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

An Array of Techniques and Technologies

Executives who want to incorporate predictive technologies in their businesses must first understand the variety of approaches that already exist. (See ”The Prediction Lover’s Handbook,” page 32.) The first generation of technology, collaborative filtering, makes correlations either item by item or customer by customer. This approach is still employed today — not only by Amazon and Netflix, but also by companies such as mobile telephone music recommender LiveWire Mobile Inc., which distributes musical choices through more than 20 wireless carriers around the world.

A relatively new approach to recommendation focuses on the attributes of an item. A movie, for example, might be classified by its length, genre (“criminal thriller”), theme (“unlikely criminals”), tone (“ominous, forceful, gritty and tense”), critic’s rating and so forth. An analysis of the movies a customer likes could lead to recommendations of other movies with similar attributes. ChoiceStream does this for both movies and online shopping. The online radio station Pandora (using classifications created by its employees) and the music software recommendation company Echo Nest (using computational analysis of sound as well as textual analysis of online content about music) have classified different aspects of thousands of songs — including timbre, key, tempo, time signature and instruments.

Other possible approaches to prediction include markets like the Hollywood Stock Exchange or Media Predict. Platinum Blue Music Intelligence employs “spectral deconvolution” of sound waves to identify songs that would be appealing to a particular listener. Epagogix uses a proprietary expert system with neural network-based algorithms to predict a movie’s success before it’s made, and many studios use regression analysis to project the success of a film before release.

Some companies are beginning to add social networking as a means of recommending cultural products. If your friends like certain songs and movies, perhaps you will like them, too — and if you and a stranger like the same songs and movies, perhaps you should become friends. LiveWire Mobile and Last.fm Ltd. have a social networking element in their music offerings, and Netflix has a “Friends” service that lets customers share movie preferences and reviews with a community.

Each prediction or recommendation approach has particular strengths and weaknesses in the context of the application. Collaborative filtering, for example, requires a substantial amount of data on past purchases to work effectively. Even when enough data exists, some experts believe collaborative filtering reduces the diversity in purchases made and makes blockbuster hits even bigger.19 Neural networks also require a large amount of data. Attribute-based recommendation requires that someone classify cultural products according to several key attributes; if there isn’t already a source of attributes for a product, developing one can be difficult. Prediction markets need a large number of independent participants to succeed; most offer some sort of prize or token reward to attract them. Of course, if a third party has already rounded up all the necessary resources to offer predictions or recommendations for your product, all you have to do is pay for them.

The best recommendation tools perform a balancing act: They connect to consumers’ sense of individuality as well as their group identification. Similarly, the tools must come up with recommendations that stretch horizons with suggestions that are new and a bit surprising, yet not off-putting. Recommendation approaches vary in how much access to the “long tail” of niche or obscure products they provide. Most recommendation engines offer a balance of the familiar and the unexplored.

At LiveWire Mobile, for example, customers want both reliable and well-known songs similar to those they like, as well as songs from different parts of the world and from different musical genres that may challenge or advance their tastes. But LiveWire Mobile’s business model is a pay-per-song model, which makes its customers somewhat more conservative than they might be in a subscription model. The lesson for executives is that if people are buying your product one at a time, choose a recommendation approach that provides conservative recommendations; if they like you enough to pay you a monthly fee, they’re probably open to a recommendation engine that provides pleasant surprises.

Finally, because markets for cultural products shift over time, it is critical to monitor changing market conditions continuously to identify emerging trends. “Model management” is essential to the development of recommendation algorithms that reflect lessons from experience, test assumptions and improve the accuracy of predictions. Netflix, for example, developed many of its recommendation approaches with customers who were Internet pioneers; now that it’s also serving later adopters, the company’s analysts feel the need to develop new tests and algorithms.

On the cutting edge of technology are attempts to identify patterns of intrinsic appeal to human viewers or, more commonly, listeners. Scientists are learning more about the mathematical connections hidden in music and how they contribute to a desire to hear certain songs repeatedly — a condition known as “earworms” or “cognitive itch.”20 Platinum Blue Music Intelligence has applied this research to analyze a song and provide recommendations to increase the likelihood that the song will be a hit — by fine-tuning the bass line, for example. CEO Mike McCready describes his company’s goal as “helping both artists and producers by explaining factors that increase the odds of a successful release.”

