Analysis of the number of mobile app downloads
Task: determine the number of downloads that our application will need to get into the top Google Play.There is no material of their own yet, but I sincerely wish that not only readers shared and exchanged experiences.
The hubroman tells about how he tried to determine the number of downloads of mobile applications in the top Google Play for Russia “from the outside”.How to solve:
1) I was looking for thematic articles and reports from various conferences (a lot about promotion methods, starting with internal factors (description in the store, keywords, name, logo) and ending with the purchase of traffic (data on click conversion in registration in the best traditions are not provided).
From what was found, the data on the top App Store two years ago were more or less relevant to the topic (in order to take the top 13 among free games, 200-300 downloads per day were enough).2) Started looking for tools for analysis.
Found:a) Xyologic and not only application search, but also reports.
From the reports, the most interesting is Top New Apps, according to them you can judge what approximate number of downloads is required to get into the top of new free apps. Also, having broken through the names of applications in search engines for the reporting period, you can track on which portals there were publications.I was most interested in the situation when there are no Russian-language publications, but there is an application in the top, I put forward two hypotheses initially:
— The Russian Google Play was simply “covered” due to a large number of downloads around the world.
— The game /application was advertised by a major developer in his other applications.
Then, after a deeper analysis, I came to the conclusion that the same reasons may be:
— The fact that the application consists of one of the application networks.
— Massive purchase of traffic from mobile advertising networks.
b) Appdata here, unfortunately, it is not always possible to find an application.
The output data is the approximate number of downloads per day, the position of the application in the top, the approximate number of users. Not all data is always given out. From here I took the average number of downloads over the last 5 days. It is especially interesting to investigate the relationship between the regional top of applications and this value, unfortunately, there are only 2 matches from a sample of 50 applications and the result is not representative.c) Androidlib has very little data, the total number of downloads for all time is estimated, there is an infographic according to the application estimates.
3) Compiled a comparison table with competitors (top applications of 2 categories to choose where to place).
The table shows the number of google+1, data on downloads from each of the services (Xyologic — for a month, Appdata — average for 5 days, Androidlib — for all time). Now you can create a correlation function.Total:
0) The most important thing is that we got an approximate figure for passage to various GP tops.
1) According to Xyologic reports, applying the approximate correction coefficients described in the article about Xyologic, we get the number of downloads that we need to focus on in order to get into the top of new Google Play applications.
2) I got a dynamic picture for some of the applications (which appdata finds), as the position in the top grows or falls depending on the number of downloads.
3) Compiled fairly complete comparison tables with competitors in the categories.
*Note 1. Along the way, I solved the problem of comparing with competitors and highlighting potential competitive advantages.
*Note 2.
Since I did all this in August, I collected everything manually, now I have done parsers in Python for part of the needs.If you are interested, I can provide data on correlation functions, and write about how we made a marketing plan for our application.
