Spotify has hinted that metrics like the listener's heart rate, speed of motion and sleep patterns could someday be used to improve recommendation tools, as smartphone technology improves.
In an exclusive interview with TechRadar, the streaming giant's product manager for discovery and recommendations, Donovan Sung, said deeper integration with mobile devices could better inform what its algorithms serve up.
When asked about the possibility of creating the perfect recommendations tool, Sung said: "The more the engine knows about the user, the easier it is for it to make interesting recommendations.
"Maybe with motion sensors in phones, we can start guessing things whether users are running, biking or driving? Maybe it the phone has a temperature sensor, or a heart rate sensor, we could guess whether the user is tense..."
If paired with a heart rate monitor, the company could possibly provide workout playlists with limited user input or could provide a driving playlist (preferably with plenty of The Eagles' hits) if it detects users are moving at higher speeds.
Sees you when you're sleeping
He also hinted the company would benefit from integrations with other apps and services, such as those that analyse the user's sleeping patterns, for example the popular Sleep Cycle app.
Sung added: "Maybe it connects to some other services? For example if we know more about your sleeping habits through mobile tracking apps, this could help.
"We would know what time you're likely to go to sleep or what time you wake up and recommendations could be tailored [to the time of day]."
Of course, this seems to be mostly conjecture regarding the future of the company's already-exhaustive editorial, algorithmic and social recommendations tools. However it's interesting that Spotify is at least thinking of taking recommendations in this direction.