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Investor Opportunities in Motion Picture Profits through Feature Film Box-Office Forecasts / Pre-Production Script Development / Cinematic Archetype Casting / Component Formulation Design / U.S. and Global Market Consulting & Mass Audience Forecasting
An article from the New Yorker on Epagogix™ and Platinum Blue™. Two companies that use a technological approach that is similar to but not as extensive, as CFI Assurance's technology.
THE FORMULA by MALCOLM GLADWELL
What if you built a machine to predict hit movies?
Issue of 2006-10-16 Posted 2006-10-09
... The most famous dictum about Hollywood belongs to the screenwriter William Goldman. “Nobody knows anything,” Goldman wrote in “Adventures in the Screen Trade” a couple of decades ago. “Not one person in the entire motion picture field knows for a certainty what’s going to work. Every time out it’s a guess.” One of the highest-grossing movies in history, “Raiders of the Lost Ark,” was offered to every studio in Hollywood, Goldman writes, and every one of them turned it down except Paramount: “Why did Paramount say yes? Because nobody knows anything. And why did all the other studios say no? Because nobody knows anything. And why did Universal, the mightiest studio of all, pass on Star Wars? . . . Because nobody, nobody—not now, not ever—knows the least goddamn thing about what is or isn’t going to work at the box office.”
What Goldman was saying was a version of something that has long been argued about art: that there is no way of getting beyond one’s own impressions to arrive at some larger, objective truth. There are no rules to art, only the infinite variety of subjective experience. “Beauty is no quality in things themselves,” the eighteenth-century Scottish philosopher David Hume wrote. “It exists merely in the mind which contemplates them; and each mind perceives a different beauty.” Hume might as well have said that nobody knows anything.
But Hume had a Scottish counterpart, Lord Kames, and Lord Kames was equally convinced that traits like beauty, sublimity, and grandeur were indeed reducible to a rational system of rules and precepts. He devised principles of congruity, propriety, and perspicuity: an elevated subject, for instance, must be expressed in elevated language; sound and signification should be in concordance; a woman was most attractive when in distress; depicted misfortunes must never occur by chance. He genuinely thought that the superiority of Virgil’s hexameters to Horace’s could be demonstrated with Euclidean precision, and for every Hume, it seems, there has always been a Kames—someone arguing that if nobody knows anything it is only because nobody’s looking hard enough.
In a small New York loft, just below Union Square, for example, there is a tech startup called Platinum Blue that consults for companies in the music business. Record executives have tended to be Humean: though they can tell you how they feel when they listen to a song, they don’t believe anyone can know with confidence whether a song is going to be a hit, and, historically, fewer than twenty per cent of the songs picked as hits by music executives have fulfilled those expectations. Platinum Blue thinks it can do better. It has a proprietary computer program that uses “spectral deconvolution software” to measure the mathematical relationships among all of a song’s structural components: melody, harmony, beat, tempo, rhythm, octave, pitch, chord progression, cadence, sonic brilliance, frequency, and so on. On the basis of that analysis, the firm believes it can predict whether a song is likely to become a hit with eighty-per-cent accuracy. Platinum Blue is staunchly Kamesian, and, if you have a field dominated by those who say there are no rules, it is almost inevitable that someone will come along and say that there are. The head of Platinum Blue is a man named Mike McCready, and the service he is providing for the music business is an exact model of what Dick Copaken would like to do for the movie business.
McCready is in his thirties, baldish and laconic, with rectangular hipster glasses. His offices are in a large, open room, with a row of windows looking east, across the rooftops of downtown Manhattan. In the middle of the room is a conference table, and one morning recently McCready sat down and opened his laptop to demonstrate the Platinum Blue technology. On his screen was a cluster of thousands of white dots, resembling a cloud. This was a “map” of the songs his group had run through its software: each dot represented a single song, and each song was positioned in the cloud according to its particular mathematical signature. “You could have one piano sonata by Beethoven at this end and another one here,” McCready said, pointing at the opposite end, “as long as they have completely different chord progressions and completely different melodic structures.”
