Price Simulator for Gaming Club Tariffs

Price Simulator: Model Tariff Changes Before They Go Live

The Price Simulator is a tool inside the Analytics section of IZI CRM that lets a club owner test any change to their tariff grid against real historical data before it touches live traffic. You enter new prices, pick a baseline period (typically 4–8 weeks of history), choose a demand-elasticity assumption, and get a projection: how will revenue, AOV (average order value), and the per-tariff sales mix change? This is not a forecast oracle — it is a numerical argument that replaces gut feel when making pricing decisions. It is especially valuable when moving to differentiated peak / off-peak pricing: before splitting one tariff into three or four time windows, you can see which scenario actually grows revenue rather than just shifting customers between slots.

Why model a price change instead of just making it?

Editing a tariff in the CRM takes two minutes. But any price change acts on real people instantly, and the downstream effects only become measurable after 2–4 weeks of accumulated data. In that window you can:

• Lose part of your regular audience who quietly switch to a competitor without filing a complaint
• Miss the mark on occupancy — raise the evening rate expecting demand to hold, and end up with an empty hall on Fridays
• Leave money on the table if you could have raised the price further than intuition suggested

The Price Simulator addresses exactly this gap: it does not require running an experiment on live revenue. Instead, it takes your session history — how many sessions were sold per tariff, when, and at what occupancy — and recalculates the financial outcome under the new prices.

One important limitation

The tool does not automatically predict how customers will react to a price change. That judgment stays with the owner, expressed through the elasticity scenario choice. The simulator shows you “if demand is unchanged,” “if demand falls moderately,” and “if demand grows.” You decide which scenario fits your market. This is more honest than an algorithm that confidently outputs one number without knowing your local competitive context.

How the simulation works: three layers

Layer 1 — Baseline data (session history)

The simulator loads your session history for the period you select. Each session carries: tariff, start time, duration, zone, and billed total. From this it builds the current-state picture: total revenue for the period, session count per tariff, and occupancy by time window.

Recommended baseline length: 4–8 weeks. Shorter periods are distorted by anomalies like holidays or tournaments. Longer periods may include tariff changes from the past that no longer reflect your current sales structure.

Layer 2 — Pricing scenario

You edit the price on one or more tariffs. You can change:

• Hourly rate (per-hour tariff)
• Package price (multipass — a prepaid block of hours)
• Fixed-window price (overnight unlimited)

You can modify several tariffs in a single scenario. For example: raise the evening rate 15%, cut the morning rate 25%, and leave the multipass unchanged — and see the net effect on club revenue.

Layer 3 — Elasticity assumption

This is the manual judgment you apply based on your knowledge of your customer base:

Scenario	Meaning	When to use
Neutral	Session volume does not change	Dominant local position, no nearby alternatives, price-insensitive audience
Moderate churn	Volume falls 10–20% when price rises	One or two competitors nearby, mixed audience
Aggressive growth	Volume rises 20–35% when price drops	Off-peak windows; testing whether a lower price stimulates daytime traffic

Each scenario produces a separate projection. In practice, look at neutral and moderate churn as your optimistic-to-realistic range rather than committing to a single number.

Step-by-step: running your first simulation

Step 1. Open the simulator

In IZI CRM: Analytics → Price Simulator. Requires Administrator or Owner permissions.

Step 2. Choose the baseline period

Aim for 4–8 weeks with no major tournaments or holiday weeks. If the selected period contains an anomalous spike — an esports tournament week with unusual occupancy — exclude it or shift the date range so it falls outside your baseline.

Step 3. Set new prices

Edit tariff values directly inside the simulator interface. The actual tariffs in your catalog are not touched — this is a scenario draft only.

Step 4. Choose the elasticity scenario

Start with Neutral and Moderate churn to get a best-case / realistic range.

Step 5. Run the calculation

The simulator recalculates instantly — data is already loaded. Output:

• Revenue change (absolute value and percent)
• AOV change
• Sales mix: sessions per tariff before and after
• Missed revenue (gap between actual occupancy and 100% fill at the new price)

Step 6. Save the scenario

Give it a descriptive name (e.g., “Evening +15%, Morning -25%, Aug baseline”) and save. You can then create additional scenarios and compare them side by side.

Step 7. Apply changes to the tariff catalog

Once you have decided on a scenario, apply the price changes in the live Tariffs section. The simulator does not push changes automatically.

Common use cases

Seasonal pricing review

Before the back-to-school period or summer holidays, your occupancy pattern shifts structurally — different audience mix, different peak hours. Run a simulation using the last 6 weeks as a baseline to see how a price adjustment maps to the incoming traffic structure rather than the outgoing season.

