Over the past few months, I have been very busy developing AppEco, a C++ simulation of mobile app ecosystems. I had a lot of fun with AppEco! It enables me to ask different kinds of “what if” questions about mobile app ecosystems. For example, with so many developers trying out different strategies to increase their downloads, I wanted to know if an innovative developer would receive more downloads compared to a copycat developer.
My collaborator, Peter Bentley (who created the No. 1 best-selling app iStethoscope Pro), and I used AppEco to simulate for popular developer strategies: Innovators, Milkers, Optimisers, and Copycats, and evaluate their performance in terms of number of downloads, app diversity, and adoption rate. We found that Innovators produce diverse apps, but they are hit or miss – some apps will be popular, some will not. Milkers may dwell on average or bad apps as they churn out new variations of the same idea. Optimisers produce diverse apps and tailor their development towards users’ needs. One interesting we did find is that Copycats receive the most downloads on average, but it can only work when there are enough other strategies to copy from. In addition, Copycats can only exist in a minority, otherwise the app store will have many duplicated apps and the ecosystem will suffer. Our paper “How to be a Successful App Developer” has been accepted at GECCO’12.
Copycats are the minority when developers choose their own strategies
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AppEco has a lot of potential. In a separate study, we have also used AppEco to study the effects of publicity on app downloads. We simulated different apps, ranging from fabulous to terrible, and applied different publicity strategies to promote the apps. Appearing on the New and Noteworthy Chart is most likely to guarantee downloads. Our simulation shows that with so many apps in the app store, a fabulous app that is not publicised may go unnoticed and consequently receive no downloads at all. We also found that the spike in app downloads after a publicity event resembles a typical epidemic curve. For all the juicy bits, read our paper “App Epidemics: Modelling the Effects of Publicity in a Mobile App Ecosystem.”
The spread of a highly infectious app through the network of users after the app is broadcasted using mass media.