Why You Should Crowd-Source Your Toughest Investment Decisions

October 16, 2013 Editor 0

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.

In highly uncertain contexts, the best tool is often “case based decision making” — developing a set of analogous situations (like the previously released movies), determining the results achieved in those cases, and then assessing how similar each case is to the decision at hand. Sometimes it’s useful to ask non-experts what they think, as we did with the movie comparisons, and sometimes it’s best to aggregate experts’ opinions. What’s most important, though, is to use a structured, rigorous approach to choosing the comparison cases, since it’s natural to fixate on the analogies that best support the action you want to take, or suspect you should take.

Our November 2013 HBR article, “Deciding How to Decide,”  describes a model for matching decision-support tools to the decision being made, and describes some rich alternatives to the analytic tools we typically teach in business school.

Go to Source

Categories: HBR, Insights

Tags: Decision engineering, Decision theory, decision-making, regression analysis