Retention Cohorts
Cohort analysis answers the question: “Of the clients who came for the first time in January, how many returned within 30 days?” This is more precise than watching a raw “total clients” number, because it tracks the behaviour of specific groups over time rather than giving a snapshot of the whole base.
How cohort analysis works in IZI
Section titled “How cohort analysis works in IZI”Analytics → Cohorts. The date range at the top controls which months the cohorts are built from.
What a cohort is
Section titled “What a cohort is”A cohort is a group of clients who share one defining characteristic. In IZI, cohorts are formed by date of first visit: everyone who visited for the first time in March is one cohort, everyone in April is another.
Retention metrics: D7, D14, D30
Section titled “Retention metrics: D7, D14, D30”- D7 — share of the cohort who returned within 7 days of their first visit
- D14 — share who returned within 14 days
- D30 — share who returned within 30 days
D30 is the primary metric: it is within this window that you learn whether a newcomer has become a regular. D7 is an early warning signal — if it is already low, the problem is at the level of the first impression, not a later drop-off.
How to read the cohort table
Section titled “How to read the cohort table”Each row is a cohort (a month or week of first visits). Each column is a retention window. The values show what percentage of that cohort came back.
| Cohort | New clients | D7 | D14 | D30 |
|---|---|---|---|---|
| January | 120 | 28% | 38% | 43% |
| February | 95 | 31% | 41% | 47% |
| March | 140 | 22% | 30% | 35% |
Reading this table:
- February is better than January — something changed between the two months that improved retention. Was a loyalty program launched? A newcomer promotion? Identifying the cause lets you scale it.
- March is a drop — 35% versus 43–47% in the previous cohorts. Look for what happened during the first-visit period of that cohort: staffing changes, technical issues, altered opening hours, or a timetable change.
Rows read left to right: D7 is always lower than D30, because some clients return later in the window. A typical range for an active club is D7 = 25–35% and D30 = 40–55%.
Step-by-step cohort diagnostic
Section titled “Step-by-step cohort diagnostic”- Open Cohorts for the last 3–4 months.
- Find the cohort with the lowest D30 — this is your highest-priority problem.
- Match it to events — what was happening in the club during the first-visit period of that cohort? Tariff changes, staff turnover, promotions starting or ending?
- Compare D7 and D30 for the problem cohort:
- D7 is normal but D30 is low → clients came back for a second visit but dropped off in the following 1–2 weeks. The issue is mid-window retention.
- D7 is already low → the problem is the first impression: registration flow, wait times, onboarding by staff.
- Apply the right tool for the diagnosis:
- Low D7 → improve the first-visit experience (admin scripts, service speed, first-session bonus).
- Normal D7, low D30 → retention in the 1–2 week window (automated push notification, return bonus).
How to improve D30: methodology
Section titled “How to improve D30: methodology”Step 1. Read your current D30 in Analytics → Cohorts. This is your baseline.
Step 2. Set a target. Adding 8–12 percentage points within 4 weeks is a realistic goal when launching a structured newcomer retention program.
Step 3. Set up an automated trigger via the automations module: event = “5 days since first visit with no return visit”, action = push notification to the IZI mobile app with a bonus on the next top-up.
Step 4. Wait 5 weeks (the time for the first post-launch cohort to complete its D30 window), then compare the new cohort against the previous baseline cohorts.
Connection to other metrics
Section titled “Connection to other metrics”Retention is closely linked to ARPU: the higher the D30, the more clients become regulars who generate consistent spend. It also feeds directly into LTV — a client retained past the 30-day mark has a lifetime value that is typically several times higher than a one-visit client.
When a cohort’s D30 improves, you will generally see ARPU for that month rise in parallel, since the same clients are visiting more often and topping up more.
See also
Section titled “See also”Frequently asked questions
What is cohort analysis in a gaming club?
Cohort analysis groups clients by the date of their first visit and tracks what share of each group returned after 7, 14, and 30 days. It lets you see how retention changes depending on when a client first joined, rather than looking at aggregate active-user numbers.
What is D30 retention?
D30 retention is the share of clients in a cohort who returned to the club within 30 days of their first visit. For a healthy gaming club, a D30 of 40–55% is a strong benchmark.
Where do I find cohort analysis in IZI?
Go to Analytics → Cohorts. At the top you can select the date range for cohort formation and switch between D7, D14, and D30 metrics.
What should I do if D30 retention is below 30%?
Launch a newcomer retention program: set up an automated push notification triggered 5–7 days after a client's first visit, offering a bonus on their next top-up. This is the fastest lever to lift D30 by 8–12 percentage points within a month.