Only three or four out of every ten movies made in America breaks even or earns a profit. Yet the decision to green-light a project is usually based solely on “expert opinions” — in other words, executives’ intuition supplemented by standard regression analysis. There’s got to be a better way.
We think we’ve found one. In a recent study, two of us (Dan and Carmina) used a technique called “similarity based forecasting” to predict box office revenues for 19 wide-release movies. Here’s how it worked. Non-expert movie-goers were asked via online surveys to judge how similar each movie was to other, previously released movies, on the basis of a brief summary of the plot, stars, and other salient features. We then forecast the revenues for the new movies by taking similarity-based weighted averages of the previously released movies’ revenues. On average, those predictions were twice as accurate as ones driven by expert opinion and standard regression forecasting. They were particularly good at identifying small revenue-earning movies. This type of case-based decision analysis is a great way to tap into crowd wisdom.
It’s impossible to eliminate risk from strategic decision making, of course. But it is possible to significantly improve your odds by understanding which decision-support tools work best for which decisions. Most companies – including the movie studios in Hollywood – over-rely on basic tools like discounted cash flow and net present value. These tools are great if you’re working in a stable environment, with a business model you understand. But if you’re on unfamiliar ground – if you’re in a fast-changing industry, launching a new product, or shifting to a new business model – they can be downright dangerous.