Improved Seating Plans for Movie Theatre to Improve Revenue: An Integrated Best Worst Method with EMSR-B

Movie Industry And Ticket Booking

The Bollywood films are multi-million dollar productions, with the record exclusive productions valuation up to 1 billion rupees. Recently, an increasing trend of making digital movies in India is observed as shown in Figure 1. According to Kimes et al. (1998) movie theaters are positioned as fixed duration and fixed pricing. The movie theaters have grater scope to apply variable pricing but they usually limit themselves to apply routines price discrimination schemes like Silver class, Gold class, Platinum class etc.

Figure 1.

Movies production in India from 2010 to 2014

Kimes and Chase (1998) pointed out the need of adopting variable pricing practices in movie ticket booking based on timing as well as seat allocation (capacity management). Few researcher worked upon forecasting box office revenue of a movie before its theatrical release which is a tough and puzzling problem (Kim et al, 2013; Ghiassi et al., 2015; Zhang et al., 2009). Zhang et al. (2009) employed a multi-layer BP neural network (MLBP) with multi-input and multi-output to build the model for prediction. Kim et al. (2013) applied social network services (SNS) which is influencing the movie industry in South Korea. In their work, the issue of forecasting box-office revenue highlighted and analyzed the data in the Korean movie market. These studies mainly focus on forecasting aspect of market demand rather than dynamic pricing in movie industry. For the success of box office star cast is generally considered an important factor. Joshi (2015) argues that big star cast may not promise profits because their presence results in minimised weekly revenue volatility. There are few studies about movie analytics like El Assady et al. (2013) and Haughton et al. (2015).