How will the mobile application space look like in the future? Which proven strategies from the past will still provide an edge? And which strategic levers should be considered to bring exorbitant profits?
To solve the mobile conundrum and peer into the future with the wisdom of the past, I’ve been collecting economic data from the most diverse data sources to estimate regressions(IRLS, LAD) of profit (quarterly and/or annual) on different software categories (desktop, web, mobile) and their features. In other words, the most obvious analysis that nobody has ever carried out.
The following are stylized initial results, omitting exact coefficients but showing their size and direction (* for statistically significant):
DESKTOP |
WEB |
MOBILE |
|
Total Addressable Market | + | ++ | + |
User Base Size | +(*) | +++(*) | ++(*) |
Development Sunk Costs | ++ | - | - |
Latency tolerant | ++ | - -(*) | - |
BUSINESS MODEL VARIABLES | |||
License fee | + | - | - — (*) |
Maintenance fees | +++(*) | - | ? |
Versioning | ++ | - | ? |
Bundling | ++ | ? | ? |
CPM/CPC | - | +++(*) | + |
Targeting Quality | ? | ++ | + |
Use Time per User | ? | ++(*) | ++(*) |
DEMAND SIDE ECONOMIES OF SCALE (NETWORK EFFECTS) | |||
Bandwagon effect | + | ++(*) | ? |
Standard setter | ++ | +++ | ? |
Linkage / interoperability | - | ++ | ? |
SWITCHING COSTS | |||
Data/file lock-in | ++ | + | ? |
Job/skill effects | ++(*) | ? | ? |
Learning/training effects | ++ | - | - |
Incumbency effect | ? | - | ++ |
R^2=0.66, sample size=352 (includes most important and known programs per category) |
Focusing into the higher size and statistically significant variables, the data reveals the different nature of each software category:
- Desktop applications: the most profitable strategy is to develop broadly used programs with low initial price, but higher maintenance fees and a significant impact on the labor market. Don’t make programs, revolutionize professions.
- Web: very high scale ad-monetized applications with major network effects. The result of the open nature of the web with its hyper-linking structure across domains and the absence of micropayments.
- Mobile software is a yet-to-be-determined mixture of desktop and web applications. This category is like desktop software, in that it has the same technical architecture, but its evolution resembles more closely that of the web due to the incumbency effects from web companies and lack of switching costs and traditional network effects.
More insights in future posts from this and other data sources.
Data sources: Yahoo Finance, Crunchbase, Wakoopa, RescueTime, Flurry, Admob, Distimo, Alexa, Quantcast, Compete, others.
Hi David,
Nice post by the way. The results are really suggestive but what leaves me wondering is that there is a million of things that can be going on at the same time.
Also, why did you ended up doing ILRS/LAD instead of more straightforward methods?
Cheers,
Sergio
Absolutely, this is just a model, and all models are toylike simplifications of reality.
I prefer to use ILRS/LAD regressions to remove the effects of heteroscedasticity, correlation and outliers.
Thanks for your comments!