McCready then hit a button on his computer, which had the effect of eliminating all the songs that had not made the Billboard Top 30 in the past five years. The screen went from an undifferentiated cloud to sixty discrete clusters. This is what the universe of hit songs from the past five years looks like structurally; hits come out of a small, predictable, and highly conserved set of mathematical patterns. “We take a new CD far in advance of its release date,” McCready said. “We analyze all twelve tracks. Then we overlay them on top of the already existing hit clusters, and what we can tell a record company is which of those songs conform to the mathematical pattern of past hits. Now, that doesn’t mean that they will be hits. But what we are saying is that, almost certainly, songs that fall outside these clusters will not be hits—regardless of how much they sound and feel like hit songs, and regardless of how positive your call-out research or focus-group research is.” Four years ago, when McCready was working with a similar version of the program at a firm in Barcelona, he ran thirty just-released albums, chosen at random, through his system. One stood out. The computer said that nine of the fourteen songs on the album had clear hit potential—which was unheard of. Nobody in his group knew much about the artist or had even listened to the record before, but the numbers said the album was going to be big, and McCready and his crew were of the belief that numbers do not lie. “Right around that time, a local newspaper came by and asked us what we were doing,” McCready said. “We explained the hit-prediction thing, and that we were really turned on to a record by this artist called Norah Jones.” The record was “Come Away with Me.” It went on to sell twenty million copies and win eight Grammy awards.
The strength of McCready’s analysis is its precision. This past spring, for instance, he analyzed “Crazy,” by Gnarls Barkley. The computer calculated, first of all, the song’s Hit Grade—that is, how close it was to the center of any of those sixty hit clusters. Its Hit Grade was 755, on a scale where anything above 700 is exceptional. The computer also found that “Crazy” belonged to the same hit cluster as Dido’s “Thank You,” James Blunt’s “You’re Beautiful,” and Ashanti’s “Baby,” as well as older hits like “Let Me Be There,” by Olivia Newton-John, and “One Sweet Day,” by Mariah Carey, so that listeners who liked any of those songs would probably like “Crazy,” too. Finally, the computer gave “Crazy” a Periodicity Grade—which refers to the fact that, at any given time, only twelve to fifteen hit clusters are “active,” because from month to month the particular mathematical patterns that excite music listeners will shift around. “Crazy” ’s periodicity score was 658—which suggested a very good fit with current tastes. The data said, in other words, that “Crazy” was almost certainly going to be huge—and, sure enough, it was.
If “Crazy” hadn’t scored so high, though, the Platinum Blue people would have given the song’s producers broad suggestions for fixing it. McCready said, “We can tell a producer, ‘These are the elements that seem to be pushing your song into the hit cluster. These are the variables that are pulling your song away from the hit cluster. The problem seems to be in your bass line.’ And the producer will make a bunch of mixes, where they do something different with the bass lines—increase the decibel level, or muddy it up. Then they come back to us. And we say, ‘Whatever you were doing with mix No. 3, do a little bit more of that and you’ll be back inside the hit cluster.’ ”
McCready stressed that his system didn’t take the art out of hit-making. Someone still had to figure out what to do with mix No. 3, and it was entirely possible that whatever needed to be done to put the song in the hit cluster wouldn’t work, because it would make the song sound wrong—and in order to be a hit a song had to sound right. Still, for the first time you wouldn’t be guessing about what needed to be done. You would know. And what you needed to know in order to fix the song was much simpler than anyone would have thought. McCready didn’t care about who the artist was, or the cleverness of the lyrics. He didn’t even have a way of feeding lyrics into his computer. He cared only about a song’s underlying mathematical structure. “If you go back to the popular melodies written by Beethoven and Mozart three hundred years ago,” he went on, “they conform to the same mathematical patterns that we are looking at today. What sounded like a beautiful melody to them sounds like a beautiful melody to us. What has changed is simply that we have come up with new styles and new instruments. Our brains are wired in a way—we assume—that keeps us coming back, again and again, to the same answers, the same pleasure centers.” He had sales data and Top 30 lists and deconvolution software, and it seemed to him that if you put them together you had an objective way of measuring something like beauty. “We think we’ve figured out how the brain works regarding musical taste,” McCready said.
It requires a very particular kind of person, of course, to see the world as a code waiting to be broken. Hume once called Kames “the most arrogant man in the world,” and to take this side of the argument you have to be. Kames was also a brilliant lawyer, and no doubt that matters as well, because to be a good lawyer is to be invested with a reverence for rules. (Hume defied his family’s efforts to make him a lawyer.) And to think like Kames you probably have to be an outsider. Kames was born Henry Home, to a farming family, and grew up in the sparsely populated cropping-and-fishing county of Berwickshire; he became Lord Kames late in life, after he was elevated to the bench. (Hume was born and reared in Edinburgh.) His early published work was about law and its history, but he soon wandered into morality, religion, anthropology, soil chemistry, plant nutrition, and the physical sciences, and once asked his friend Benjamin Franklin to explain the movement of smoke in chimneys. Those who believe in the power of broad patterns and rules, rather than the authority of individuals or institutions, are not intimidated by the boundaries and hierarchies of knowledge. They don’t defer to the superior expertise of insiders; they set up shop in a small loft somewhere downtown and take on the whole music industry at once. The difference between Hume and Kames is, finally, a difference in kind, not degree. You’re either a Kamesian or you’re not. And if you were to create an archetypal Kamesian—to combine lawyerliness, outsiderness, and supreme self-confidence in one dapper, Charlie Brown-headed combination? You’d end up with Dick Copaken.
