Analytics Overview

The Analytics module in IZI gives club owners and staff a complete, real-time picture of business performance — more than 30 reports covering revenue, client behaviour, hall utilisation, staff oversight, and tariff efficiency, all without manual spreadsheet exports. Key metrics like ARPU, hall load, and client retention update live and are available from the moment the club opens. Most tables support CSV export for external analysis or accounting integrations.

For owners, the practical starting point is the Key Metrics (KPI) block: one screen that surfaces the five numbers that matter most. For the Administrator and any staff with analytics permissions in their custom role, the Daily Analytics and Session Report tabs provide the operational granularity needed to run a shift well.

Report groups at a glance

The Analytics section in the CRM sidebar is structured into eleven blocks, each covering a distinct management area.

Report group	What it contains	Primary audience
Key Metrics (KPI)	Revenue, ARPU, hall load, average spend, new vs returning clients — all on one screen	Owner, Administrator, custom roles with KPI access
Daily Analytics	Hourly breakdown for any day: sessions, revenue, load by time of day	Administrator, custom roles with daily analytics access
Product Report	F&B sales, stock movement, inventory, receiving and write-offs	Custom roles covering bar or stock management
Tariff Sales	Unit sales and revenue by tariff for the selected period	Owner, custom roles with sales reporting access
Bonus Report	Bonus accruals and redemptions, per-client operations, tag breakdown	Custom roles covering marketing or loyalty
Client Report	Client list, revenue, cohort retention, tariff and zone breakdown	Owner, custom roles with client data access
Shift Report	Revenue and operations per shift, cash reconciliation	Custom roles covering shift or finance responsibilities
Session Report	Session metrics, hall load heatmap, per-device load	Administrator, custom roles with session access
Suspicious Actions	Anomalous operations, breakdown by employee, tech-mode log	Owner, Administrator
Promo Code Report	Promo code usage, summary and per-code detail	Custom roles covering marketing
Price Simulator	Tariff change modelling on historical data	Owner

KPI: what to track and why

The Key Metrics block is the owner’s primary entry point. Each metric answers a specific question about business health.

Revenue for the period is the baseline trend indicator. Comparing the current period to the previous identical one — week over week, month over month — removes seasonal noise and shows direction. A sharp drop without an obvious external cause is the signal to dig into operational data.

ARPU (Average Revenue Per User) is the average revenue generated by one paying client during the period. Formula: total revenue ÷ number of unique paying clients. When revenue grows but ARPU falls, the club is attracting more clients who spend less — a pricing or upsell problem, not a traffic problem. These require different responses.

Hall load is the share of operating time during which seats are occupied. It is visible hour by hour in Daily Analytics and on the heatmap in Session Report. Off-peak gaps are the most direct signal of where a discount mechanic or event could generate incremental revenue from otherwise idle capacity.

Retention measures what percentage of clients returned in the month following their first visit. This is the product-quality metric: if retention is below the typical benchmark for the club’s region and format, the priority is improving the client experience — not increasing marketing spend. More acquisition on a leaky retention base rarely improves unit economics.

Average order value (AOV) per session is the average amount a client spends per visit, including bar purchases and other products. It grows through tariff upsell, F&B cross-sell, and loyalty programme mechanics. Detailed breakdown is in Client Report → Revenue.

LTV (Lifetime Value) estimates the total value of a client across their relationship with the club. In IZI, LTV is evaluated through cohort analysis: the cumulative revenue of each monthly cohort is tracked across subsequent months, producing a curve that shows how quickly client acquisition costs are recovered.

Cohort retention reports

Cohort analysis shows how clients behave over time — not just in the moment. It is the standard tool for distinguishing a product quality problem from a marketing problem.

The mechanism: clients are grouped by the month of their first visit (the cohort). For each cohort, IZI calculates the percentage that returned one month later, two months later, three months later, and so on. The result is a matrix where each row is a cohort and each column is an ordinal month in the client’s lifecycle.

The Client Report → Retention section in IZI builds this matrix automatically.

