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Understanding No-Shows Through Predictive Analytics

Updated: Mar 9, 2019

The world has entered into an era where data is a form of currency. Reasons for collecting consumer data range from tracking payment information to security. Methods of repurposing are vast as well. Data has not been fully explored in application to predictive analytics for forecasting event attendance. SeatCycle is doing just that.

Consumer data alone is a powerful tool – but combined with additional sources of rich data it can take on new forms. Other sources of helpful data include:

Analysis of historical event data: understanding specific event details about teams, performers or plays can help venues identify crucial factors such as frequently sparse sections, arrival times, last minute purchases, etc.

Typical effects of event timing: trends like turnout percentage and arrival time appear based on event start time or time of year

Popularity of event: demand is a key factor when calculating predictive analytics

Trends in the industry: other external factors that are prevalent in the ticketing industry will affect attendance patterns

Weather: how a rainy day will affect turnout versus a sunny one, especially crucial at outdoor venues



This data can help venues predict no-shows in advance in order to curate the optimal experience for all of their patrons, while maximizing revenue.

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