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If you were asked to think of one company that is defined by its use of algorithms, you might name Google.
And for good reason: the search giant's algorithms are not only at the heart of its success, but for many, they're the source of constant hope and fear as changes to them can literally make or break businesses.
But another prominent name on the consumer internet might also be a viable contender for the title 'algorithm company': Netflix.
In a post on its blog, Netflix revealed details around the recommendation algorithms it uses. Note the plural 'algorithms'. Netflix personalization science and engineering staff members Xavier Amatriain and Justin Basilico explain:
Personalization starts on our homepage, which consists of groups of videos arranged in horizontal rows. Each row has a title that conveys the intended meaningful connection between the videos in that group. Most of our personalization is based on the way we select rows, how we determine what items to include in them, and in what order to place those items.
Take as a first example the Top 10 row: this is our best guess at the ten titles you are most likely to enjoy. Of course, when we say “you”, we really mean everyone in your household. It is important to keep in mind that Netflix’ personalization is intended to handle a household that is likely to have different people with different tastes. That is why when you see your Top10, you are likely to discover items for dad, mom, the kids, or the whole family. Even for a single person household we want to appeal to your range of interests and moods. To achieve this, in many parts of our system we are not only optimizing for accuracy, but also for diversity.
That's just the beginning. Netflix's algorithms also factor in awareness, freshness, similarity, and social connections, amongst other things.
Why the sophistication? Miraculously, Netflix says that "75% of what people watch is from some sort of recommendation." From this perspective, it's not a stretch to say that Netflix's business today is driven by the ability of its recommendation algorithms to make good recommendations. Which sort of explains why the company created a million-dollar challenge with the goal of improving its recommendations by what appeared to be a small margin.
While most companies aren't as big as Netflix, many businesses large and small will increasingly find that algorithms are a crucial part of serving customers effectively. From helping customers find the right products, to reducing fraud, to delivering services more efficiently and cheaply, there are arguably few businesses that can't benefit from a Netflix-like approach.
The challenge of course, is increasingly not data. Thanks to the big data trend, more and more companies are collecting that. The challenge is performing the type of analysis that Netflix-like algorithms perform. With this in mind, the really important trend to watch may not be big data, but rather big analysis.