The company’s analysis has resulted in the creation of 60 distinct clusters, a dozen or so of which are active at any particular point in history. A Chopin prelude may be in the same cluster as songs by Frank Sinatra, Genesis and ZZ Top. Billed as a tool to assist artists and producers, Platinum Blue’s technology uses spectral sound-wave analysis to offer advice. For example, the tool was used to analyze the song “Crazy,” by Gnarls Barkley. The analysis found that “Crazy” belonged to the same hit cluster as several recent hit songs as well as older hits by Olivia Newton-John and Mariah Carey. The data clearly indicated that “Crazy” was going to be a huge success, which it was.

New technologies will continue to emerge for analyzing and predicting consumer tastes. Innerscope Research is beginning to employ biological approaches to study consumer engagement for advertising and television programs.21 The company measures biological indicators of mental engagement, such as heart rate and galvanic skin response. NASA developed an even more direct measure of human attention using brain waves, but thus far the technology has not been successfully applied commercially. As soon as it’s clear that money can be made using these biological assessment tools, their use will undoubtedly grow despite some observers’ moral and ethical qualms.

Predictions and the Creative Process

In the great majority of cases we studied, recommendations were made after the cultural product was created and assisted the customer in choosing among finished offerings. Prediction can also be used, as in the European movie theater chain Kinepolis Group NV, to project the staffing levels needed in a particular theater for a particular film on a particular weekend. Studios use similar prediction approaches to judge how many DVDs to manufacture and ship. It is also possible to use the content of recommendations even before and during product creation.

This precreation approach is most actively explored in the movie business, where long production cycles and high costs predominate. Movie producers have long had rules of thumb to guide their moviemaking decisions — a star brings in the crowds, audiences like happy endings, PG-13 rated films are moneymakers, sequels make between two-thirds and three-quarters of the original movie’s box office and so forth. Studio executives at two studios told us that these maxims are widely believed within their companies, though there are plenty of well-known exceptions to these rules. And financial executives at many studios make predictions of how much money a particular picture will make — both before and after initial theatrical release (in so-called decay models). It is clear from the high percentage of unsuccessful films that the decision process is largely nonscientific. One reason is that individual studios produce relatively few movies and cannot compile extensive data. One studio executive, for example, told us that since the studio produces an average of only eight movies per year, a highly statistical approach to predicting success and failure would be impossible.

However, scientific help is now available for moviemakers before their work is done. Epagogix, in particular, is focused on predicting the likelihood of success of movie scripts before production even begins. The company’s neural network analyses identify attributes of scripts that are correlated with either success or failure as defined by box-office revenue. This would seem to be an appealing prospect to studios, and by most accounts it is technically possible.

Using the simple metric of whether a film will recoup its production costs at the U.S. box office, Epagogix is able to predict “turkeys” and “eagles” twice as accurately as studios. It can also make specific recommendations that it predicts will increase box-office receipts: For one film, the software recommended reducing the number of scene locations, a decision that would not only increase the likely box-office take, but also significantly reduce production costs. Hedge fund managers have discussed with Epagogix a studio partnership that would predict the success of a film before it is made, and well-known agents have discussed using Epagogix’s tools before their actor-clients accept roles (particularly those who are paid in part as a percentage of box-office receipts).

Some studio executives themselves, however, have thus far been less than enthusiastic about turning their decision-making art into a science. The primary obstacles appear to be cultural rather than analytical or technological. One executive suggested to Epagogix executives that he would be ostracized by the Hollywood community — and not invited to the good parties! — if word got out that he was producing movies based on analytical prediction models. Epagogix has also developed other contexts in which its prediction approaches might be useful, including typical business situations like “mak[ing] the best objective decisions about spending risk capital and managing operational budgets.”22

This resistance to science has historical precedence in Hollywood, as noted as early as 1941 by Leo Rosten, the academic, screenwriter and humorist: “The movie makers work with hunches, not logic; they trade in impressions rather than analyses. It is natural that they court the intuitive and shun the systematic, for they are expert in the one and untutored in the other.”23 A financial executive at a major studio confirmed in an interview that so far all prediction models have made relatively little headway with executives who make film production decisions, though he is hoping that they will be applied more frequently in the future.

