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Again, your argument continues to validate one of the essential points I make: that these data sets are most useful for identifying existing media that “works”

Again, your argument continues to validate one of the essential points I make: that these data sets are most useful for identifying existing media that “works” and then scaling/marketing it. Here is where I think we disagree: I don’t believe that Netflix’s ability (or Google’s, for that matter) to predict new markets is substantively improved by their data set. I understand that that might be a radical perspective among data-driven decision-makers, but I stand by it. Yes, the data set is extremely useful for scaling/marketing an asset (as we have both highlighted), but frankly datasets don’t yet know what the next hot thing will be, and there is a lack of statistical evidence to suggest otherwise (ie Netflix has produced, what, 1-10k shows? That doesn’t necessarily provide much statistical reliability, even if they have a high rate of success, which they don’t). Furthermore, Netflix picking themes, topics, etc as you suggest to drive successful production runs contrary to their most successful strategy so far, which is to cast a wide net by producing highly diverse content at low costs then scaling what works. While they use data to deduce which shows are successful, the production of those shows occurred with little to no data-driven direction beyond the obvious understanding that a wide net catches more fish. When a company has a combination of access to scale and quality data, they quickly outpace and overwhelm their competitors. Netflix hasn’t quite accomplished this, suggesting that their dataset isn’t as magical as you imply. Lastly, their dataset isn’t as private as you might believe, either. To view it as a golden egg that will exclusively produce quality content just for them is naive.

joshuamorton 8y ago
>Again, your argument continues to validate one of the essential points I make: that these data sets are most useful for identifying existing media that “works” and then scaling/marketing it. I'm not sure how you're managing to miss this so completely, but no its explicitly not this. Are you familiar with how recommender systems work? Id suggest reading this article[1], which discusses how Netflix uses data to identify the fanbases for shows that they haven't yet launched. Yes, they use data to post-validate successful shows, but everyone does that, it's what Nielson has done for decades. Netflix is doing more: they're able to (with decent accuracy) predict what market a show will be popular with before launch, and they do this to market niche shows that would otherwise never be launched at all with a strong certainty that they'll find success. Your second paragraph invents evidence and certainty and data that you don't have. Frankly, it makes you look like you are unfamiliar with statistics and data analytics, which is fine, but then you shouldn't share strong opinions about how things you don't fully understand can't work. How do you know that they can't have statistical reliability? Thousands of data points is absolutely enough to have statistical certainty. Yes you can absolutely predict the popularity of a genre with a high degree of certainty before filming a single scene. How do you know that they don't have a high rate of success? (I refer you back to [1]: >She notes the mixed reviews for Lost in Space. “Do we care?” Not that much, it turns out. >Furthermore, Netflix picking themes, topics, etc as you suggest to drive successful production runs contrary to their most successful strategy so far, which is to cast a wide net by producing highly diverse content at low costs then scaling what works. I'd just like to make this really, exceptionally clear: they don't pick what content to produce by throwing darts. They pick the diverse ("niche") content in a very data driven way. Again, quoting [1]: > It [...] predicat[es] programming decisions on immense amounts of data about true viewing habits, not estimated ones. It has discovered ways to bundle enough niche viewers to make good business out of fare that used to play only to tiny markets. But I suggest reading the entire article, because it thoroughly dismantles the presumptions your making about how netflix does production. (Especially the part about "Money Heist"). >When a company has a combination of access to scale and quality data, they quickly outpace and overwhelm their competitors. Netflix hasn’t quite accomplished this, suggesting that their dataset isn’t as magical as you imply. Wait, who are their competitors that they aren't outpacing? Netflix absolutely dominates their market (streaming media), they defined it. Everyone else is playing catch up. If you are considering Netflix to be a competitor to WB or Disney, netflix had more revenue than Universal Pictures, Columbia (Sony) or Paramount, and WB, Fox, and Disney are all part of huge conglomerates that make it hard to tell, but it's possible that in terms of movies and TV, Netflix is second only to Disney. If not, it's certainly close. Of course I'd also like you to justify your original statement, I don't think it's generally true, there's more to a business than just data, and I can't think of any business except maybe Google where scale and quality data was the defining factor. In almost every other case it was something else: network effects (FB, Twitter, Insta, WhatsApp), logistics (Amazon), or a different business model, normally freemium (Spotify, Slack, etc.). All of those companies use data for all sorts of different things, but they didn't take over/find success because of their data. >Lastly, their dataset isn’t as private as you might believe, either. Yes, their data is absolutely private, unless you think that the netflix prize from 2009, before they started streaming and 4-5 years before their first original, is at all relevant. [1]:https://www.vulture.com/2018/06/how-netflix-swallowed-tv-industry.html https://www.vulture.com/2018/06/how-netflix-swallowed-tv-ind...

