Building a ticketing revenue model: price, product mix, and bundled products

Ticket revenue is not one price times one attendance number. It is a mix of admission types, time-based pricing, add-ons, discounts, partner channels, and bundled products shared between venues. Leadership needed to evaluate price and product decisions, but the existing view could not show where a change would actually land.

In a ticketed business, attendance and price are the revenue budget. A pricing decision modeled at list price overstates its effect, and a bundled product shared between venues can move revenue from one entity to another without changing the total.

• Broke ticket revenue into its legs — admission type, channel, add-ons, and bundles — and modeled each on its own drivers.
• Derived revenue per guest from revenue and attendance rather than treating it as an editable assumption, so it can never drift out of step with the two figures it comes from.
• Worked through the accounting allocation rules for bundled products, so the model shows which entity and department absorbs a discount — not just the combined effect.
• Built a "no price change" baseline anchored to prior-year actuals, then measured every scenario against it.
• Validated the model against periods where actual results were already known, and flagged any segment where assumptions no longer matched actual behavior.

• A driver-based ticketing revenue model feeding the budget and in-year forecasts.
• Scenario analysis for price and product changes, showing the effect on attendance, realized price, revenue per guest, and each entity's revenue.
• Price-realization analysis separating list price from what is actually collected after discounts, channels, and mix.

• Pricing and product decisions evaluated on realized revenue, by entity, before they are made.
• Revenue assumptions in the budget derived from one consistent framework instead of separate spreadsheets.
• Allocation effects of bundled products made visible to leadership, rather than discovered after the close.

• Driver-based revenue modeling
• Price realization analysis
• Bundled-product revenue allocation
• Scenario modeling
• Model validation against actuals
• SQL
• Python