Netflix
Netflix’s recommendation heavy algorithm serves the primary function of maximizing users’ watch time. By indexing heavily on previous user tastes/tendencies and what’s currently trending, Netflix aims to make a successful recommendation that draws in the user’s attention. The more accurate Netflix’s suggestions are, the less friction users receive in the action of choosing a show. Decision fatigue can discourage users from watching and resuls in churn.
Youtube
Youtube’s search and algorithm offers the perfect environment for ad placement. Youtube’s ads are smart; they’re unobstrusive because they relate to the user’s search, becoming a natural part of the results rather than a pesky interruption. Because search is such an integral aspect of browsing (it’s Google’s flagship product!), it allows Youtube to use search queries and user data to offer up the ads through which Google makes ~80% of its revenue. In the screenshot below, we can see a simple search for “stanford” yields 2 related ad results amongst 4 total results.
Airbnb
Airbnb’s browse and search user flow is heavily based on filters. Airbnb’s ultimate North Star is to maximize number of bookings made, and bookings are only made when the customer finds the perfect space that suits their needs. User needs vary widely from situation to situation, so a recommendation-based algorithm would be highly ineffective, as it doesn’t take user input into account. On the other hand, a search-bar is too open ended. Having the flow of filters (from choice of day, to location, to number of residents) gives users the power to suit their own logistics and find that perfect option.
