An correlative analysis on

1. ABOUT
(MY) SOUNDCLOUD

2. ME
2

3. METHODOLOGY
Name Followers Following Sounds Comments Likes Location
bestworks 7400 338 59 33 413 Berlin
Andy Hughes 2063 1739 173 8 1
Dallas,
Texas
BRENMAR 31714 174 74 561 6 NY
&ME 33911 96 57 91 96 Berlin
Bebetta 16046 178 53 196 9 Frankfurt
Aaron Static 8921 319 47 78 39 Melbourne
atish 8497 31 41 208 45
San
Francisco
Animal Trainer 2342 67 40 66 40 Zurich
// HÔHME
david hôhme
2390 31 31 321 0 Brooklyn
bons vivants 571 292 31 71 558 Bern
alexstatic S.A.S 414 776 26 137 234 Berkshire
#root.access 7799 1759 24 700 747 frankfurt
A Professional
Dreamer
4548 1249 19 1704 556
-
-
Christoph
Woerner
5762 123 17 1506 1377
18k-yatz 251 1412 17 29 24 Tel Aviv
arkadiusz. 3927 252 16 4431 1672
Guadalajar
a
Die
Vogelperspektive
2995 78 16 133 82 Bremen
Barker &
Baumecker
5612 62 16 12 32 Berlin
ANIÈ 7282 1656 15 275 187 Leipzig
Ben van Flijken 1634 904 14 2556 1260 Munich
I conducted both a quantitative and qualitative analysis of
all my followers (116) and a sample (1/5)of those whom I
follow. I collected information on follower and following
counts, likes, comments, sounds posted, location and
whether they were a Pro member. I collected this data
manually from each users page to get a better qualitative
understanding of each user. By reviewing each user
individually, I was able to create a taxonomy of user types.
!
I classified users as either a listener, artist or organization. I
also assigned subclassifications within each category.
Artists are labeled as ‘Hobbyist’, ‘Aspiring,’ ‘Established
underground,’ or ‘Commcercial.’ Listeners are labeled as
‘Casual’ or ‘Power.’ Organizations are sub labeled as
‘Record Labels,’ ‘Magazines,’ ‘Radio stations,’
3

4. LISTENERS
POWER & CASUAL
!
★ Listeners have the highest median following count (207).
ARTISTS
HOBBYIST, ASPIRING, ESTABLISHED UNDERGROUND, & COMMERCIAL
!
★Artists, on average post the most the most amount of comments (192).
ORGANIZATIONS
RADIO STATIONS, EVENT PRODUCERS, RECORD LABELS, MAGAZINES &
COMMUNITIES
!
★ Organizations have the lowest following:follower ratio (0.69) compared to
artists and listeners.
★
IDENTIFIED USER TYPES
4

5. SUBTYPE HOBBYIST ASPIRING
ESTABLISHED
INDEPENDENT
COMMERCIAL
EXAMPLE
USE CASE
Find new sounds & share
it with friends
Share their original
sounds with friends and
find new music
Promote their releases
and connect with fans
Promote and tease
upcoming releases
FREQUENCY More than 4 hours a day Everyday Once a week Once a month
MEDIAN
FOLLOWER
COUNT
202.5 2,995 37,357 301,156
ARTISTS
5

6. SUBTYPE CASUAL POWER
EXAMPLE
USE CASE Listen to sounds sent by and reblogged by friends
Find new sounds & share it with friends, is a
tastemaker.
FREQUENCY A couple times a week 4 hours a day
MEDIAN
FOLLOWER
COUNT
15.5 125.5
LISTENERS
6

7. SUBTYPE RECORD LABEL
EVENT
PRODUCER
RADIO STATION COMMUNITY MAGAZINE
EXAMPLE
USE CASE
Promote artists and
new releases
Promote upcoming
events
Post new sounds
and mixes of new
artists
Promote artists and
new releases
Promote sounds
related to their
editorial
FREQUENCY
A couple times a
week
Once a week Once a day Once a day An hour a day
MEDIAN
FOLLOWER
COUNT
9,219
ORGANIZATIONS
7

8. Power
45
!
Casual
29
!
Aspiring
29
Hobbyist
29
Radio
2
Event
1
Record
label
1
Community
2
Artists
Listeners
Organizations
Established Underground 2
Top Countries Top Cities
Germany Berlin
US New York
UK Frankfurt
Netherlands Hamburg
Switzerland Chicago
MY FOLLOWERS
8

9. USERS I FOLLOW
Established underground
39
!
Casual
8
Power
5
Record label
20
Magazine
9
Event
9
Radio
1
Agency
1
Aspiring
21
!
Hobbyist
8
Listeners
Artists
Organizations
75%
PRO USERS
9

10. User Type
Correlation
(r)
Listeners 0.67
Casual 0.63
Power 0.80
Artists -0.23
Hobbyist 0.61
Aspiring -0.22
Established underground -0.26
Commercial -0.26
Organizations -0.26
WHERE DO I FIT IN?
The figure on the right shows the correlation between my
Soundcloud profile and user types and subtypes within my
network. I compared the median follower, following, post, like &
comment counts of listeners, artists and organizations. I also
compared the counts of sub-types within listener and artist
groups. I chose no to calculate the means for the sub-types of
organizations because of a lack of a data points. I compared the
data sets of each user type to the follower, following, post, like
and comment counts of my profile.
!
A correlation coefficient is a measure of correlation between
two variables or data yielding a value between -1 and 1. A value
closer to -1 means the datasets are highly uncorrelated while a
value closer to 1 implies high correlation. I have the highest
correlation to other listeners specifically Power listeners.
10

11. Exclusively
posts
Exclusively
plays
Casual
Listener
Power
Listener
Hobbyist
Artist
Aspiring
ProducerOrganization
Commercial
artist
Established
underground ME
R = 0.63
R = 0.81
R = -0.58
11
R = -0.22
R = 0.61R = -0.26
R = -0.26