Magazine Winter 2009 Research Feature

What People Want (and How to Predict It)

Companies now have unprecedented access to data and sophisticated technology that can inform decisions as never before. How successful are they at helping forecast what customers want to watch, listen to and buy?

Thomas H. Davenport and Jeanne G. Harris Reading Time: 27 min 

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The Year 2007 was a terrible year for many big movie stars. One major exception was Will Smith, whose film “I Am Legend” set a box-office record for a movie opening in December, taking in $77 million. In 2008, Smith’s star vehicle “Hancock” grossed more than $625 million worldwide despite poor critical reviews. Smith’s success was not all that surprising, however: With the exception of the Harry Potter movies, those in which Smith star have higher opening weekends and average box-office receipts than movies with any other male lead.1

Does Smith know something that Jim Carrey and others do not? Quite possibly: When Smith went to Hollywood to start his film career, he and his business manager studied a list of the 10 top-grossing movies of all time. “We looked at them and said, OK, what are the patterns?” Smith recalls. “We realized that 10 out of 10 had special effects. Nine out of 10 had special effects with creatures. Eight out of 10 had special effects with creatures and a love story.”2

The leading question

Methods for predicting what consumers want have been around for decades. But how good are the newest tools?

Findings
  • Science-based ways to predict success will keep transforming any industry in which customers lack the time and attention to differentiate among increasing offerings.
  • A wide variety of tools have emerged, which need to be matched to the right application.
  • Though potent, these systems don’t replace decision making.

Smith calls himself a “student of universal patterns” and studies box-office results after every weekend, looking for patterns of success. Given his track record of choosing films that reliably deliver $120 million or more, he is clearly an astute observer.

Smith’s ability to analyze and predict which movies are likely to succeed belies conventional wisdom on predicting consumer taste. Such predictions are viewed as an art, not a science. The reasons for success or failure are inscrutable. Producers of movies, music, books and apparel pursue their artistic visions and offer them to the public, which may or may not recognize genius when it sees it.

It’s easy to see why most people view the prediction of taste as an art. Historically, neither the creators nor the distributors of “cultural products” have used analytics — data, statistics, predictive modeling — to determine the likely success of their offerings.

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About the Authors

Thomas H. Davenport is the President”s Distinguished Professor of Information Technology and Management at Babson College in Wellesley, Massachusetts. Jeanne G. Harris is executive research fellow and director of research for the Accenture Institute for High Performance Business; she is based in Chicago. They are the authors of Competing on Analytics: The New Science of Winning (Harvard Business School Press, 2007).

References

1. R. Grover, “Box Office Brawn,”

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Comments (4)
bryceyahtz
I agree with @Taylor that customers may still take the recommendations provide because they are to lazy to do any amount of thinking or research. A well laid out campaign with the proper PR to hype the movie/book/event can make a huge difference on opening night. I am sure with  Will Smith hiring the best in Hollywood to groom his career helped him become the star he is today and not only by following what movies made the big bucks back in the day. As much as in todays society we claim to be informed buyers I still feel a good campaign with flashy press can sometimes over shadow a bad product or movie and persuade us to see/buy it.
Jamie Roux
I gained a lot of insight from the discussion above. I have always believed that one should present the good attributes of different products and then let the customer decide for himself. Clearly this has been a mistake. Customers who do a lot of research before buying, will not be influenced that much by recommendations, but it seems like many are simply too lazy and just buy according to recommendations. 
I think credibility does play a major role. Take buying shares as an example. Many people have to rely on recommendations simply because they do not have enough knowledge. But they know that a particular broker has proven himself over time, so his recommendation can be followed without hessitation.
Taylor
Even if the data is not analyzed properly, customers may still take the recommendations provided (which are supposed to be what they want) because they are just too lazy or impatient to do any amount of thinking or research itself.

In this case, the market researchers can pat themselves on the back for increasing sales through their data mining, but is it more a case of the customer being allowed to be lead and therefore the all the data research is less relevant
intelligentsystems
Tom & Jeanne,

My compliments on taking such a fresh, and new approach, to the topic of predictive modeling. When reading the article I could not help but think of a hilarious movie Mel Gibson did a few years back, called "What Women Want", where he is an advertising executive who can read women's minds. For generations, people have imagined what it would be like to have a proverbial "crystal ball", and how they would use it to affect financial, military, and social outcomes. The most significant finding in your article though, is that the heads of movie studios do not believe that mathematics can effectively be used to predict outcomes, but rather their own intuition and gut feelings are much better suited to the task. One company that you seem to have overlooked, is Mobile Agent Technologies ( www.agentos.net ), and their methodology for Common Sense Reasoning (tm). Their offering is based upon a rules engine and human cognitive theory. Their software has the ability to simulate human intuition and gut feelings on a computer. This technology when combined with subject matter expertise, data analytics, and predictive models, can be used to build a new generation of automated decision making systems, which can make faster, more accurate, and unbiased decisions. In this economy, the ability to reduce the cost of making decisions is crucial. In over six months of using the Netflix service, I have only ordered one movie that their system has recommended. As for Amazon, I keep getting email solicitations for books on topics that are of absolutely no interest to me. As you stated in your article, product recommendations are only an "adjunct" to the main distribution business of these and similar firms. I am looking forward to future articles on the subject, and more about innovative firms like Mobile Agent Technologies.