Netflix is on the verge of surpassing 100 million global subscribers, a testament to how much the video streaming service has changed the entertainment landscape since its debut a decade ago.
The company will reach that milestone this weekend if its projections are correct. Netflix made the prediction Monday with the release of its first-quarter earnings.
The service added nearly 5 million subscribers during the first three months of the year, and will end March with 98.7 million customers in roughly 190 countries.
THANK THE SMARTPHONE
Over the past decade, “what really did it for Netflix was the explosion of phones and tablets that allowed people to watch video everywhere,” said Wedbush Securities analyst Michael Pachter. “But Netflix clearly had a vision before those devices became so ubiquitous.”
About 51 million of Netflix’s subscribers are in the U.S. By the end of this year, Piper Jaffray analyst Michael Olson expects the majority of the company’s subscribers to be overseas. Netflix ended March with nearly 48 million subscribers outside the U.S.
Netflix CEO Reed Hastings expects the next 100 million subscribers to come more quickly than the first 100 million, but he didn’t provide a specific timetable during online video review of the company’s first quarter.
“Everybody watches TV and nearly everybody has the internet, so I don’t see anything that’s going to stop Netflix from getting to most people in the United States and then eventually hopefully most people around the world,” Hastings said.
Progress toward that ambitious goal has helped drive the company’s stock price progressively higher over the past five years. In that time, Netflix has added 72 million more subscribers.
The Los Gatos, California company currently has a market value of about USD63 billion. Its stock rose $1.90 to $149.15 in Monday’s extended trading, even though subscriber growth during the first quarter came in slightly below management forecasts.
STILL CHASING HBO
For all its success, Netflix still has a ways to go before it catches up with HBO, the popular pay-TV channel that has served as its role model. HBO has 134 million subscribers worldwide, including viewers paying for an internet-only version of the channel that was inspired by Netflix’s success.
Other cable channels also are offering internet-only options as more viewers, especially younger people, eschew traditional TV packages and subscribe to streaming services instead.
The trend has confronted Netflix with more competition in the battle for household entertainment budgets. Netflix so far has answered the challenge by spending heavily on original shows such as “Stranger Things” and “House of Cards” and selling its service at a relatively low price. Netflix’s subscriptions range from $8 to $12 per month, with the most popular option at $10.
“The model works from a consumer perspective because it is such a good value,” Pachter said.
In a measure of how much Netflix subscribers like the service, Hastings said this week that they collectively stream more than 1 billion hours of programming per week.
By comparison, Google says about 1 billion hours of video per day are watched on its mostly free YouTube service. “We have YouTube envy,” Hastings joked.
WILL NETFLIX RAISE PRICES?
Pachter and other analysts wonder how long Netflix will be able to hold the line on price as its programming costs rise in tandem with its appeal to a more diverse international audience. Movie and TV studios typically also demand more money as more people subscribe to channels in an effort to make as much as possible off their content.
As it is, Netflix expects to spend about $6 billion on programming this year.
Netflix hasn’t given any inkling it will raise prices again. It lost some long-time U.S. subscribers after their rates went up by as much as $2 per month last year. Netflix had previously frozen prices for millions of subscribers at 2014 levels.
But if it wants to keep investors happy, the company will eventually have to improve its relatively low profit margin. The Los Gatos, California, company earned $178 million on revenue of $2.6 billion in the first quarter. Analysts predict Netflix will make $482 million on revenue of more than $11 billion for the entire year. Michael Liedtke, AP Technology Writer
How Netflix ratings algorithm helps treating cancer
Researchers are actively using these public data to find the set of gene alterations that are responsible for each tumor type. But this problem is not as simple is identifying a single dysregulated gene in each tumor. Hundreds, if not thousands, of the 20,000 genes in the human genome are dysregulated in cancer. The group of dysregulated genes varies in each patient’s tumor, with smaller sets of commonly reused genes enabling each cancer hallmark.
Precision medicine relies on finding the smaller groups of dysregulated genes that are responsible for biological function in each patient’s tumor. But, genes may have multiple biological functions in different contexts. Therefore, researchers must uncover a set of “overlapping” genes that have common functions in a set of cancer patients.
Linking gene status to function requires complex mathematics and immense computing power. This knowledge is essential to predict of outcome to therapies that would block the function of these genes. So, how can we uncover those overlapping features to predict individual outcomes for patients?
Fortunately for us, this problem has already been solved in computer science. The answer is a class of techniques called “matrix factorization” – and you’ve likely already interacted with these techniques in your everyday life.
In 2009, Netflix held a challenge to personalize movie ratings for each Netflix user. On Netflix, each user has a distinct set of ratings of different movies. While two users may have similar tastes in movies, they may vary wildly in specific genres. Therefore, you cannot rely on comparing ratings from similar users.
Instead, a matrix factorization algorithm finds movies with similar ratings among a smaller group of users. The group of users will vary for each movie. The computer associates each user with a group of movies to a different extent, based upon their individual tastes. The relationships among users are referred to as “patterns.” These patterns are learned from the data, and may find common rankings unforeseen by movie genre alone – for example, users may share a preference for a particular director or actor.
The same process can work in cancer. In this case, the measurements of gene dysregulation are analogous to movie ratings, movie genres to biological function and users to patients’ tumors. The computer searches across patient tumors to find patterns in gene dysregulation that cause the malignant biological function in each tumor.
The analogy between movie ratings and cancer genetics breaks down in the details. Unless they are minors, Netflix users are not constrained in the movies they watch. But, our bodies instead prefer to minimize the number of genes used for any single function. There are also substantial redundancies between genes. To protect a cell, one gene may easily substitute for another to serve a common function. Gene functions in cancer are even more complex. Tumors are also highly complex and rapidly evolving, depending upon random interactions between the cancer cells and the adjacent healthy organ.
To account for these complexities, we have developed a matrix factorization approach called Coordinated Gene Activity in Pattern Sets – or CoGAPS for short. Our algorithm accounts for biology’s minimalism by incorporating as few genes as possible into the patterns for each tumor.
Different genes can also substitute for one another, each serving a similar function in a different context. To account for this, CoGAPS simultaneously estimates a statistic for the so-called “patterns” of gene function. This allows us to compute the probability of each gene being used in each biological function in a tumor.
For example, many patients take a targeted therapeutic called cetuximab to prolong survival in colorectal, pancreatic, lung and oral cancers. Our recent work found that these patterns can distinguish gene function in cancer cells that respond to the targeted therapeutic agent cetuximab from those that do not.
Editor’s Note – This is an excerpt of an article by Elana Fertig, Assistant Professor of Oncology Biostatistics and Bioinformatics at the Johns Hopkins University, titled “What Netflix can teach us about treating cancer”, which was originally published yesterday in theconversation.com.
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