Movie Industry Economics: How Data Analytics Can Help Predict Movies’ Financial Success

Purpose: Data analytics techniques can help to predict movie success, as measured by box office sales or Oscar awards. Revenue prediction of a movie before its theatrical release is also an important indicator for attracting investors. While measures for predicting the success of a movie in box office sales and awards are widely missing, this study uses data analytics techniques to present a new measure for prediction of movies’ financial success.Methodology: Data were collected by web-scraping and text mining. Classification and Regression Tree (CART), Random Forests, Conditional Forests, and Gradient Boosting were used and a model for prediction of movies' financial success proposed. Content strategy and generating high profile reviews with complex themes can add to controversy and increase the chance of nomination for major movie awards, including Oscars.Findings/Contribution: Findings show that data analytics is key to predicting the success of movies. Although predicting sales based on data available before the release remains a difficult endeavor, even with state-of-the-art analytics technologies, it potentially reduces the risk of investors, studios and other stakeholders to select successful film candidates and have them chosen before the production process starts. The contribution of this study is to develop a model for predicting box office sales and the chance of nomination for winning Oscars. Practical Implications: Cinema managers and investors can use the proposed model as a guide for predicting movies’ financial success

Pakistani Youtubers and Social Media Entrepreneurship: Opportunity Identification for Value Creation in Content Market

Purpose: Youtube has become the most visible social platform of video content and thus the most potential venue for entrepreneurial activities of the nascent individual media producers to share their generated video clips to earn income. However, the increasing number of Youtubers who work on converging subjects makes them in tough competition, while there are many niche markets that are left alone. This research explores market opportunities in content market for Youtubers in Pakistan. Methodology: The conceptual framework of this study is taken from Antony Ulwick’s algorithm of opportunity identification. Firstly, a series of interviews conducted with Pakistani Youtubers and the main factors extracted. Then a questionnaire developed and presented to the respondents to rank the extracted factors from 1 to 10 based on two measures of importance and satisfaction from current status. The difference between the two number reflected the market potential demand for any given factor. Finally the factors ranked as market opportunities for Youtubers. Findings/Contribution: While media entrepreneurship has received great research attention during the recent years but still lacks the required tools and methods to measure the various dimension of this construct, in particular, opportunity identification as the cornerstone of entrepreneurship. This article introduced Ulwick’s algorithm of opportunity identification to the media entrepreneurship research