July 12, 2019
By Enric Pedró
One of our main objectives, as an App Store Optimization (ASO) agency, is to draw conclusions and predictions on any given app’s traffic behavior. Throughout Lab Cave’s history in the mobile industry, we have been working on several data analysis projects with the aim of understanding and drawing conclusions on the amount of weight that User Acquisition (UA) has on Organic Traffic.
User Acquisition (UA) refers to the act of gaining new users through paid traffic channels on both apps stores; Google Play and iOS. Each store classifies its paid traffic in different ways.
It’s important to emphasize that, despite both stores naming their non-organic channels with different names, they have one thing in common, these channels only track traffic that has not been generated organically. This is highly relevant since here at Lab Cave we believe that an install generated by organic traffic has a higher value and quality than an install made through a paid channel. This is one of the reasons why we focus on generating organic traffic and why it’s so important to understand the impact that external factors, such as UA (paid traffic), have on the visibility of an app.
In the mobile industry there is a common belief that paid and organic traffic have a positive relationship — that is to say that the more paid traffic an app has, the more organic traffic it will receive. Although this can be the case for some apps, over the years we have come upon different apps where this correlation was non-existent (see case 2). Nonetheless, the real issue is not whether UA has an impact on organic traffic but to be able to numerically measure this impact and to have the means to apply this measurement to a range of apps.
Measuring the number of organic installs that come as a result of paid traffic is commonly known as K-factor. This term is used to estimate the exact impact that Organic installs have on Paid installs. Nonetheless, as mentioned before, prior to measuring the impact of UA on Organic traffic, we need to identify if a direct correlation exists between these two channels.
In order to explore the presumptions regarding the correlation between UA and organic traffic, a number of in-depth analyses were conducted by our Data Science Team. A sampling of data from three distinct apps with high traffic was analyzed over a period of 7 months, where a linear correlation study (Pearson correlation coefficient) between UA and organic installs within the time series was conducted. The Pearson correlation applies values from -1 to 1, being negative values a negative linear correlation. In this study, we have considered values from 1 (positive correlation) to 0 (negative correlation). Values that are less than 0.5 are considered low correlation.
As stated in our previous article published on PocketGamer.biz, after evaluating our sample of apps, the results showed that only one of the three apps analyzed showed a positive correlation between UA and organic traffic in iOS, while the other two displayed a non-relationship between these two channels.
As the graph below shows, we encountered a significant correlation between paid traffic and organic browse traffic (traffic coming from users who are navigating within the store). Even though we weren’t able to draw conclusions by merely looking at the graph, the cross-correlation analysis registered a positive relationship between these two channels, resulting in a Pearson coefficient of 0.7. This correlation was not found in other Apps in either iOS or Google Play.
Considering that browse and search traffic are both classified as organic traffic, and even though the results between browse and paid traffic showed a positive correlation, there was significant discordance when analyzing the correlation between search and paid traffic (see Graph 2). As an ASO company, this turned out to be quite interesting to us, given that our ASO service in iOS mainly focuses on increasing search traffic through keyword optimization.
In both cases 1 and 2, the analysis showed a negative correlation between organic and paid traffic. For case 2, in Google Play, graph 3 shows a Pearson coefficient of 0.16, which is considered a very weak correlation.
Taking into account the analyzed data, we can conclude that, for the tested time period, it isn’t possible to determine that a linear correlation between organic installs and UA installs exists. Hence, the possibility of numerically measuring the precise impact of UA on organic traffic becomes even more complex.
Nevertheless, the fact that only three apps were analyzed — in comparison to the millions of apps available — makes it worth mentioning that, with such a small data sample, conclusions cannot be drawn for every app and game on the market.
Given our expertise as an ASO Agency, and having undertaken a variety of projects, we cannot determine a specific app pattern given the different behavior, in terms of traffic, that each app has. At Lab Cave, as a data-driven company, our objective is to constantly be at the cutting edge of the mobile industry while offering our clients a custom-made service according to each unique app, where different techniques and data analyses are applied.
Enric Pedró has focused his career on mobile growth, development and advertising, he’s currently Chief Marketing Officer at Lab Cave, by Fibonad, where he oversees the company’s ASO (App Store Optimization), Mediation and Publishing services.
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