“I remember when I was a sophomore in high school and I went into the bathroom once to wash my hands,” Copaken said. “I noticed the bubbles on the sink, and it fascinated me the way these bubbles would form and move around and float and reform, and I sat there totally transfixed. My father called me, and I didn’t hear him. Finally, he comes in. ‘Son. What the . . . are you all right?’ I said, ‘Bubbles, Dad, look what they do.’ He said, ‘Son, if you’re going to waste your time, waste it on something that may have some future consequence.’ Well, I kind of rose to the challenge. That summer, I bicycled a couple of miles to a library in Kansas City and I spent every day reading every book and article I could find on bubbles.”
Bubbles looked completely random, but young Copaken wasn’t convinced. He built a bubble-making device involving an aerator from a fish tank, and at school he pleaded with the math department to teach him the quadratic equations he needed to show why the bubbles formed the way they did. Then he devised an experiment, and ended up with a bronze medal at the International Science Fair. His interest in bubbles was genuine, but the truth is that almost anything could have caught Copaken’s eye: pop songs, movies, the movement of chimney smoke. What drew him was not so much solving this particular problem as the general principle that problems were solvable—that he, little Dick Copaken from Kansas City, could climb on his bicycle and ride to the library and figure out something that his father thought wasn’t worth figuring out...
...Dick Copaken has a friend named Nick Meaney. They met on a case years ago. Meaney has thick dark hair. He is younger and much taller than Copaken, and seems to regard his friend with affectionate amusement. Meaney’s background is in risk management, and for years he’d been wanting to bring the principles of that world to the movie business. In 2003, Meaney and Copaken were driving through the English countryside to Durham when Meaney told Copaken about a friend of his from college. The friend and his business partner were students of popular narrative: the sort who write essays for obscure journals serving the small band of people who think deeply about, say, the evolution of the pilot episode in transnational TV crime dramas. And, for some time, they had been developing a system for evaluating the commercial potential of stories. The two men, Meaney told Copaken, had broken down the elements of screenplay narrative into multiple categories, and then drawn on their encyclopedic knowledge of television and film to assign scripts a score in each of those categories—creating a giant screenplay report card. The system was extraordinarily elaborate. It was under constant refinement. It was also top secret. Henceforth, Copaken and Meaney would refer to the two men publicly only as “Mr. Pink” and “Mr. Brown,” an homage to “Reservoir Dogs.”
“The guy had a big wall, and he started putting up little Post-its covering everything you can think of,” Copaken said. It was unclear whether he was talking about Mr. Pink or Mr. Brown or possibly some Obi-Wan Kenobi figure from whom Mr. Pink and Mr. Brown first learned their trade. “You know, the star wears a blue shirt. The star doesn’t zip up his pants. Whatever. So he put all these factors up and began moving them around as the scripts were either successful or unsuccessful, and he began grouping them and eventually this evolved to a kind of ad-hoc analytical system. He had no theory as to what would work, he just wanted to know what did work.”
Copaken and Meaney also shared a fascination with a powerful kind of computerized learning system called an artificial neural network. Neural networks are used for data mining—to look for patterns in very large amounts of data. In recent years, they have become a critical tool in many industries, and what Copaken and Meaney realized, when they thought about Mr. Pink and Mr. Brown, was that it might now be possible to bring neural networks to Hollywood. They could treat screenplays as mathematical propositions, using Mr. Pink and Mr. Brown’s categories and scores as the motion-picture equivalents of melody, harmony, beat, tempo, rhythm, octave, pitch, chord progression, cadence, sonic brilliance, and frequency.