How to interpret the cohort matrix:

• If the most recent cohorts show consistently higher first-column retention than older ones, a recent product change or experience improvement is working.
• If one month’s cohort is anomalously low, look at operational events from that period: a location change, staff turnover, or a competitor opening nearby.
• If retention falls steadily from the third month onwards across all cohorts, the loyalty programme or absence of recurring activities is the likely cause.
• If older cohorts (6–12 months ago) retain better than newer ones, the quality or source of new client acquisition has changed — worth reviewing traffic channels.

Cohort analysis identifies where to look, not why. After spotting an anomaly in a cohort row, move to the detailed Session and Client reports for the same period to understand the operational cause.

Suspicious actions and staff oversight

The Suspicious Actions section is an oversight tool for the owner and senior staff who need to monitor operations without being physically present in the club. IZI automatically flags operations that deviate from normal patterns or carry signs of misuse.

The section has four tabs:

Employees — a breakdown of anomalous operations by staff member. Visible at a glance: who has an atypical number of balance operations, refunds, or tech-mode entries. This is a conversation-starting tool, not a verdict — the data shows where to ask questions.

Balance Operations — a detailed log of deposits and deductions matching suspicious patterns: deposits with no subsequent sessions, unusually large single transactions, repeated identical amounts.

Device Analysis — per-device statistics: atypical operating hours, operations on devices with no active sessions.

Tech-Mode Log — every entry into technical mode, with the staff member, time, and duration recorded. Technical mode is a legitimate service tool, but habitual use on an occupied device produces zero revenue from that seat during the interval.

The tab structure lets an owner review the flags in two to three minutes per day without going through each transaction individually.

Price Simulator

The Price Simulator is the tariff testing tool — a way to evaluate a pricing change before committing to it on the live price list.

How it works: you enter a hypothetical price for one or more tariffs. The simulator takes historical sessions from your club — your actual sales for the selected period — and recalculates revenue at the new rate. The output is a revenue delta: how much more or less you would have earned had that price been in effect during the reference period.

Typical use cases:

• Evaluate whether raising the late-night tariff is worth the risk.
• Compare two pricing scenarios for the same zone side by side.
• Understand how price-sensitive the existing audience is: if the tariff were X% higher, does revenue increase, or does session volume fall enough to cancel the gain?
• Prepare a data-backed recommendation for an investor or co-owner — not “I think we should raise prices” but a calculation on the club’s own historical data.

The simulator works on historical data only. It shows a mechanical revenue delta assuming session volume stays constant — it does not forecast how client behaviour would change at a new price point. Use it as an orientation, not a precise projection.

Multi-club and network analytics

For owners operating multiple clubs — franchise networks or independent chains — IZI provides analytics at both the individual club and organisation levels.

Switching between clubs is done via the club switcher in the CRM header. Every analytics section responds to the switch: the same KPI block, heatmap, or cohort report opens for any club in the network without a separate login.

Client Report at organisation level shows the client base that visits multiple clubs without double-counting. A client who visits two clubs in the network is counted as one unique client at the organisation level.

Tariff Sales at organisation level gives a network-wide product portfolio view: which tariffs sell best across all clubs, not just within a single location.

Practical workflow for a network owner: open the KPI block for each club at the start of the week and compare load and ARPU. A club where both metrics are below the network average is the priority for review. A club with high load but low ARPU points to a pricing optimisation or upsell opportunity.

For deeper side-by-side comparisons, use export: pull the same report for each club, combine into a summary spreadsheet. This is a manual step — consolidated cross-club dashboards in a single screen are on the product roadmap.

Navigating the analytics section

In the CRM sidebar, click Analytics. Sub-sections appear: Revenue, Sessions, Clients, Hall, Bonuses, Bar, Shifts.

Use the date range picker at the top of each report to set the analysis period. Most reports update instantly when the range changes. The built-in compare mode (available on most reports) automatically selects the previous identical period for delta calculations.

To export any filtered view, click the Export CSV button in the table header. The export respects all active filters — period, club, tariff, zone.

See also

• Revenue report
• Key metrics dashboard
• Clients report
• Hall utilisation report
• Retention cohorts
• Tariff basics and pricing
• Bonus engine overview