Testing differentiated pricing

You want to move from one evening tariff to three: morning (below base), evening (base), and weekend (above base). The simulator shows how revenue redistributes when some customers shift between slots — and whether splitting the single tariff actually increases total revenue or just adds complexity.

Parametric reference ranges from peak / off-peak pricing methodology:

• Peak tariff ≈ base price × 1.2–1.5
• Off-peak ≈ base × 0.6–0.8
• An off-peak discount below 20% of the evening rate typically does not drive customer switching

Plug your actual base price into these multipliers, enter the resulting prices in the simulator, and check the projected revenue.

Evaluating a new multipass before launch

Before launching a multipass — a prepaid block of hours — use the simulator to model what happens when, say, 20% of hourly customers switch to the package. This matters especially for clubs that have never sold a multipass: there is no multipass history, but there is plenty of hourly session history, and the simulator can model the switch.

Responding to a competitor price move

A nearby club drops their overnight rate. Should you match? Run two scenarios — “cut our overnight 15%” and “hold” — and compare the projections. The numbers will not give a definitive answer (competitive dynamics always carry uncertainty), but they quantify the cost of each path.

Reading the results: priority order

After the calculation, read the output in this sequence:

1. Total revenue change — the headline number. If revenue grows under the neutral scenario but drops under moderate churn, you need to assess how realistic that churn level is for your specific market.

2. AOV change — reveals whether the revenue shift comes from higher per-session prices or from a change in session volume. If AOV rises but revenue does not, the projection assumes meaningful customer loss.

3. Per-tariff sales mix (before / after) — critical when simulating multiple tariffs. Look for cannibalization: if a cheaper morning tariff is so attractive that it pulls in evening traffic, that is a risk even if total revenue looks flat.

4. Missed revenue — shows how much revenue is theoretically uncaptured at current (or new) occupancy. High missed revenue during off-peak hours signals that a price cut in those windows might be effective.

Using simulator results alongside other reports

The Price Simulator is one piece of the analytics picture, not a standalone oracle. Pair its output with:

• Hall occupancy heatmap — shows actual peak and low-occupancy patterns that the simulator relies on as input. Before running a simulation, confirm the chosen period is representative, not an anomalous stretch.

• Tariff sales report — shows how customers are currently distributed across tariffs. If 90% of revenue comes from one tariff, that concentrates the simulation risk significantly.

• Session metrics report — average session length affects how hourly tariffs translate into revenue. If the average session is 2.5 hours rather than 1, the AOV from an hourly tariff is calculated differently.

Common mistakes

Mistake 1: Using an anomalous period as the baseline

A tournament week, a grand opening, or a holiday stretch creates atypical occupancy. If you use that as the baseline, the simulation treats the exception as the norm. Always verify that your chosen weeks are typical operating weeks.

Mistake 2: Running only the neutral scenario

Neutral (“volume unchanged”) is the optimistic case. In practice, raising prices almost always causes some reduction in volume, even if small. Treat moderate churn as the more realistic starting point when modeling price increases.

Mistake 3: Not checking back after 4 weeks

Once you have applied a new tariff, return to Analytics after 4 weeks and compare actual results with the simulation projection. This calibrates your sense of elasticity for future simulations: if the real outcome matches neutral, your audience genuinely is price-insensitive at this level. If it matches aggressive churn, account for that in the next round.

Mistake 4: Simulating one tariff in isolation

Tariffs interact. Raise the evening rate without touching the overnight rate, and some customers will simply switch to overnight if it is still cheaper. Simulate the tariff grid as a system, not as isolated price points.

Frequently asked questions

Can the Price Simulator be used to forecast revenue for the coming month? Partially — as a rough estimate assuming traffic structure stays the same. The simulator does not account for seasonality or external events, so for forward revenue planning use a separate financial model. Use the simulator specifically for quantifying the effect of price changes.

Are top-up bonuses factored into the simulation? The simulator calculates cash revenue — actual amounts collected. Top-up bonuses influence customer behavior (visit frequency, AOV) but appear in the simulator baseline only through the actual sessions and their billed amounts.

What if the club history is too short (recently opened)? With less than 4 weeks of data, simulation accuracy is low. In that case, use the parametric reference ranges from the pricing methodology guide and run the simulator once you have accumulated at least 4 full representative weeks.

Related pages

• Peak / off-peak pricing methodology
• Tariff types in IZI: full overview
• Hall occupancy heatmap
• Tariff sales report
• Multipass — prepaid hour packages
• AOV in a gaming club — what it is and how to measure it
• Top-up bonuses: how they work and how to configure them