AK

Data Dictionary

KPI definitions, sources, calculation methodology, attribution logic, and synthetic data notes.

KPI Definitions

TermDefinitionSource
Sell-Through RateTickets sold divided by total capacity, expressed as a percentage.Ticketing system
ROASRevenue attributed to marketing divided by marketing spend.Attribution engine
CPAMarketing spend divided by number of ticket purchases.Attribution engine
CPMCost per 1,000 ad impressions.Ad platforms
CTRClicks divided by impressions.Ad platforms
Conversion RatePurchases divided by sessions.Web analytics
Revenue per Available SeatTotal revenue divided by capacity for a performance.Ticketing system
Assisted ConversionA touchpoint that contributed to a purchase but was not the last click.Attribution engine
Forecasted OccupancyModel-projected final sell-through based on current sales pace.Forecast model
Incremental Ticket SalesModel-estimated tickets attributable to a marketing change.Forecast model
Sales VelocityDaily tickets sold, typically compared against a rolling 7-day average.Ticketing system
Probability of SelloutModel-estimated probability that an event will sell 100% of capacity.Forecast model
Attribution WindowLookback period for crediting a conversion to a marketing touchpoint.Attribution engine

Attribution Logic

  • Default attribution: last non-direct click with a 30-day lookback
  • Comparison models available: first-touch, last-touch, linear, assisted-conversion
  • Assisted conversions are counted when a channel touch precedes purchase within window
  • Cross-device stitching: probabilistic, using logged-in customer identifiers

Forecast Assumptions

  • Baseline: pace-curve regression fit to prior comparable events
  • Weekly seasonality, campaign event flags, and inventory tier are model inputs
  • Confidence intervals: 80% by default, widened when pace data is sparse
  • Refresh frequency: hourly ticketing, daily marketing, weekly attribution

Synthetic Data Methodology

All data on this platform is fictional. Event names, venues, customer counts, transactions, and channel metrics are generated to demonstrate realistic patterns: seasonality, mobile vs desktop conversion gaps, retargeting saturation, budget-constrained search, and weekend production strength. No real customer, company, or transaction information is used.