Will Smith Predicts Movie Success!

http://sloanreview.mit.edu/the-magazine/articles/2009/winter/50207/what-people-want-and-how-to-predict-it/

Creative judgment and expertise will always play a vital role in the creation, shaping and marketing of cultural products. But the balance between art and science is shifting. Today companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago.

As a result, the prediction of consumer taste is quietly becoming a prominent feature of the entertainment and shopping landscape. Creators and distributors of cultural products are attempting to predict how successful a particular product will be before, during or after its creation. Consumers of cultural products can draw upon recommendations — a form of prediction as well — about which products or product attributes will appeal to them.

As offerings proliferate and consumers’ “share of mind” comes under assault from a bombardment of choices and opinions, recommendation technologies will allow consumers to evaluate options and synthesize ratings more systematically. Prediction will be equally useful for creators of products and content.

Valuing Prediction and Recommendation

One of the reasons that recommendation offerings are proliferating is that consumers today are overwhelmed by “the paradox of choice” — so many choices to make, and no easy way to distinguish among the offerings.

Technology — Prediction’s Great Enabler

A key reason why prediction and recommendation are important now is that they are easier to realize, from a technical standpoint.

The best reason to use recommendations, however, is that they seem to work — at least for consumers (predictions of success for creators are too new to judge effectiveness). Netflix, for example, has found that customers like its recommendations about 10% better (half a star in their five-star rating system) than their own selections.

Several companies have also discovered that their recommendations help to sell more products. Acquamedia found that revenue for its mobile phone network customers increased between 15% and 20% when consumers made use of its music recommendations. Silver Egg reported double-digit growth when its customers were offered recommendations for media purchases. Blockbuster Inc. has seen decreased customer churn month to month since it deployed the ChoiceStream Inc. recommendation engine. Overstock.com Inc. employed a ChoiceStream-based Gift Finder on its Web site in time for the 2006 holiday season, and the technology increased revenue by 250% from those who used it.17 Overstock also found that in the first 18 months after launching a refined e-mail targeting system, e-mail marketing revenue doubled and the average order size increased 5.9%.18

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