An HBO Inc. executive was similarly skeptical of the feasibility of using analytics on the creative side of the company’s business. HBO’s executives view their role as “human curators” whose discerning audience seeks high-quality original programming that confounds conventional expectations. HBO does employ some analytics, but not for prediction. Its production planning department, for example, uses software with codified rules such as “R movies must not be scheduled in the daytime” to help schedule programming.

It is the artists themselves who may ultimately embrace the use of prediction techniques to guide their decision making for everything from picking movie scripts to fine-tuning a song to optimize its market potential. At Platinum Blue Music Intelligence, the response has been mixed, according to the CEO. “We got thousands of e-mails from musicians essentially saying either, ‘This is just another example of the Man trying to keep us down using impersonal computers’ or ‘When can I get this technology to help me get discovered?’” Those who adopt the technologies may well find they have a powerful new tool to help them.

Business Model Risks and Opportunities

Those who want to incorporate the prediction or recommendation of cultural products into their businesses need to consider several management issues. One is the business model they choose to adopt. Should prediction be the only way to make money, or must it accompany a business model that makes money through other approaches as well?

Many of the companies we studied, including Apple, Netflix, LiveWire Mobile and Amazon, make their money primarily by being distributors of cultural products, not recommenders. The recommendation work they do is a small adjunct to their distribution business. And if the distribution model is problematic — as in the case of Pandora, where the need to pay royalties to record companies for online music almost shut down the company recently — the recommendations alone may not be enough to let an organization thrive. When we suggested to Netflix CEO Reed Hastings that the company’s recommendation capabilities might be sold to other online or telecom-based movie distributors, he replied that recommendations alone were insufficiently valued by most online distributors.

Most of the companies we encountered that provide only recommendation or prediction capabilities are relatively small. To thrive, they need to spread their recommendation capabilities across a variety of industries. ChoiceStream, for example, now offers recommendations not only for movies and online retail, but also for books (through Borders.com), TV listings and music. The company is considering using its technology broadly for targeted online advertising, and Overstock already uses it for this purpose. ATG Recommendations (formerly CleverSet), a recent startup in the recommendation engine business, has customers who are using its tools to sell wine, baked goods, T-shirts and software through the Internet. The Hollywood Stock Exchange has provided the model and tools for online markets in data storage, drug development in pharmaceuticals, and predictions for Popular Science magazine. Recommendation software providers say that their approaches rapidly build up a font of rich, accurate consumer preference data, which they believe to be potentially valuable to producers of the products and services they recommend. Some of these small firms will not succeed, however; the recommendations company MatchMine offered a “portable” set of recommendations that could be transferred across different Web sites, but it recently discontinued operations.

The need to continually update and refine models is another management issue. Netflix offers incentives to external analysts to improve its model through the Netflix Prize: $1 million to anyone who can improve the company’s prediction algorithm by 10% (one group is almost there at about 9.5%, but the contest is already more than two years old). Amazon continues to refine its collaborative filtering model. Attribute-based recommendation companies such as ChoiceStream, which serves multiple industries and customers, must refine not only their analytical models, but also their means of collecting attributes economically. And firms like Echo Nest whose sole offering is their recommendation engine must find ways to make their business profitable through partnering, online advertising or other means.

Finally, despite the great promise of prediction and recommendation systems, it’s important for executives to avoid going to the extreme. These systems are not a substitute for decision making, nor do they provide automatic, infallible answers. Using these tools does not obviate the need for business judgment or cultural acumen. As one movie studio executive put it, “Consumers already complain they are being pandered to. Isn’t this the ultimate in pandering?” Even Will Smith doesn’t rely solely on his analysis in choosing scripts; he also seeks input from his family and friends. Creating successful cultural products will always be a mixture of art and science. It appears, however, that the amount of science in the mixture is increasing. It’s likely that the same science-based approaches to predicting success and recommending products will continue to transform not just the cultural products industry, but any other industry in which new products are expensive and risky, and in which customers lack the time and attention to understand differences among proliferating offerings.

(Reprint #:50207)

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