Since the launch of powerslyde in early March of this year, we have spent a great deal of time understanding the data that we are collecting.
Our focus has not been to drive installs, but rather to learn about the activities of our users and learn from the data. We began the process of applying analytics and gathering insights surrounding the entire business and have seen some amazing things.
We began to look at the app discovery problem as a three-sided issue as pointed out in an article by Ouriel Ohayon. We then realized because of the scope of the problem, we would be required think differently and develop a new solution to address app discovery.
Today I walked by a billboard for the iPad. It stated “300,000 apps for everything you love”. As much as I admire Apple for their products and their vision as a consumer of apps, I was perplexed to find there were that many apps for the things I love. I actually don’t have the time to browse through so many apps, and would guess the majority of consumers have similar constraints. Recent reports indicate that over 50% of the time, people rely on information about the apps they download from trusted friends and family.
My co-founder has compared the app stores of today with Home Depot. They have everything you could possibly want, but finding it is another matter and finding someone to help you is even, well, non-existent. Did the app stores create the problem, or are they the result of the problem? I don’t believe that either is the case. The problem came about due to the rapid rise of the device technology and the developer response to the opportunity it created.
When you think of app discovery and who has the most to gain, or lose, the answer becomes clear when you begin to consider the developer side of the equation. It used to be that a developer could be relatively assured of a large number of downloads in the app stores, just by being in the app stores. Today that is not the case. The discovery problem has only become more difficult with millions of apps across multiple app stores.
When we designed powerslyde, we wanted to address the app from the consumer’s side of the problem. We reasoned that if we built it with the consumer in mind, it would also be a win for developers.
Here are a few of our early insights:
- We have an active user base, 45% of our users are active on a monthly basis
- Our active users recommend over 11 apps per month
- The install rate from those recommendations is 77%
- One of our original assumptions was that our users would download paid apps at the same rate as other consumers, which had been 10.89%. In fact, powerslyde users are downloading only 3.6% paid apps.
The interesting thing about this last point is that in the app discovery space there are a multitude of apps, yet no one has solved the problem or has emerged as a true leader.
Four of the top five apps are centered on free apps. That seems to validate the findings that powerlsyde users do not like to pay for apps they can get for free. We are learning that people like to save time and money, and it is important to leverage their friends and family.
A couple of positive trends have emerged which should be great news for developers. First, when it comes to recommending apps to other powerslyde users, the less familiar the app the more likely the user is to download it. Second, when a powerslyde user sees an app that had been popular or talked about in the past, the action of “pulling” the app from another powerslyde users profile increases the install rate by multiples of the recommendation rate due to the recognition of the app, and the ownership by a trusted friend.
At the heart of this is what we like to call FOMO or The Fear Of Missing Out.
We are also learning about the other apps that powerslyde users have on their mobile devices that leads to a completely seperate topic diving into the amazing data and analytics. Because we know what apps reside on a users device, we can provide additional analytics and insights with regard to those apps.
The following are examples of the types of analysis and insight that developers are telling us they find useful:
- High quality behavioral targeting
- Apps that appear most often with other apps
- Apps that a developer should target ad spend to achieve improved results
- Apps that appear most often with others, which leads to Positive and Negative lift
As you might guess, there are many more correlations to be made, far too numerous to list.
And so we have the rise of the 'Appfluencer.' The person, who is the expert, and more importantly, your expert in helping to sort through the clutter to find the perfect app for you.