Comparing Network Clubs by Metrics: Methodology
Comparing Network Clubs by Metrics: Methodology
Section titled “Comparing Network Clubs by Metrics: Methodology”Comparing clubs is the key management tool for a network. It serves two purposes: it identifies problem clubs before they become crises, and it surfaces best practices that can be replicated. The core rule: compare normalised metrics, not absolute numbers.
Metrics for Comparison
Section titled “Metrics for Comparison”1. Revenue per PC per Day
Section titled “1. Revenue per PC per Day”Formula:
Revenue_per_PC_per_day = Club_revenue ÷ Number_of_PCs ÷ Days_in_periodWhy it matters: normalises different club sizes. A 10-PC club with 50,000 revenue/month and a 30-PC club with 100,000 revenue/month — the first is more efficient: 167/PC/day vs 111/PC/day.
Signal thresholds:
- Deviation > 30% from network average → needs explanation
- Stable downward trend 3+ weeks → a problem, not dispersion
2. Average Order Value (AOV)
Section titled “2. Average Order Value (AOV)”What it shows: how willing clients at each club are to top up large amounts. Depends on loyalty programme quality, admin sales skills, audience spending power.
How to compare: absolute AOV values may differ between clubs (different price segments). Look at trend — is AOV growing or falling at each club, and how does AOV movement correlate with loyalty programme launches/changes.
Illustration: if after launching the same bonus tier at club A AOV grew 20% but at club B didn’t change — the difference is likely admin script quality.
3. Hourly Utilisation
Section titled “3. Hourly Utilisation”What it shows: usage patterns. Comparing patterns between clubs shows where unrealised potential exists.
How to read it: if club A has 40% daytime utilisation but club B has 15% in a similar-area location — this signals either club A does something better (superior off-peak marketing?) or club B needs a daytime bonus. Methodology → How to Fill Off-Peak Hours.
4. D30 Newcomer Retention
Section titled “4. D30 Newcomer Retention”What it shows: how well each club retains new clients.
Why it matters for comparison: retention strongly depends on admin onboarding (whether they deliver the script on first visit) and Automations configuration. If one club has 35% retention and another 18% with a similar audience — the problem is processes, not location.
5. ARPU per Active Client
Section titled “5. ARPU per Active Client”Formula:
ARPU = Club_revenue_for_period ÷ Active_clients_for_periodWhat it shows: average revenue per client. Grows with increased visit frequency or AOV. Normalises different client base sizes across clubs.
Comparison Matrix: Weekly Review Template
Section titled “Comparison Matrix: Weekly Review Template”| Metric | Club A | Club B | Club C | Δ best/worst |
|---|---|---|---|---|
| Revenue/PC/day | — | — | — | — |
| AOV | — | — | — | — |
| Peak utilisation | — | — | — | — |
| Off-peak utilisation | — | — | — | — |
| D30 retention | — | — | — | — |
| ARPU active client | — | — | — | — |
| New clients | — | — | — | — |
Fill in weekly. Watch the “Δ best/worst” column — if the gap exceeds 30% on any metric, that’s a point for deep diagnosis.
Underperforming Club Diagnosis Algorithm
Section titled “Underperforming Club Diagnosis Algorithm”Step 1. Rule Out External Causes
Section titled “Step 1. Rule Out External Causes”Before drawing operational conclusions:
- Road construction or building work nearby? → temporary factor
- A competitor opened within 500 m? → competitive factor
- Location-specific seasonal pattern? → normal
Step 2. Determine the “Type” of Problem
Section titled “Step 2. Determine the “Type” of Problem”By deviation pattern:
| Symptom | Likely cause |
|---|---|
| Low revenue + normal utilisation | Low AOV — issue with loyalty programme or rates |
| Normal revenue + low utilisation | Small client base or no off-peak tools |
| Normal intake + low retention | Onboarding process issue or service quality |
| Everything low | Bad location or systemic management problem |
Step 3. Apply a Targeted Tool
Section titled “Step 3. Apply a Targeted Tool”| Problem | Tool |
|---|---|
| Low AOV | Bonus tier + admin scripts |
| Low daytime utilisation | Off-peak bonus |
| Low newcomer retention | Newcomer programme |
| No new clients | Marketing, referral programme |
Step 4. Replicate Successful Practices
Section titled “Step 4. Replicate Successful Practices”The best club in the network is your internal benchmark. Regularly ask: “What does club A do differently that gives it +25% AOV?”
Practice: a quarterly meeting of all club managers. The best club presents 2–3 practices that produced results. Others implement them.
When Differences Between Clubs Are Normal, Not a Problem
Section titled “When Differences Between Clubs Are Normal, Not a Problem”Not every deviation is a warning sign. Normal reasons for differences:
- Different price segments: a city-centre club vs a residential-area club — different AOV is normal
- Different base maturity: a new club in the first 3–6 months always lags a mature club in retention
- Different size: a small club in a good location can show better revenue/PC than a larger club
Look for anomalies within comparable pairs: two clubs of similar age, similar audience, similar location — that’s where comparison is most informative.
All formulas are parametric. Threshold values (30% deviation, 4 weeks) are benchmarks — calibrate to your network’s specifics.
Related: Network Dashboard · How to Fill Off-Peak Hours · How to Reduce Churn · How to Increase Club Revenue · How to Scale to a Second Location
Frequently asked questions
Which metrics should you use to compare network clubs?
Five key comparison metrics: revenue per PC per day (normalises different club sizes), average order value (AOV), peak-hour utilisation, D30 newcomer retention, ARPU per active client. Comparing absolute amounts is incorrect if clubs are different sizes.
What to do if one club is significantly underperforming the rest?
First rule out external causes (a competitor, road construction nearby, location-specific seasonality). Then drill into detailed club analytics: hourly utilisation, new client dynamics, AOV trend. The problem is usually specific and solvable with one or two tools.
How do you know if the gap between clubs is statistically significant or just noise?
Rule: deviation > 20% over a comparison period of ≥ 4 weeks is already a signal. Deviation < 10% over any period — within normal dispersion. One-off weeks (holidays, events) — exclude from comparison.
How do you use the best club to improve the others?
Study what the best club does differently: which Automations are active, how the rate structure is built, what role the admin plays in sales. Replicate successful practices to other clubs — this is the fastest way to improve weak clubs.
Should you close a chronically underperforming club?
If a club has been operationally unprofitable for 6+ months with no clear cause and all corrective measures have been tried — yes, closing is more rational than continuing to subsidise. But first check: the problem may be a specific manager, not the location.