We continue the blog series that we believe will give you a clear understanding on how Aarki finds ways to fully understand, test, and optimize creative ads to ensure that targeted users see the best possible variant. Read on for insights on contextual creative selection.  

With the global increase of mobile and internet penetration and the growing number of apps installed on mobile devices, a vast amount of data is available for mobile marketers. Through Machine Learning (ML) powered advertising platforms, marketers are now able to use Machine Learning to leverage data effectively and connect with their target audience. But what does ML really mean for mobile advertising? 

In the not-so-distant past, most app marketing campaigns were focused on driving a large volume of app installs at the lowest possible cost. Nowadays, marketers are increasingly interested in optimizing app campaigns directly on the return on investment (ROI).

Programmatic advertising continues to shift away from simply impressions to a more advanced audience-targeted approach. Ultimately, advertisers running programmatic app marketing campaigns are interested in acquiring users with a high lifetime value (LTV). The definition of LTV varies from advertiser to advertiser, but in most cases, it is expressed as the revenue the user generates through in-app purchases or in-app advertising.

As the mobile programmatic industry rapidly grows, so does the number of bidders, causing app marketers to pay more attention to auction dynamics. Bid optimization and bid landscape inferencing are increasingly becoming crucial parts of the bidder strategy.

By Igor Raush, Software Engineer

For the majority of its history, the programmatic advertising industry has accepted the second-price auction model as the gold standard for auctioning off inventory. Under this model, the bidder with the highest bid wins the auction but pays the second-highest price. This encourages each bidder to bid their break-even price, knowing that they are guaranteed to pay less.

By Sergey Yengoyan, Software Engineer 

As the number of mobile apps continues to grow at a rapid pace, any dimensionality reduction method that helps decrease the size of a prediction model can improve performance.

Gone are the days when advertisers were relying on the install volume and post-install events in analyzing campaign performance. Though app install remains as one of the most common key performance indicators (KPI), nowadays in an app marketing campaign, app marketers are increasingly becoming interested in optimizing campaigns directly on the return on investment (ROI).

By Igor Raush, Software Engineer 

Advertisers are increasingly interested in optimizing their campaigns directly on the return on investment (ROI) or the return on ad spend (ROAS). In a real-time bidding setting, it becomes crucial to predict the expected revenue from a particular ad impression, which, in combination with the KPI, will determine the amount we are willing to bid.

By Igor Raush, Software Engineer

The focus of app marketing is shifting from driving installs and minimizing cost per install to acquiring users who will become paying customers, effectively maximizing the ROI. Unfortunately, high-LTV users who can be attributed to an ad campaign are extremely rare in comparison to the number of impressions served, and it is difficult to accurately capture the profile of a quality user.