Copaken and Meaney brought in a former colleague of Meaney’s named Sean Verity, and the three of them signed up Mr. Pink and Mr. Brown. They called their company Epagogix—a reference to Aristotle’s discussion of epagogic, or inductive, learning—and they started with a “training set” of screenplays that Mr. Pink and Mr. Brown had graded. Copaken and Meaney won’t disclose how many scripts were in the training set. But let’s say it was two hundred. Those scores—along with the U.S. box-office receipts for each of the films made from those screenplays—were fed into a neural network built by a computer scientist of Meaney’s acquaintance. “I can’t tell you his name,” Meaney said, “but he’s English to his bootstraps.” Mr. Bootstraps then went to work, trying to use Mr. Pink and Mr. Brown’s scoring data to predict the box-office receipts of every movie in the training set. He started with the first film and had the neural network make a guess: maybe it said that the hero’s moral crisis in act one, which rated a 7 on the 10-point moral-crisis scale, was worth $7 million, and having a gorgeous red-headed eighteen-year-old female lead whose characterization came in at 6.5 was worth $3 million and a 9-point bonding moment between the male lead and a four-year-old boy in act three was worth $2 million, and so on, putting a dollar figure on every grade on Mr. Pink and Mr. Brown’s report card until the system came up with a prediction. Then it compared its guess with how that movie actually did. Was it close? Of course not. The neural network then went back and tried again. If it had guessed $20 million and the movie actually made $110 million, it would reweight the movie’s Pink/Brown scores and run the numbers a second time. And then it would take the formula that worked best on Movie One and apply it to Movie Two, and tweak that until it had a formula that worked on Movies One and Two, and take that formula to Movie Three, and then to four and five, and on through all two hundred movies, whereupon it would go back through all the movies again, through hundreds of thousands of iterations, until it had worked out a formula that did the best possible job of predicting the financial success of every one of the movies in its database.
That formula, the theory goes, can then be applied to new scripts. If you were developing a $75-million buddy picture for Bruce Willis and Colin Farrell, Epagogix says, it can tell you, based on past experience, what that script’s particular combination of narrative elements can be expected to make at the box office. If the formula says it’s a $50-million script, you pull the plug. “We shoot turkeys,” Meaney said. He had seen Mr. Bootstraps and the neural network in action: “It can sometimes go on for hours. If you look at the computer, you see lots of flashing numbers in a gigantic grid. It’s like ‘The Matrix.’ There are a lot of computations. The guy is there, the whole time, looking at it. It eventually stops flashing, and it tells us what it thinks the American box-office will be. A number comes out.”
The way the neural network thinks is not that different from the way a Hollywood executive thinks: if you pitch a movie to a studio, the executive uses an ad-hoc algorithm—perfected through years of trial and error—to put a value on all the components in the story. Neural networks, though, can handle problems that have a great many variables, and they never play favorites—which means (at least in theory) that as long as you can give the neural network the same range of information that a human decision-maker has, it ought to come out ahead. That’s what the University of Arizona computer scientist Hsinchun Chen demonstrated ten years ago, when he built a neural network to predict winners at the dog track. Chen used the ten variables that greyhound experts told him they used in making their bets—like fastest time and winning percentage and results for the past seven races—and trained his system with the results of two hundred races. Then he went to the greyhound track in Tucson and challenged three dog-racing handicappers to a contest. Everyone picked winners in a hundred races, at a modest two dollars a bet. The experts lost $71.40, $61.20, and $70.20, respectively. Chen won $124.80. It wasn’t close, and one of the main reasons was the special interest the neural network showed in something called “race grade”: greyhounds are moved up and down through a number of divisions, according to their ability, and dogs have a big edge when they’ve just been bumped down a level and a big handicap when they’ve just been bumped up. “The experts know race grade exists, but they don’t weight it sufficiently,” Chen said. “They are all looking at win percentage, place percentage, or thinking about the dogs’ times.”
Copaken and Meaney figured that Hollywood’s experts also had biases and skipped over things that really mattered. If a neural network won at the track, why not Hollywood? “One of the most powerful aspects of what we do is the ruthless objectivity of our system,” Copaken said. “It doesn’t care about maintaining relationships with stars or agents or getting invited to someone’s party. It doesn’t care about climbing the corporate ladder. It has one master and one master only: how do you get to bigger box-office? Nobody else in Hollywood is like that.”
