The Orlando Magic is a professional basketball team that sells tickets to their games. As with any business, the Orlando magic forecast their ticket sales in order see the demand for their games. The team also “translates” that forecasted demand into ticket prices in order to maximize their revenue for each game. Before, the magic used to set the ticket prices the same for every game no matter what the day of the week, or the season, or the team they were playing. The magic decided that they were not reaching their revenue potential, so the team took their strategies up a notch in order to better manage their revenue. Their director of business strategy, Anthony Perez, helped implement a better yield/revenue management system, so the team
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As an entity that sells tickets to games, they need to forecast the demand for those tickets in order to help maximize their revenue potential. The chapter talks about regression analysis which the Orlando Magic uses, more specifically multiple regression analysis. The magic applied the multiple-regression model using several key variables that can affect ticket sales to forecast demand more accurately for games. Some of the independent variables the magic used in their forecasting model included the following: day of the week, opponent popularity rating, and time of the year. Different days of the week have different ticket sales demand; to help forecast, the magic assigned days of the weeks’ different ratings—for example, Saturdays are a 6 rating for being the most popular, and Mondays are the least popular with a 1. Different opponents create different ticket demand for the magic because the Lakers with a superstar would be a more popular team than the suns with no superstars. Popular teams can be rated as high as an 8 and less popular teams as low as 0. The time of the year has an affect on demand as well, spring games rated with a 3 are more popular than early fall games rated with a 0. All these independent variables are used in the multiple regression analysis model concept mentioned in the chapter. The magic has to apply the forecasting concept, more specifically multiple regression, well to maximize revenue potential as it’s integral to their
As an entity that sells tickets to games, they need to forecast the demand for those tickets in order to help maximize their revenue potential. The chapter talks about regression analysis which the Orlando Magic uses, more specifically multiple regression analysis. The magic applied the multiple-regression model using several key variables that can affect ticket sales to forecast demand more accurately for games. Some of the independent variables the magic used in their forecasting model included the following: day of the week, opponent popularity rating, and time of the year. Different days of the week have different ticket sales demand; to help forecast, the magic assigned days of the weeks’ different ratings—for example, Saturdays are a 6 rating for being the most popular, and Mondays are the least popular with a 1. Different opponents create different ticket demand for the magic because the Lakers with a superstar would be a more popular team than the suns with no superstars. Popular teams can be rated as high as an 8 and less popular teams as low as 0. The time of the year has an affect on demand as well, spring games rated with a 3 are more popular than early fall games rated with a 0. All these independent variables are used in the multiple regression analysis model concept mentioned in the chapter. The magic has to apply the forecasting concept, more specifically multiple regression, well to maximize revenue potential as it’s integral to their