joshuamorton 8y ago

>Again, your argument continues to validate one of the essential points I make: that these data sets are most useful for identifying existing media that “works” and then scaling/marketing it. I'm not sure how you're managing to miss this so completely, but no its explicitly not this. Are you familiar with how recommender systems work? Id suggest reading this article[1], which discusses how Netflix uses data to identify the fanbases for shows that they haven't yet launched. Yes, they use data to post-validate successful shows, but everyone does that, it's what Nielson has done for decades. Netflix is doing more: they're able to (with decent accuracy) predict what market a show will be popular with before launch, and they do this to market niche shows that would otherwise never be launched at all with a strong certainty that they'll find success. Your second paragraph invents evidence and certainty and data that you don't have. Frankly, it makes you look like you are unfamiliar with statistics and data analytics, which is fine, but then you shouldn't share strong opinions about how things you don't fully understand can't work. How do you know that they can't have statistical reliability? Thousands of data points is absolutely enough to have statistical certainty. Yes you can absolutely predict the popularity of a genre with a high degree of certainty before filming a single scene. How do you know that they don't have a high rate of success? (I refer you back to [1]: >She notes the mixed reviews for Lost in Space. “Do we care?” Not that much, it turns out. >Furthermore, Netflix picking themes, topics, etc as you suggest to drive successful production runs contrary to their most successful strategy so far, which is to cast a wide net by producing highly diverse content at low costs then scaling what works. I'd just like to make this really, exceptionally clear: they don't pick what content to produce by throwing darts. They pick the diverse ("niche") content in a very data driven way. Again, quoting [1]: > It [...] predicat[es] programming decisions on immense amounts of data about true viewing habits, not estimated ones. It has discovered ways to bundle enough niche viewers to make good business out of fare that used to play only to tiny markets. But I suggest reading the entire article, because it thoroughly dismantles the presumptions your making about how netflix does production. (Especially the part about "Money Heist"). >When a company has a combination of access to scale and quality data, they quickly outpace and overwhelm their competitors. Netflix hasn’t quite accomplished this, suggesting that their dataset isn’t as magical as you imply. Wait, who are their competitors that they aren't outpacing? Netflix absolutely dominates their market (streaming media), they defined it. Everyone else is playing catch up. If you are considering Netflix to be a competitor to WB or Disney, netflix had more revenue than Universal Pictures, Columbia (Sony) or Paramount, and WB, Fox, and Disney are all part of huge conglomerates that make it hard to tell, but it's possible that in terms of movies and TV, Netflix is second only to Disney. If not, it's certainly close. Of course I'd also like you to justify your original statement, I don't think it's generally true, there's more to a business than just data, and I can't think of any business except maybe Google where scale and quality data was the defining factor. In almost every other case it was something else: network effects (FB, Twitter, Insta, WhatsApp), logistics (Amazon), or a different business model, normally freemium (Spotify, Slack, etc.). All of those companies use data for all sorts of different things, but they didn't take over/find success because of their data. >Lastly, their dataset isn’t as private as you might believe, either. Yes, their data is absolutely private, unless you think that the netflix prize from 2009, before they started streaming and 4-5 years before their first original, is at all relevant. [1]:https://www.vulture.com/2018/06/how-netflix-swallowed-tv-industry.html https://www.vulture.com/2018/06/how-netflix-swallowed-tv-ind...