An app install campaign is a user-acquisition-focused activity which aims to generate installs adopting the best payout model for the optimization of the app campaign.
The essential goal of an app install campaign is motivating people to download the app and encouraging them to produce in-app events and generate revenues. An app install campaign also benefits organic downloads, boosting the ranking of your app in app stores.
The payout model in the app install campaigns
Payout models are a crucial part of an app install campaign because they represent the ways in which advertisers pay the publisher, which happens through an ad network or DSP. The payout model should always be in line with the goals of the campaign to create a successful mobile app install campaign. Therefore, it should always depend on the kind of app, its purpose, and the overall strategy of the campaign.
CPI and CPA are the two main payout models which meet two main purposes: enhancing the app user base and acquiring high-quality users. Knowing the differences between CPI and CPA is crucial if you want to work for a successful app install campaign. CPI campaigns are usually used in those campaigns whose particular focus is growing the app user base, while CPA is the preferred model of the campaigns centered on the acquisition of high-quality users.
Cost Per Install
CPI is a pricing model used in app install campaigns, setting the price that the advertiser has to pay to the publisher after every install, thus, it can be described as a fixed price. Whenever a user installs and opens the app for the first time after having engaged with the ad, the advertiser must pay a fixed cost to the DSP or other media source. The CPI method allows you to scale the installs’ volume to grow the app user base and increase the popularity of the app.
However, you can also aim at high-quality users working on the optimization of the campaign by tracking post-install events. Therefore, the CPI model represents a good indicator of the performance of your app, allowing you to see the effectiveness of your campaign in generating installs and helping to acquire a larger user base.
The only problem with the CPI model is that it could not be the best solution for the increase of revenues. That’s why CPA must come into action by fixing the other goals of the app install campaign.
Cost Per Action
CPA is the second payout model in which the advertiser has to pay the publisher only when a user performs some kind of in-app event such as subscription, purchase or other forms of post-install events. This action is chosen by considering the app KPIs, which help to choose the more suitable action for the campaign, that’s to say, the one that is more in line with the client’s goals. Overall, the choice of the pricing method will depend on the app user acquisition strategy of the campaign and also on the level of risk the advertiser is willing to take.
There is not a single method suitable for all app install campaigns because every model best fits different purposes. However, the CPA model is mostly used in those campaigns in which the advertiser is interested in growing its user base through the acquisition of high-quality users, who are more likely to perform an action within the app helping increase the ROAS of the campaign.
That’s why CPA campaigns are very popular, bringing several benefits to app campaigns and ensuring the acquisition of the most interested customers for the advertisers who avoid wasting their budget and reducing every kind of risk.
The choice between CPI and CPA models depends on the main goal set for the campaign and the differences between them. However, you can always switch between CPI and CPA depending on the results obtained and the purposes of the campaign.
CPI campaigns are essential for all those advertisers who want to increase the volumes of installs and their user base, while CPA is the perfect model for all the advertisers who want to take fewer risks while growing their ROAS. Therefore, you always need to follow these small tricks if you want to work for app campaign optimization.