Every hour has value.
Price it with a plan.

Every resource. Every time window. An intentional price.

Build the rate structure around how your venue actually trades. A weekday afternoon, a prime weekend slot and a specific activity space do not have to share the same rate.

• Venue-specific rate rules with weekday, weekend or all-day-type scope.
• Start and end times, effective dates, and peak/off-peak designation.
• Separate public and member hourly rates.
• Scope by activity type and selected bays or lanes.
• Rule priorities to resolve overlapping offers; visibility into rules hidden by higher-priority coverage.
• Pricing coverage calendar and a preview tool for date, time, duration, membership and resource selection.

Make benefits part of the price—not a correction at the desk.

Connect membership pricing and credit rules to booking, while managing the commercial offer behind the membership itself.

• Member-rate display alongside the booking availability experience.
• Tier-based benefits, booking windows and applicable discounts.
• Credit coverage that distinguishes fully covered, partly covered and cash-priced sessions.
• Peak eligibility and per-booking credit limits where configured.
• Membership price administration with billing intervals, promotional offers and legacy/founder price options.
• Visibility into member protections alongside dynamic-pricing governance.

Respond to demand within rules you set.

COPA’s dynamic-pricing tools use utilization and booking pace to model price changes. Operators define the targets, weights and limits instead of handing over an unlimited pricing mandate.

• Named, versioned policies with draft and active states.
• Separate utilization targets for peak and off-peak periods.
• Configurable weights for utilization and booking pace.
• Minimum and maximum price multipliers, plus caps on the pace signal.
• Separate peak/off-peak price floors and ceilings.
• Smoothing controls and a configurable near-term freeze window.

See the proposed change before making it live.

A pricing policy can be run as a simulation, inspected and deliberately promoted. The modeling and the decision to apply prices are separate steps.

• Select a policy version and run a venue-specific simulation.
• Inspect individual runs and their generated outputs.
• Compare modeled base revenue, modeled dynamic-pricing revenue and projected lift.
• Review the inventory buckets included in the projection.
• Preview live base prices, final prices and their differences.
• Use integrity and run-performance views to investigate the pricing picture.

Treat unsold time as a specific opportunity.

An empty slot between bookings is different from an entire quiet afternoon. The Gap Engine surfaces underused inventory and tracks what happens as an opportunity ages.

• Venue-scoped scans for underutilized slots.
• Opportunity-level current price, suggested price and discount visibility.
• Detection counts, survival time and escalation level.
• Structural-weakness signals to distinguish recurring soft inventory.
• Review, activate or deactivate individual promotions.
• Analytics for fill rate, discount depth and time-to-fill across opportunities.

Find out where a discount makes a difference.

Compare observed fill behavior across days, hours and discount tiers. The map helps distinguish price-sensitive inventory from slots where giving up more revenue may not help.

This is diagnostic analysis of historical observations. It does not establish that a discount caused a booking.

• Day-of-week and hour-of-day heatmap.
• Discount-tier filtering and selected-slot response curves.
• High-elasticity, moderate-elasticity, inelastic and insufficient-data classifications.
• Sample counts and average time-to-fill alongside observed fill rates.
• Comparison with baseline fill behavior.
• Structural versus non-structural inventory analysis and estimated revenue-efficiency comparisons.

Sometimes the smartest discount is a smaller one.

The recommendation engine compares observed discount tiers using fill rate and price after discount. It can suggest going deeper, saving discount, keeping the current approach—or waiting for more data.

Confidence describes the amount of supporting history; it is not a statistical guarantee or a promised revenue result.

• Suggested discount percentage and price for an opportunity.
• Expected fill rate and time-to-fill context from similar historical slots.
• Current versus recommended expected revenue and the modeled difference.
• Sample-count-based confidence labels.
• An explicit insufficient-data response when usable history is missing.
• A review-and-apply action for actionable recommendations.

Carry your commercial offer through to the tab.

Pricing extends beyond activity time. COPA’s commerce rules support targeted offers and controlled discount application at checkout.

The minimum-price setting is a percentage of selling price, not a cost-based profit guarantee. Bundles, event offers and post-tax rules have distinct treatment.

• Day/time windows and effective dates for scheduled offers such as happy hour.
• Membership-tier eligibility and booking-channel conditions.
• Menu-item, category and tag-based targeting where configured.
• Exclusive and stackable rule handling with defined precedence.
• A configurable minimum-price percentage for eligible automatic pricing-rule discounts.
• Rule application records and permission-gated administration.