demand patterns to the tariff matrix.
• Shift Report — staff activity and transactions per shift; context for explaining anomalies on the heatmap.
• AOV — Average Order Value — average transaction size metric; complements occupancy data with revenue-quality insight.
• Sessions per Player — customer return frequency; influences predictable baseline load.