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Abigayle Laurenitis
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The emergence of Netflix and similar OTT (Over-the-top) services have often been described as the virtual "DVD collection in the Sky". The concept of "Pay a monthly fee and watch as much as you would like" is strong, but of course no stronger than the actual strength of the movie and series content offered.
Just like a dinner buffet, the concept works best if there is plenty of choice and something for everybody. So eating as much as you would like is not in itself really that interesting, if choice is limited. Likewise with Netflix, the concept of "watch till you drop" is only appealing is there is variety.
If you - like me - are old enough to have a collection of DVDs (or even VHS tapes), I am pretty sure your first thought would be, that "its definitely much much more than my DVD collection". And while I have friends who amassed massive DVD (and torrent based collections, if we are to be honest..) movie collections, you are probably right.
The correct answer - for the Danish version of Netflix - is 2.065. And if you think the DK version is a severely watered down version of the main US, well the numbers for the US version is 5.558. (source: www.allflicks.net).
Well, first I would say it is always interesting when consumers perception of an offering does not match the reality. But more importantly, I believe it shows a potential weakness of what is know as the "SVOD business model", ie. a monthly subscription model, in which you can watch unlimited movies. On contrast to metered concepts or pay-per-view (TVOD).
When Netflix originally launched, it was easier for them to get huge back-catalogs of content for a reasonable price, allowing them to launch a massive collection of content for a low monthly price. This is changing (Starz 2011, Viacom 2013 and Epix 2015 are all examples of previous long-term massive content that was not renew). Netflix also did not produce its own original content initially. This is also changing. Clearly the industry is now much more aware of the value of content, and 5-year all-out catalog deals are history. And replacing this with original content is expensive - and takes time.
What people want is quality over quantity. The SVOD business model does not allow for tiering of content, it is basically at its core a buffet offering - where you do not get to limit the customers to "Only 1 meatball allowed per plate". You need to take into account the guy who piles up 25 meatballs on his plate and does not touch the salad bar.
As content costs increases, I believe we will see a trend towards and even slimmer collection, with a focus to original and premium content from Netflix. While Netflix might have a chance to make such a transition based on the sheer size of its success and operations, I would challenge you to come up with an example of a buffet restaurant that have changed into a premium restaurant and still maintained an "all you can eat" concept. Simply put, when quality becomes the primary offering, there is no other way than ala-carte. Meaning we will need to pay to what we watch, and abandon the idea of having it both ways.
In 1999, Netflix had 2,600 DVDs to choose from but intended to grow its library to 100,000 titles. To make it easier for members to find movies, Netflix developed a personalized merchandising system. Initially, it focused on DVDs, but in 2007, Netflix launched streaming, which used the same personalization system.
Search. There was little investment in search in the early days as Netflix assumed members searched for expensive new release DVDs. But the team discovered that the titles members chose included lots of older, less expensive, long-tail titles, so they ramped up investment in search.
The high-level engagement metric was retention. However, it takes years to affect this metric. So Netflix had a more sensitive, short-term proxy metric: The percentage of members who rated at least 50 movies during their first two months with the service.
The theory: members would rate lots of movies to get better recommendations. Many ratings from a member signaled they appreciated the personalized recommendations they received in return for their ratings.
It took Netflix more than a decade to demonstrate that a personalized experience improved retention. But consistent growth in this proxy convinced the company to keep doubling down on personalization.
Over time, Netflix got better at suggesting similar titles for members to add to their queue, which drove this source from ten to fifteen percent of total queue adds. The QUACL was a great test environment for algorithm testing. In fact, Netflix executed its first machine learning tests within the QUACL.
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Gib\u2019s note: Welcome to the 200 new members who joined since my last essay! After five months, we\u2019re 5,400 strong. In each essay, I draw from my experience as both VP of Product at Netflix and Chief Product Officer at Chegg to help product leaders build their careers. This is essay #50.
Netflix began as a DVD-by-mail startup, following the invention of the DVD player in 1996. In 1998 Netflix launched its website with less than 1,000 DVDs. Here\u2019s what the site looked like its first few years:
In twenty years, Netflix has gone from members choosing 2% of the movies the merchandising system suggests to 80% today. And the system also saves members\u2019 time. In the early days, a member would explore hundreds of titles before finding something they liked. Today most members look at forty choices before they hit the \u201Cplay\u201D button. Twenty years from now, Netflix hopes to play that one choice that\u2019s \u201Cjust right\u201D with no browsing required.
Below, I detail Netflix\u2019s progress from the launch of Cinematch in 2000 to 2006. It\u2019s a messy journey, with an evolving personalization strategy propelled by Netflix\u2019s ability to execute high-cadence experiments using its home-grown A/B test system.
Netflix introduced a personalized movie recommendation system, using member ratings to predict how much a member would like a movie. The algorithm was called Cinematch, and it\u2019s a collaborative filtering algorithm.
Netflix created a five-star rating system and eventually collected billions of ratings from its members. Netflix experimented with multiple \u201Cstar bars,\u201D at times stacking the stars to indicate expected rating, average rating, and friends\u2019 rating. (It was a mess.)
Dynamic store. This algorithm indicated if the DVD was available to merchandise. Late in the DVD era, the algorithm also determined if a DVD was available in a member\u2019s local hub. (By 2008, Netflix only merchandised titles that were available locally to increase the likelihood of next-day DVD delivery.)
Recognizing multiple family members used a shared account, Netflix launched \u201CProfiles.\u201D This feature enabled each family member to generate its own movie list. It was a highly requested feature, but only two percent of members used it despite aggressive promotion. It was a lot of work to manage an ordered list of DVDs, and only one person in each household was willing to do this.
Given the low adoption, Netflix announced its plan to kill the feature but capitulated in the face of member backlash. A small set of users cared deeply about the feature\u2014 they were afraid that losing Profiles would ruin their marriages! As an example of \u201Call members are not created equal,\u201D half the Netflix board used the feature.
The hypothesis: if you create a network of friends within Netflix, they\u2019ll suggest great movie ideas to each other and won\u2019t quit the service because they don\u2019t want to leave their friends. At launch, 2% of Netflix members connected with at least one friend, but this metric never moved beyond 5%.
Create algorithms and presentation layer tactics to connect members with movies they\u2019ll love. Use the explicit/implicit taste data, along with lots of data about movies and TV shows (ratings, genres, synopsis, lead actors, directors, etc.), to create algorithms that connect members with titles. Also, create a UI that provides visual support for personalized choices.
Why the 2011 dip in the metric? By this time, most members streamed movies, and Netflix had a strong implicit signal about member taste. Once you hit the \u201CPlay\u201D button, you either kept watching or stopped. Netflix no longer needed to collect as many star ratings.
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