ANALYTICS

When to Forget the RearviewMirrorFROM THE JUNE 2015 ISSUE

HMATT CHASE

ow can marketers predict whether audiences will pay to see a new film or

download a new song? Such forecasts are notoriously tricky. Academic

researchers call films and songs fashion products, because their sales are

driven by volatile consumer tastes. Fashion products often have short life cycles and rely

on impulsive purchase decisions.

One way to improve forecasts is to seek more data. But new research suggests that with

fashion products, this doesnt always help. IE Business Schools Matthias Seifert and a

team of colleagues examined the joint role of historical and contextual data in human

judgments about which products will take off and which will sputter. For example, how

does a music company weigh historical data (Taylor Swifts previous album sold X

copies) against contextual data (We plan to spend Y marketing this album) when

evaluating upcoming releases?

Because creative industries are highly dynamic, historical data isnt always useful on its

own. Although you might assume that, say, a movie with a big-name actor will be a hit,

studies have found that star power isnt actually a significant predictor of box office

receipts. And contextual data tends to be subjective and qualitative, which increases the

complexity of the judgment at hand.

Seiferts team studied predictions about where pop singles would enter the Top 100 chart.

First they interviewed 23 senior managers at four major record companies to identify

predictor variablesfactors that ought to correlate with success. These included the

marketing budget for each single, who else was releasing singles the same week, and

whether the artist was an established performer or a fresh face. Then they gave lists of

forthcoming singles, along with predictor-variable information about them, to 92 A&R

managerspeople who scout and recruit musicians. Over a 12-week period the A&R

managers completed four online questionnaires about the likely chart-entry positions of

the singles theyd been assigned, using the predictor variables to make their forecasts210

in all. After that the researchers sat back to see how each single performed.

Finally they categorized each predictor variable as historical or contextual and ran

mathematical analyses to see which type had worked bestand how the two had worked

together. They found an interesting wrinkle: We make better judgments about volatile

demand when we consider only contextual data. Historical data seemed to impair the A&R

managers ability to interpret context.

Because algorithms can forecast linear relationships much better than the human brain

can, Seiferts team suggests letting a computer make decisions in stable environments

where the predictions depend solely on historical data. But when asking someone to make

a judgment call in a volatile environment, consider withholding historical information so

that he or she can focus on contextual information. More data isnt always better.

About the Research: Effective Judgmental Forecasting in the Context of Fashion

Products, by Matthias Seifert, Enno Siemsen, Allgre L. Hadida, and Andreas B.

Eisingerich

A version of this article appeared in the June 2015 issue of Harvard Business Review.

Related Topics: DATA

This article is about ANALYTICS

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