How to Calculate Sessions per Active Player

How to Calculate Visit Frequency (Sessions per Active Player) in a Gaming Club

Visit frequency is one of the clearest indicators of player loyalty: it tells you how often the average active client comes to your club within a month. In IZI CRM the metric is calculated automatically inside Analytics → Clients — no manual exports needed. An active player is any client who completed or started at least one session during the selected period; the system already filters them for you. The data is broken into a frequency distribution across five visit-day buckets: 1, 2–3, 4–6, 7–10, and 10+. To get the final sessions per active player number, divide Sessions count (from the Sessions Data block) by Total clients (unique active players). Both figures live on the same report page, one click away.

Where to Find the Data in IZI CRM

Clients section — retention tab

Navigate to Analytics → Clients. The page has several blocks; the two you need are in the lower half:

Visit Funnel — a step-down chart showing how many clients reached the 1st visit, 2nd, 3rd, 4th, 5th and beyond. Each step reveals retention toward the next threshold. If 200 out of 500 monthly clients came at least twice, that is a 40% retention rate to the second visit.

Visit Frequency Distribution — a table with five buckets: 1, 2–3, 4–6, 7–10, and 10+ visit days. For each bucket you see the absolute number of clients and their share as a percentage of all active clients in the period.

Sessions Data block — absolute numbers

Still inside Analytics, switch to the Key Metrics (KPI) tab. In the Sessions Data block find:

CRM field	What it contains
Sessions count	Total gaming sessions in the period
Total clients	Unique clients (active players) in the period
Returning clients	Clients whose first visit was before the period start
New clients	Clients visiting for the first time within the period

Divide Sessions count by Total clients to get sessions per active player for the selected period.

Step-by-Step Calculation

Step 1. Select the period

Open Analytics → Clients and set the date filter to a full calendar month. Comparisons are only valid across equal-length periods — month over month, quarter over quarter.

Step 2. Pull the active clients and sessions numbers

From the Sessions Data block (or the KPI cards at the top of the page) note:

• S — Sessions count
• U — Total clients (unique active players)

Step 3. Compute the metric

sessions_per_active_player = S ÷ U

Parametric example: if a club recorded 3,000 sessions and 1,000 unique active clients in a month, the result is 3.0 visits per active player.

Step 4. Read the frequency distribution

Switch to Clients → Visit Frequency Distribution. A typical healthy club pattern:

Bucket (visit days)	What it means
1	One-timers — came once and did not return within the period
2–3	Occasional visitors
4–6	Regular players
7–10	Active core
10+	Superusers — highest-revenue cohort

The larger the share of clients in the 4+ buckets, the higher your sessions per active player and the more stable the club’s unit economics.

Step 5. Read the visit funnel

The Visit Funnel shows five steps: 1+, 2+, 3+, 4+, 5+ visits. The ratio of step 2 to step 1 is your repeat-visit retention rate. If it falls below 35–40%, it is worth reviewing your loyalty program and top-up bonus settings.

How to Interpret the Results

Benchmark ranges

This metric has no universal standard — it depends on your audience, location, and club format. Use these parametric benchmarks:

• Below 2.0 — low retention. Most clients visit once and do not return within the month. Priority action: engage new clients within 48 hours of their first visit.
• 2.0–3.5 — typical range for most clubs. There is a loyal core, but meaningful churn after the first visit.
• 3.5–5.0 — strong loyalty. Common in clubs with active bonus programs, regular tournaments, and a stable local audience.
• Above 5.0 — either a highly dedicated niche audience (such as an esports training facility) or a signal to check whether technical or test sessions are included in the filter.

Breakdown by client type

IZI splits clients into four subtypes:

• New (registered) — first visit in the period, phone number on file
• New (unregistered) — first visit in the period, no phone number
• Returning (registered) — visited before the period, phone number on file
• Returning (unregistered) — visited before the period, no phone number

For accurate retention analysis, registered clients are the most informative: they are identifiable and support cohort tracking. For more on segmentation, see How to Create a Client Segment.

Connection to Other Metrics

Sessions per active player does not stand alone. Use it alongside:

• ARPU — average revenue per client. The product of ARPU × visit frequency × average session value gives you your baseline unit economics.
• Average session length — if frequency is high but sessions are short, clients are dropping in briefly; check session extension scenarios.
• Club utilization — high frequency combined with low overall utilization means the active core is concentrated in narrow time slots.
• Retention — cohort-level retention by first-visit month; extends the view beyond a single reporting period.

In IZI these metrics are available on adjacent tabs within the same Analytics section. You can switch between them without resetting the date filter.

Common Interpretation Mistakes

Comparing periods of different lengths. A 31-day month versus a 28-day month will produce different values even if player loyalty is identical. Always compare equal-length periods.

Including technical accounts. If your club has test devices or staff accounts without phone numbers, they can inflate the session count without reflecting real audience growth. Use the “Registered” filter for a clean slice.

Mixing multiple club locations. If you manage a network, calculate the metric separately per location — audience profiles and formats can differ significantly. Cross-location comparisons are available in Organization Analytics.

Ignoring the distribution. An aggregate score of 3.2 visits can hide very different realities: 80% of clients visiting 3–4 times, or 60% visiting once while 10% visit 15+ times. The funnel and frequency distribution reveal the actual structure behind the average.