We use AppEco to simulate Apple’s iOS app ecosystem and investigate the effectiveness of common publicity strategies such as viral marketing, mass broadcast, targeted broadcast, and recurring broadcast, having your apps appear on the top apps chart, and having your apps appear on the new apps chart.

1. App
Epidemics
Modelling
the
Effects
of
Publicity
in
a
Mobile
App
Ecosystem
Soo
Ling
Lim
and
Peter
J.
Bentley,
University
College
London
AppEco
Model
Experiments
Developer
agent
App Store We
inves:gated
3
causal
factors
for
epidemics:
• Changes
in
host
exposure
Represents
a
solo
developer
or
a
team
of
• Changes
in
host
suscep:bility
developers
working
together
to
make
an
app
• Changes
in
app
infec:ousness
Uses
an
evolu:onary
strategy
to
build
apps
Developer App User
AHributes:
builds and downloaded by
• Development
dura:on
uploads
• Days
taken
• Probability
inac:ve
Results
App
artefact
Infec=ous
Non-­‐infec=ous
Developer agents Strategy
Excellent
App
Good
App
Average
App
Excellent
App
Good
App
Average
App
Built
by
developer
agent
Initialise ecosystem build and upload Update app store No
Exposure
6201.11
(1768.24)
694.58
(707.44)
0.26
(0.81)
3.32
(1.80)
0.77
(0.89)
0.26
(0.54)
apps
Mass
Exposure
5829.48
(1681.26)
935.53
(120.93)
5.19
(4.90)
4188.15
(657.08)
13.26
(24.53)
2.88
(1.69)
AHributes:
Targeted
Exposure
5889.71
(1721.91)
892.86
(319.30)
0.71
(1.27)
53.49
(409.15)
3.78
(1.62)
1.71
(0.71)
• Features
(10x10
grid)
loop for N timesteps Recurring
Exposure
5832.04
(1338.54)
913.66
(515.99)
0.71
(1.27)
6.22
(2.12)
1.51
(1.07)
0.36
(0.66)
• Number
of
downloads
Enhancing
Mode
of
Transmission
5818.77
(1847.14)
623.34
(708.93)
1.29
(0.81)
4.02
(1.88)
1.76
(0.84)
1.23
(0.49)
• Probability
infec:ous
User agents browse
through
Top
Apps
Chart
• Time
uploaded
to
app
store
Increase agent and download apps,
Exit Enhancing
Mode
of
Transmission
5840.05
(1610.12)
1020.07
(67.89)
172.48
(19.50)
4258.01
(517.44)
490.36
(58.39)
123.46
(17.69)
population and recommend
apps to friends through
New
Apps
Chart
User
agent
Total
downloads
averaged
over
100
runs
(standard
devia=on
in
brackets).
One
download
is
equivalent
to
10,000
real
downloads.
Has
preferences
(or
taste
informa:on)
that
determine
the
app
features
that
it
prefers
(a)
(b)
AHributes:
• Preferences
(10x10
grid)
• Days
between
browse
• Days
elapsed
• Number
of
friends
App
store
environment
Shop
front
for
users
to
browse
and
download
apps
Browsing
methods:
• New
Apps
Chart
• Top
Apps
Chart
!
• Keyword
Search
An
epidemic
curve
for
a
good
app
resul=ng
from
Mass
Exposure
in
an
example
run.
The
spread
of
the
excellent
infec=ous
app
through
the
user
network
using
Calibra=ng
AppEco
for
iOS
(a)
the
Mass
Exposure
strategy,
and
(b)
the
Enhancing
Mode
of
Transmission
through
New
Apps
Chart
strategy.
250" 250" 250"
Conclusions
Total&iOS&App&Users&(Million)&
Total&iOS&App&Users&(Million)&
200" 200" 200"
!
150" 150" 150"
• Enhancing
the
mode
of
transmission
through
New
Apps
Chart
results
in
the
highest
chance
of
an
epidemic.
100" Actual" 100" 100"
Actual" Actual" • The
more
suscep:ble
the
users
are
to
the
app
(i.e.,
the
more
users
like
the
app),
the
more
downloads
the
app
receives.
50"
Simulated"
50"
Simulated"
50"
Simulated" Spike
in
app
downloads
as
reported
However,
a
highly
desirable
app
may
s:ll
receive
no
downloads
just
because
users
are
unaware
of
it.
0" 0" 0"
by
Apple
to
the
second
author
for
• Infec:ous
apps
are
more
likely
to
trigger
an
epidemic
and
receive
more
downloads
than
non-­‐infec:ous
apps.
his
iStethoscope
Pro
app
aFer
a
• Non-­‐infec:ous
apps
can
be
downloaded
at
an
epidemic
propor:on,
but
users
must
be
very
suscep:ble
to
the
apps
and
Q4'08"
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Quarter&
! Quarter&
! Quarter&
! publicity
event.
the
apps
have
to
be
publicised,
best
by
the
New
Apps
Chart
strategy,
followed
by
Mass
Exposure
and
Targeted
Exposure.

2. www.appeco.co.uk