Advanced ASO strategies

Discover best practices on how to use Sensor Tower to drive new users to your platform

Understanding the Impacts of Paid Marketing Campaigns

Sensor Tower provides comprehensive insights through multiple mobile data platforms. It is incredibly useful for evaluating the impacts of paid marketing campaigns on app growth and understanding user activities. Using Ad Intelligence combined with Store IntelligenceUsage Intelligence, and App Intelligence, you can track the trends of an app's ad spending, and see how it has influenced downloads/revenue, the number of active users and category rankings. This article will use a lifestyle app as an example to show you all relevant features, and how you could gain valuable insights across Sensor Tower's platforms. 


Track Advertising Activities Using Network Analysis

With the Network Analysis feature under Ad Intelligence, you can track how an app's Share of Voice (SoV) had varied over time across ad networks. You could also add your competitor's apps to compare their share of voices side by side in all ad networks. 

In the chart below, you could find the % share of voice of the app in the US App Store in 2018. This app had launched burst ad campaigns last year, and it mainly focused on the time period from January to mid-April across multiple networks. 

Note: Since the size of networks are different, an 1% share of voice from some networks such as Facebook can be much more impactful than a 1% share of voice in smaller networks such as Applovin. Please don't compare the percentage number of SoV across networks.


Evaluate Marketing Effectiveness Using App Analysis 

App Analysis within Store Intelligence provides downloads and revenue estimates for any app from the App Store and Google Play Store. You can add a cohort of apps to compare multiple apps' downloads or revenue across countries and devices. 

In the chart below, you are looking at the weekly downloads that this app got from the US App Store in 2018. Do you remember the point in time when this app started decreasing their advertising activity? As they decreased their advertising in April, their weekly downloads trend perfectly reflects this change as their downloads dramatically dropped from 30K+ to 10K+ in April as well. On the other hand, it proves that the app growth was significantly driven by their ad activities during that January to April time period.

Note: Sensor Tower's downloads metric is unique downloads per iOS or Google Play account. It does not count re-installs, app updates, or subsequent installs on an additional device for existing iOS or Google Play account. 



How Does Category Ranking Change with Downloads and Ad Activity

The Category Rankings feature within App Intelligence helps you track daily or hourly category rankings of your own or your competitors' apps over the selected period time in any country. While neither Apple App Store nor Google Play Store has revealed specifics about how they calculate category rankings, Sensor Tower finds out that both app store algorithms generally factor in average app store ratings, the volume of ratings/reviews, keyword density, app usage statistics, growth trends, downloads, etc. 

Therefore, when an app launches a burst marketing campaign which drives the growth in downloads, the category ranking of this app would also be influenced significantly. As the graph shows, this app's category ranking had climbed from 60th to 10-20th in the Lifestyle category from January to April. It had improved the visibility of this app in the App Store, which contributed to organic download growth as well. However, since mid-April, the app's category ranking dropped along with the downloads. This also matches up with its advertising trends.

Note: The "+" signs on the timeline mean that the app had updates on that day. You can click on the icon, and it will take you to the App Updates Timeline feature under App Intelligence.



Analyze the Impacts of Ad Activity On Usage 

Sensor Tower's Usage Intelligence platform enables you to distinguish real growth from noise by following the user journey post app installation. Through Active User data, you would be able to compare apps' DAU/WAU/MAU side by side in the same graph.

Note: Active user is defined as any user that brings the app to the foreground of the phone for more than 2 seconds for a selected period. 

The WAU of this app had grown from 60K to 100K during the marketing campaigns. Although this app's downloads dramatically declined since mid-April of 2018, it was able to retain the new users acquired from marketing campaigns, and the decrease of ad spending did not bring significant negative effects to the active user number as much as to the downloads. 




This use case presents how Sensor Tower's platforms can help you understand the impacts of paid marketing campaigns on downloads, category rankings, and active users. Going through this article, you learned that this app's advertising activities had significantly contributed to their download growth, higher category rankings, and it successfully retained new users after marketing campaigns to mitigate the negative effects of decreasing ad spend. 


If you have any questions on this use case or our products, please reach out to

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