In the summer of 2003, Copaken approached Josh Berger, a senior executive at Warner Bros. in Europe. Meaney was opposed to the idea: in his mind, it was too early. “I just screamed at Dick,” he said. But Copaken was adamant. He had Mr. Bootstraps, Mr. Pink, and Mr. Brown run sixteen television pilots through the neural network, and try to predict the size of each show’s eventual audience. “I told Josh, ‘Stick this in a drawer, and I’ll come back at the end of the season and we can check to see how we did,’ ” Copaken said. In January of 2004, Copaken tabulated the results. In six cases, Epagogix guessed the number of American homes that would tune in to a show to within .06 per cent. In thirteen of the sixteen cases, its predictions were within two per cent. Berger was floored. “It was incredible,” he recalls. “It was like someone saying to you, ‘We’re going to show you how to count cards in Vegas.’ It had that sort of quality.”
Copaken then approached another Hollywood studio. He was given nine unreleased movies to analyze. Mr. Pink, Mr. Brown, and Mr. Bootstraps worked only from the script—without reference to the stars or the director or the marketing budget or the producer. On three of the films—two of which were low-budget—the Epagogix estimates were way off. On the remaining six—including two of the studio’s biggest-budget productions—they correctly identified whether the film would make or lose money. On one film, the studio thought it had a picture that would make a good deal more than $100 million. Epagogix said $49 million. The movie made less than $40 million. On another, a big-budget picture, the team’s estimate came within $1.2 million of the final gross. On a number of films, they were surprisingly close. “They were basically within a few million,” a senior executive at the studio said. “It was shocking. It was kind of weird.” Had the studio used Epagogix on those nine scripts before filming started, it could have saved tens of millions of dollars. “I was impressed by a couple of things,” another executive at the same studio said. “I was impressed by the things they thought mattered to a movie. They weren’t the things that we typically give credit to. They cared about the venue, and whether it was a love story, and very specific things about the plot that they were convinced determined the outcome more than anything else. It felt very objective. And they could care less about whether the lead was Tom Cruise or Tom Jones.”
The Epagogix team knocked on other doors that weren’t quite so welcoming. This was the problem with being a Kamesian. Your belief in a rule-bound universe was what gave you, an outsider, a claim to real expertise. But you were still an outsider. You were still Dick Copaken, the blue-blazered corporate lawyer who majored in bubbles as a little boy in Kansas City, and a couple of guys from the risk-management business, and three men called Pink, Brown, and Bootstraps—and none of you had ever made a movie in your life. And what were you saying? That stars didn’t matter, that the director didn’t matter, and that all that mattered was story—and, by the way, that you understood story the way the people on the inside, people who had spent a lifetime in the motion-picture business, didn’t. “They called, and they said they had a way of predicting box-office success or failure, which is everyone’s fantasy,” one former studio chief recalled. “I said to them, ‘I hope you’re right.’ ” The executive seemed to think of the Epagogix team as a small band of Martians who had somehow slipped their U.F.O. past security. “In reality, there are so many circumstances that can affect a movie’s success,” the executive went on. “Maybe the actor or actress has an external problem. Or this great actor, for whatever reason, just fails. You have to fire a director. Or September 11th or some other thing happens. There are many people who have come forward saying they have a way of predicting box-office success, but so far nobody has been able to do it. I think we know something. We just don’t know enough. I still believe in something called that magical thing—talent, the unexpected. The movie god has to shine on you.” You were either a Kamesian or you weren’t, and this person wasn’t: “My first reaction to those guys? Bullshit.”
How Hollywood's Power Elite Lost the Plot
By Mark Hooper
Published: 19 November 2006
Hollywood, like nowhere else, loves a system. Everyone is in search of the perfect plot, of the golden rules to guarantee a box-office hit like Shyamalan claimed to have unearthed. Star Wars director George Lucas has spoken of the need to create "60 two-minute scenes" with which to sustain the public's excitement. He is also a famous proponent of the theories of Joseph Campbell, whose book The Hero With a Thousand Faces posited the concept of the "monomyth", an archetypal heroic plot that is supposedly common to all mythologies and religions. But it wasn't just Lucas's biblical sci-fi fantasy that benefited from Campbell's vision. A seven-page memo created for Disney by Hollywood producer Christopher Vogler reignited interest in the monomyth in the 1990s - inspiring films as diverse as The Lion King and The Matrix trilogy.
And a recurring thread running throughout many of these epic stories is that the hero is just as often an unknown, unproven actor. Mark Hamill and Harrison Ford, for instance, were nobodies before Lucas cast them as the ultimate heroes, allowing the audience to empathise with their journey all the more. Even better, there's no reason why the hero can't be a cartoon cat. As long as the plot is in place, the rest will follow.
