Abstract
When it comes to success for entertainment products, William Goldman famously quipped, “Nobody Knows Anything.” You’ve either got the gift—artistic and managerial intuition—or you don’t. This view has permeated entertainment for years and has suggested that scientific insights is less relevant for managing entertainment than for other industries in which it is used to create and market products. Today’s “big data analytics” revolution is seen by many as an alternative to managerial intuition. So which is the best way forward for creating successful entertainment products in the age of big data? We argue that the right path is a careful integration of intuition and analytics, building on the strengths of each and compensating for the weaknesses. We show that analytics also needs powerful theory to be effective, as empirical results and predictions will otherwise be misleading and useless. Entertainment Science fuses managerial intuition with data analytics and theory, suggesting a new path for the thoughtful entertainment manager.
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- 1.
Art De Vany’s reference to the Goldman idea is sometimes used as an academic fig leaf by industry managers who despise the use of analytical approaches. It, however, contrasts strongly with De Vany’s role as one of the “founding fathers” of empirical entertainment research, who studied the creative industry’s patterns and “rules” extensively with ambitious mathematical and statistical tools (De Vany [2004] offers a summary of his work). De Vany also used his insights as a consultant for movie producers via his firm, Extremal Film Partners (see, e.g., Indiewire Team 2011).
- 2.
“Sabermetrics” was brought to prominence in the landmark book “Historical Baseball Abstract” by James (1985), in which the author uses historical data from the Society for American Baseball Research (SABR, hence “saber”) and applied advanced statistical analyses to identify the 100 top players in history at each baseball position.
- 3.
Specifically, the econometric model was fed with information on advertising spending, the artist’s previous performance (a kind of “star power”), and the song itself.
- 4.
Michel Clement’s paper (2004) marks a notable exception—in German though.
- 5.
One of the problems of deviation metrics such as MAPE is that percentage deviations are systematically higher for smaller values of the dependent variable than for greater values. One approach to correct this is to weigh the cases. When we do so in our example, using the ad spending per movie as weight, the MAPE shrinks to 43% (from 58%). See also our discussion of prediction measures in the context of innovation management for entertainment.
- 6.
This remains the case, by the way, when we adjust the R2 for the number of IVs—this adjusted R2 value rises from 0.59 to 0.76.
- 7.
The same would happen if you would try to explain the extraordinary success of James Cameron’s Titanic as part of a sample of less successful films and include “Lead-actor-says-‘I-am-the-king-of-the-world’-while-standing-on-large-ship” as an IV.
- 8.
Vigen (2015, p. 29) offers a colorful example when citing very high correlations between murder rates and ice cream consumption. So which ice cream ingredient turns us into killers? None, at least as far as we (and food scientists) know. Instead, the season is the omitted variable here: murders are more common in the summer, which is when people usually eat ice cream. Thus, any attempt to ban ice cream to reduce murder rates would turn out to be quite ineffective…
- 9.
For readers who want to dive deeper into the method dimension of Entertainment Science, there is an abundance of good books about regression analysis and its extensions. For beginners, we recommend Hair Jr. et al.’s (2014) book, which covers the fundamentals of regression analysis, along with other statistical methods, in a way that is both highly competent and readable. For advanced topics and questions on regression analysis, we suggest the work of Angrist and Pischke (2009), who devote detailed attention to most of the pitfall issues we have listed here—as well as many others.
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Hennig-Thurau, T., Houston, M.B. (2019). Forget the “Nobody-Knows-Anything” Mantra: It’s Time for Entertainment Science!. In: Entertainment Science. Springer, Cham. https://doi.org/10.1007/978-3-319-89292-4_1
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