How social sensors could solve the eternal problem of abundance

Michael is
Tec. Engineer & Master of Arts

Last.fm Cofounder

RjDj Founder

and talk about social
sensors

Wednesday, 16 March 2011


we have tons of songs in our pockets.


movies are on torrent before they are released


news, maps, recommendations, videos

Chris Anderson, “Free! Why $0.00 Is the Future of Business”


all for free

What if our real world
would be like our digital world?


Pieter Bruegel the Elder's „Luilekkerland“ ("The Land of Cockaigne"), painted in 1567

This is pretty much the digital world of today mapped to the real world.

Lubberland was the Big Rock Candy
Mountain of medieval Europe, where
the living was easy and the land flowed
with milk and honey. This mythical
country had houses of barley sugar,
roofs of cakes, rivers of wine, and
streets paved with pastry; buttered
larks (a delicacy of the period) fell
instead of rain; roast geese passed
slowly down the streets, begging to be
eaten; even better, shops provided
goods without asking for payment.



in lubblerland:
-> no need for transportation


in lubblerland:
-> all free


in lubblerland:
-> advertising is no topic because goods are just there!


in lubblerland:
-> branding is no topic

Think about it. The Lubberland turns everything upside down. Think about the impact only for the advertising
industry....

The Lubberland Problem


Besides health issues, the people of lubberland have one big problem


so where is the problem? what do you think is the problem of the guy with his eyes open?

What’s next?


the biggest consumer problem in lubblerland is what’s the next thing to consume.


he could have anything, the more he can get the bigger his problem.. in the same way, whats
next is the main problem in many of todays digital markets.

The Lubberland Problem is
key in understanding today’s
digital markets


The social approach
in solving the Lubberland Problem



Around 2000 i started a project called last.fm. The topic of last.fm was to create a music platform for the post
napster generation. My assumption was that the more music becomes available and the easier it is to get new
music the less people know what they actually want next.


So we started to record the listening behavior of users which helped them to get their online music collection.
Last.fm received over one million tracks per day from it’s users.


Based on the online music collections of the users, we were able to generate stats and connect users with similar
music and play new music based on your stats.

We recognized that key to our project was not any more the mp3 ﬁle itself but the music taste of the user.
Because we knew the music taste, we could answer the question of whats next and fulﬁll our goal.


This is a plot of the ﬁrst music map that we were able to print. It shows the similarity of artists based on user
listening behavior. The three clusters are similar artists or music genres. Between the big centered cluster and the
right bottom cluster you can see a band which is called Radiohead. In 2003 when this plot was made, Radiohead
was THE top selling band and they had huge commercial success. According to the similarity plot this is down to
the fact that they are attracting fans from two genres. Their market is bigger than other artists market.

Last.fm plays the right kind of music to
the right ears at the right time.


Consumer ﬁnd their products:

Products ﬁnd their consumer:


On the Last.fm portal, which one of the above do you think is true?




....This is very much like in the land of plenty.

The social SENSORS approach
to solve the Lubberland Problem


SMART MUSIC FOR SMART DEVICES


RjDj is smart music for smart devices.

Smart Music Players
1958
1998 2008


The iPhone is the walkman of the 21st century. But compared to the traditional walkman, it is much smarter. It has
a processor, is networked, has accelerometer, compass, microphone, touchscreen, gyroscope etc.

Hands on
http://www.youtube.com/watch?v=hvWFv954IBU


so what can you do with this. Hands on. Example of touch based input.

Move on
http://www.youtube.com/watch?v=tZEOe7B5Igg


Example of accelerometer based input.

Mic on
http://www.youtube.com/watch?v=a5RlH1bEJTU


example of microphone based input.

hands, move, mic,
weather, time,
moon, location,
networking,
sleeping, quiet

on


but what happens when you combine all of this and extend it with networked and social data?

Inception The App
Christopher Nolan
Hans Zimmer
RjDj


the ﬁrst app we applied this formula to was inception the app, which we did together with Christopher Nolan and
Hans Zimmer.

http://vimeo.com/17519897


video of inception the app

Social Sensors
walking
traveling

friends

sleeping

sunny
airport


the inception app is using a lot of data about the user’s context like the user’s location, how the weather is, what
is the time of day, is he traveling, is he moving, is it quiet...


based on the sensory input dreams or augmented music pieces are unlocked. Some dreams can only be unlocked
once a month, on fullmoon or when you are in africa or at an airport or when someone uses the app next to you
or when you are at a quiet place. Musical pieces become scarce again and very valuable if you managed to unlock
them through activities in your real life. It is almost like going to a concert. In concerts, you have to be at a
speciﬁc place in a speciﬁc time to become part of a great musical experience.

The right kind of music to the right ears
at the right time.


in a similar way to last.fm inception the app is playing the right kind of music to the right ears at the right time.




and again the product ﬁnds the consumer and not vice versa.

Launch Pad Hollywood

vs.

Warner Inception App:

• same release date
• same movie
• same feature and promo
• very poor performance


Now before you go and credit the app’s download numbers to the fame factor: The Inception movie was a perfect
launch pad for the app. Warner launched their own inception app at the same time...But the warner app had a very
poor performance.

3
30
300
3000
30000
300000
3000000
3
3


we released inception the app just before christmas 2010. One month after christmas the app was downloaded 3
million times.

34 Minutes
Average Time in App per Session

These 11% are on average
4.5 hours in the app!

11%
0 - 10 sec

10 - 30 min

6%
39%

15%

3 - 10 min

15% 7%
7%
11 - 30 sec

1 - 3 min
0.5 - 1 min


average time in the app per session is and an incredible 34 minutes.

94 Years
Total listening time in ﬁrst month

40

30

20

10

0
Static Reverie Sleep Quiet Sunny Action Fullmoon Travelling Overture Limbo Africa

“Honestly this app is incredible and ridiculously addictive. I have unlocked
them all but the ones for update 3 and the Africa dream. Do we really have to go
to Africa to unlock this dream? Thanks for an amazing experience.”

email from user Jake, 21 January 2011


in the ﬁrst month people listened 94 years of uninterrupted music

“... Augmented sound design,
underrated. Maybe someday
silent augmentation will be
classified like silent cinema.”
jDj
g ab out R
ce S terlin
Bru


Talk Value


What’s next?


Augmented Music + Social Sensors

★ Entertainment / Gaming
★ Infosystems / Ads
★ Health
★ Extended platforms (Car, House...)


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