How to market your app

243 views

Published on

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.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
243
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

How to market your app

  1. 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   Q408" Q109" Q209" Q309" Q409" Q110" Q210" Q310" Q410" Q111" Q211" Q311" Q408" Q109" Q209" Q309" Q409" Q110" Q210" Q310" Q410" Q111" Q211" Q311" Q408" Q109" Q209" Q309" Q409" Q110" Q210" Q310" Q410" Q111" Q211" Q311" 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. 2. www.appeco.co.uk  

×