Segment ARPU by Day of Week in a Gaming Club

Segment ARPU by Day of Week in a Gaming Club

Knowing how much each customer brings in on a Tuesday versus a Saturday reveals exactly where your club is leaving money on the table. IZI provides two reports for this: the session heatmap (when customers arrive) and the Revenue & ARPU report (how much each segment spends). ARPU — total revenue divided by unique customers — is split between new and returning visitors, so you see not just “what we earned” but “who brought it in and on which day.” The heatmap aggregates data through the weekday dimension of the session_device_hours analytics cube; the ARPU lines are built from the transactions cube grouped by clientType and hasPhone. Below is a step-by-step path from opening the right screens to actionable management conclusions.

Step 1. Session Heatmap: When Customers Arrive

Path: Analytics → Sessions → Average Occupied Devices per Hour

The heatmap is a 7 × 24 matrix: rows are weekdays (Mon–Sun), columns are hours (00:00–23:00). Each cell shows the average number of occupied devices for that weekday–hour pair across your selected date range. Deeper red means higher occupancy.

How to read the matrix:

• Bright-red clusters — peak hours when the club runs at or near full capacity.
• Pale or empty cells on weekdays — potential dead time with low demand where a targeted promotion could help.
• Unexpectedly red cells in early Sunday morning — a signal of overnight traffic worth supporting with a dedicated tariff.

Zone filter. A dropdown in the top-left corner lets you switch from All Zones to a specific zone. Comparing a VIP room against the standard floor often reveals very different load patterns.

Export. The Export button in the top-right downloads the matrix as a CSV: rows = weekday labels (Mon, Tue, Wed…), columns = hours (0:00, 1:00…), values = average device count rounded to one decimal place.

Step 2. Revenue & ARPU by Customer Type

Path: Analytics → Customers → Revenue & ARPU

This screen shows a composite chart: stacked bars for revenue split across four customer segments, with two ARPU lines overlaid for new and returning customers.

Four Customer Segments

Segment	Meaning
New (reg.)	First visit in the period + has a linked phone number
New (unreg.)	First visit in the period + no linked phone number
Returning (reg.)	Visited before the period + has a linked phone number
Returning (unreg.)	Visited before the period + no linked phone number

The ARPU lines are calculated as:

• New ARPU = (revenue from New reg. + New unreg.) / unique new customers
• Returning ARPU = (revenue from Returning reg. + Returning unreg.) / unique returning customers

Cash Method vs. Accrual Method

A toggle in the top-right of the page switches between Cash method and Accrual method.

• Cash method — top-ups minus withdrawals. Best for tracking money flow into the club.
• Accrual method — charges minus refunds (actual consumed services: sessions, bar, combos). Under this method revenue is further broken down into Sessions, Bar, and Combos.

For weekday ARPU analysis, starting with the cash method is usually cleaner: it is easier to interpret and is not affected by charge-posting delays.

Step 3. Drill Down by Spend Category

Three additional charts appear below the main Revenue & ARPU chart on the same page:

• Revenue by customer type and spend category — a stacked bar showing how much new vs. returning customers contributed, broken down by sessions, bar, and combos.
• Transaction count and share by spend category — how many transactions fall into each category and their relative weight.
• Sales by spend category and customer type — the same split, cross-tabulated by new vs. returning.

These charts help answer the follow-up question: if Saturday ARPU is higher than Wednesday ARPU, is that because of more sessions or because the bar average ticket is higher?

Step 4. Connecting the Heatmap to ARPU

There is no single screen in IZI that overlays both reports simultaneously — they are two separate analytics views. To build the full weekday picture:

1. Open the heatmap → export the CSV for your chosen period (4 weeks works well).
2. Open Revenue & ARPU → set the same date range → switch granularity to By Day using the control in the top-right corner.
3. Cross-reference: on days where the heatmap shows high occupancy (red cells), check what ARPU looks like for new and returning customers.

Common patterns operators discover:

• Friday evening: high occupancy, but new-customer ARPU is lower than Saturday — walk-in visitors tend not to top up their balance.
• Tuesday afternoon: low occupancy, but returning-customer ARPU is high — loyal regulars come during quiet hours and play longer sessions.
• Sunday early morning: unexpected demand — worth checking whether you have a tariff suited to that audience.

Step 5. Granularity and Date Range

Both reports share the same date picker at the top of the page (the useAnalyticsDateRange hook). For weekday analysis:

• Range: 4–8 weeks gives enough data to average out random spikes while staying operationally relevant.
• By-Day granularity in the Revenue & ARPU report lets you see each individual calendar day rather than a weekly aggregate, making it straightforward to spot recurring patterns.

The heatmap always aggregates data across the full selected range and divides by the number of times each weekday appears in the range — so each cell shows a mean value for that (weekday, hour) pair, not a sum. This makes comparisons fair across periods of different lengths or clubs of different sizes.

What to Do with the Results

Once you have identified your weekday ARPU profile, IZI gives you concrete levers:

• Low new-customer ARPU on weekdays → review your tariff configuration: the minimum top-up threshold may be set too low for a first-time visit to generate meaningful revenue.
• High occupancy on Friday evening with average ARPU → check the customer retention report: how many of those Friday newcomers return the following week?
• Returning ARPU more than 2× new ARPU → loyal customers are your core revenue engine; look at how the top-up bonus is configured to encourage first-time visitors to come back.

To compare these weekday ARPU dynamics across multiple clubs in your network, use club and period comparison — the same ARPU metrics are available aggregated at the organization level.

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Both reports load data through a single GraphQL request with dateFrom/dateTo parameters and the club’s local timezone. Make sure the correct timezone is set in the club settings — otherwise overnight hours will be shifted relative to actual clock time.