Last week we shared some high level insights we have gleaned from the data on over 1 million apps in the Apple iTunes App Store that we actively track. This week, we wanted to share how we analyze the data at an application level to help our customers discover emerging apps and publishers.
To achieve our goal of surfacing the most interesting apps and publishers, we have developed various algorithms that analyze the App Store data continuously and alert us when an app, for example, shows significant upward or downward momentum in its ranking. For example, one of the algorithms looks at the historical mean rank of an app within its primary genre and alerts us when the rank falls out of the range of a few standard deviations of this mean. To understand the rationale behind the algorithm, we need to understand the dynamics of the rankings. The following graph shows the distribution density of absolute daily changes in rank for all apps on the App Store.
The mean (91.93) is indicated by the grey horizontal line whereas the red lines indicate the lower (12.77) and upper limits (171.10) of one standard deviation away from the mean. This shows that sudden movements in rank beyond a few standard deviations of the mean are relatively rare and might indicate an interesting development. Therefore, the algorithm alerts us when this criteria is met for any app. One such recent alert produced recently by this algorithm was for SpotHero, a mobile app that helps users find and pay for parking spots in large metros. Here’s what its historical rank looks like (lower is better) –
The most recent data point on the graph above is indicated in blue. The graph also shows the historical mean, median and the lower threshold of 2 standard deviations from the mean. As is clear from the graph, SpotHero’s app has jumped significantly in rankings recently. This might indicate a successfully executed marketing campaign, a new improved version of the app or improving product traction among other positive signals.
Here are 2 more apps that also fit the bill according to the same algorithm – SilverCar,
And, Droplr –
Both these apps show significant upward momentum in their respective genres. Combined with the fact that the companies behind these apps are all at an early stage of funding (Series A or earlier), these might represent potential investment opportunities. As we integrate this mobile data into the product, our customers will be notified of these alerts as they happen. Below are some details on 5 apps that were recently surfaced by our algorithms:
This was just an example of how one of our algorithms helps surface interesting apps. We have various other algorithms that look at other trends such as time spent in the Top 100, movement relative to competitors and consistent improvement over long periods of time. Over the next few weeks, we’ll shed more light on how some of our others algorithms work as well as other trends in the mobile data that we’ve observed.
Subscribers with access to CB Insider Research can login to get access to a list of mobile apps that our algorithms have identified significant rankings changes for. For investment and M&A deal sourcing and understanding mobile trends, this is very valuable. Eventually, this data will be integrated into CBI, but in the meantime, we wanted to highlight interesting movements for our existing customers.
This report was created with data from CB Insights’ emerging technology insights platform, which offers clarity into emerging tech and new business strategies through tools like:
- Earnings Transcripts Search Engine & Analytics to get an information edge on competitors’ and incumbents’ strategies
- Patent Analytics to see where innovation is happening next
- Company Mosaic Scores to evaluate startup health, based on our National Science Foundation-backed algorithm
- Business Relationships to quickly see a company’s competitors, partners, and more
- Market Sizing Tools to visualize market growth and spot the next big opportunity