Recently, Hollywood has been all abuzz over a new golden formula. Epagogix is a system for determining the commercial potential of screenplays, being hawked by the entrepreneurs Dick Copaken, Nick Meaney and Sean Verity, as well as two boffins who refer to themselves only as "Mr Pink" and "Mr Brown" (after the characters in Reservoir Dogs). The team concentrates specifically on how to break the $50m barrier that is seen to mark the divide between a hit movie and a flop.
The Epagogix approach is elaborate and almost insanely detailed, but its strength, say its inventors, is that it is ruthlessly impartial: it is purely interested in what makes money (and what doesn't). One of the main findings, from their extensive research, was that neither the identity of the star nor the director was a major factor in guaranteeing success - plot development, locale and character are far more important. As one studio executive, quoted by Malcolm Gladwell in The New Yorker, said: "They cared about venue, and whether it was a love story, and very specific things about the plot that they were convinced determined the outcome. It felt very objective. And they couldn't care less whether the lead was Tom Cruise of Tom Jones."
You can see the appeal to the studios: a supposedly foolproof system for delivering box-office returns - without the need for A-list tantrums or fees. As cold and clinical as it sounds, Epagogix provides the message all the studios want to hear: the script is everything. We can survive without prima donnas. http://www.newyorker.com/fact/content/articles/061016fa_fact6
Oct 16th 2006: What if you built a machine to predict hit movies? Writing in the New Yorker magazine, Malcolm Gladwell, noted author of ‘Blink’ and ‘Tipping Point’ takes an in-depth look at why different people like different kinds of film, and how Hollywood approaches this. He examines the historic context and application of rules and systems, neural networks, and talks to Epagogix’s staff about their approach in general, and how it applies to a specific sample movie. Read full article(English)
Oct 8th 2006: "This is an incredibly valuable tool" Noted American writer Malcolm Gladwell at the podium at the annual New Yorker Magazine Festival. Gladwell talks astutely and entertainingly about how a far-sighted major Hollywood studio chief introduced him to Dick Copaken, a noted Washington DC lawyer who loves film and who became actively involved with the launch of Epagogix - a UK company, based on an innovative British idea. Epagogix has a contract with this studio, which recognises the creative and commercial value of Epagogix's output. Gladwell illustrates Epagogix's approach very well, within the constraints of the company's strict confidentiality agreements and IP protection requirements, although in some places, by necessity, he must speculate about or condense details of Epagogix's genesis and process. Please note that contrary to a statement later in the film, Epagogix does not only focus on major studios, but also reviews scripts for independents. Watch the video(English)
February 20th 2007: Is there a formula to predict Oscar Winners? Deutsche Welle Radio's Matthew Lawton asks Epagogix's Nick Meaney this timely question, and investigates other aspects of the 'artistic' versus the 'commercial' imperatives of film-making. Listen to the interview(English - MP3 sound file 3.43 MB) Deutsche Welle homepage: http://www.dw-world.de
Feb 9th 2007: Q&A with Nick Meaney, Epagogix’s CEO Timed to coincide with the opening of the Berlin Film Festival, the German Financial Times speaks to Epagogix’s Nick Meaney about movies and methodologies, rather than the business angle you might expect. Read full article(German - PDF document, Epagogix article on page 2)
Jan 28th 2007: Where commerce meets creativity Peter Körte, the well-respected cultural editor of the Frankfurter Allgemeine Sonntagszeiting takes an in-depth look at Hollywood and interviews members of Epagogix's team about the potential impact of their services. Read full article(German)
24th Dec 2006: Die 200-Millonen-Dollar-Formel Malcolm Gladwell’s groundbreaking analysis of the impact and implications of Epagogix’s approach translated into the German language by Switzerland’s Neue Zurcher Zeitung, NZZ online. Read full article(German)
19th November 2006: How Hollywood's power elite lost the plot Mark Hooper, of The Independent believes that the business of movie-making is undergoing a major shift, and sees that Epagogix is ‘setting Hollywood a-buzz’. Read full article(English)
16th November 2006: Per il botto al botteghino c'è un software malandrino Marco de Martino provides a summary overview of Epagogix’s methodology for the news website Panorama.it. Please note that the article incorrectly identifies the origins of Epagogix’s services and wrongly attributes the initial development of Epagogix’s methodology to the USA, rather than the UK. Read full article(Italian)
Oct 16th 2006: What if you built a machine to predict hit movies?
February 20th 2007: Is there a formula to predict Oscar Winners?
24th Dec 2006: Die 200-Millonen-Dollar-Formel
16th November 2006: Per il botto al botteghino c'è un software malandrino