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Case Studies

Event Slot Forecasting

Mission:

Predict how fast climb slots will sell out — before anyone straps on a harness.

The Challenge:

The company runs hourly rooftop climbs, and demand swings wildly based on weather, events, holidays, and time of day. The goal was to forecast ticket sales for each time slot up to 90 days in advance and help the business maximize pricing + capacity decisions.

How the Khares Analytics team helped:

  • Ran time-series demand analysis across thousands of climb windows
  • Identified key demand drivers
  • Engineered features like date hierarchy (day/week/month/quarter), weekday/weekend split, and time-of-day buckets
  • Built predictive models to forecast sell-through and slot trajectory (hold steady vs sell out)

Key Insights Uncovered:

  • Event days dramatically boost climb demand
  • Afternoon & sunset climbs consistently outperform morning slots
  • Price elasticity varies seasonally — optimal pricing isn’t static
  • Slot momentum matters: early booking pace predicts final fill level

The Outcome:

A production-ready model that helps operations + revenue teams:
  • Forecast slot sell-out likelihood
  • Adjust pricing and capacity in advance
  • Optimize marketing timing and spend
  • Plan peak staffing weeks ahead
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