Bias busters: Taking the ‘outside view’
Taking the “outside view”
The problem
You are the head of a
major motion-picture studio, and you must decide whether to greenlight a movie
project. You need to predict whether it will be boffo (a box-office hit) or a
bust. To make this decision, you must make two interrelated forecasts: the
costs of production and potential box-office revenue.
Production costs are
easy, you think: you know the shooting days, specific location costs, and
computer-generated imagery costs. You can enter these into a spreadsheet that
reflects the film’s production plan. Potential box-office revenue is harder to
predict, but you know roughly how many screens the film will be on during
opening weekend, how “hot” your stars are right now, and how much you are going
to spend on advertising.
Do you have enough data
to make a decision? Maybe. Are the data enough to make the right decision?
Probably not. Research shows that film executives overestimate potential
box-office revenue most of the time.
The research
That’s because film
executives often take what Nobel laureate Daniel Kahneman and colleagues refer
to as the “inside view.”1 They build a detailed case
for what is going to happen based on the specifics of the case at hand rather
than looking at analogous cases and other external sources of information. (If
they do look at other data, it’s often only after they’ve already formed
impressions.) Without those checks and balances, forecasts can be overly
optimistic. Movie projects, large capital-investment projects, and other
initiatives in which feedback comes months or years after the initial decision
to invest is made often end up running late and over budget. They often fail to
meet performance targets.
The remedies
One way to make better
forecasts, in Hollywood and beyond, is to take the “outside view,” which means
building a statistical view of your project based on a reference class of
similar projects. Indeed, taking the outside view is essential for companies
seeking to understand their positions on their industries’ power curves of
economic profit. To understand how the outside view works, consider an
experiment performed with a group at a private-equity company. The group was
asked to build a forecast for an ongoing investment from the bottom up—tracing
its path from beginning to end and noting the key steps, actions, and
milestones required to meet proposed targets. The group’s median expected rate
of return on this investment was about 50 percent. The group was then asked to
fill out a table comparing that ongoing investment with categories of similar
investments, looking at factors such as relative quality of the investment and
average return for an investment category. Using this outside view, the group
saw that its median expected rate of return was more than double that of the
most similar investments.
The critical step here,
of course, is to identify the reference class of projects, which might be five
cases or 500. This process is part art and part science—but the overriding
philosophy must be that there is “nothing new under the sun.” That is, you can
find a reference class even for ground-breaking innovations—something music
company EMI (of The Beatles fame) learned the hard way.
In the 1970s, EMI
entered the medical-diagnostics market with a computed tomography (CT) scanner
developed by researcher and eventual Nobel Prize winner Godfrey Hounsfield. The
company had limited experience in the diagnostics field and in medical sales
and distribution. But based on an inside view, senior management placed a big
bet on Hounsfield’s proprietary technology and sought to build the required
capabilities in house.
It took about five
years for EMI to release its first scanner; in that time, competitors with
similar X-ray technologies as well as broader, more established sales and
distribution infrastructures overtook EMI. In seeking to do everything alone,
EMI suffered losses and eventually left the market. Building a reference class
would have allowed the company to not only predict success in the market for CT
scanners but also develop a more effective go-to-market strategy.3
Compared with EMI’s
situation, finding a reference class for a film project might seem like a
no-brainer: you figure there will be lots of movies in the same genre, with
similar story lines and stars, to compare with the focal project. And yet, when
we asked the head of a major motion-picture studio how many analogues he
typically used to forecast movie revenue, he answered, “One.” And when we
inquired about the most he had ever used, he said, “Two.” Research shows that
using the correct reference class can reduce estimation errors by 70 percent.
Companies often think
it is too hard and too time-consuming to build a reference class. It is not. In
an effort to improve the US military’s effectiveness in Iraq in 2004, Kalev
Sepp, a former special-forces officer in the US Army, built a reference class
of 53 counterinsurgency conflicts with characteristics of the Iraq war,
complete with strategies and outcomes. He did this on his own in little more
than 36 hours. He and his colleagues subsequently used the reference class to
inform their decisions about critical strategy and policy changes. Other
organizations can do the same—learning as much from others’ experiences as they
do from their own.
By Tim Koller and Dan Lovallo
https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/bias-busters-taking-the-outside-view?cid=other-eml-alt-mkq-mck-oth-1810&hlkid=bb6631dcc64549e8a35e1467abc2559a&hctky=1627601&hdpid=c2dd8a15-66ff-4055-b167-c2099e36ef82
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