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).

ByIgor 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.

ByIgor 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.

CTR is loosely correlated with the quality of users. As a result, training models to optimize for clicks can lead to achieving high CTR but poor ROI. Transfer learning is one approach to optimize for quality users.

ByIgor Raush,Software Engineer

An advertisement's click-through rate (CTR) is often used as an early indicator of its effectiveness; however, the ultimate goal of any campaign is to reach and acquire prospective customers. Unfortunately, the CTR is often weakly, or even inversely correlated with the quality of a user segment, as measured by the retention rate or ROI. As a result, training models to optimize for clicks can lead to wasting impressions on low-quality users, achieving high CTR but poor ROI.

Wouldn’t it be greatif we could predict the performance of our next mobile app marketing campaign even before it starts?

More importantly, it would definitely be useful if we could identify the key aspects of a campaign that are most likely to drive its performance.

Most app marketing campaigns are focused on driving a large volume of app installs at the lowest possible cost. While this may be a good initial strategy, it must give way to more downstream focused strategies as the campaign matures. More savvy app advertisers are increasingly recognizing the folly of an install-focused approach and are instead exploring mechanisms for optimization of post-install events.

An ad can be deemed successful if it piques audience interest enough to incite them to interact with the call-to-action. To measure how well the ad does in capturing interest, ad click-through rate (CTR) is typically used. The higher the CTR, the more successful the ad is in generating interest amongst the target audience. In addition, predicting the CTR can be helpful in setting campaign goals. The more accurate the prediction is, the better it can help advertisers set realistic expectations. This prediction can also be used to make better media buying decisions. Thus, the ability to accurately predict ad CTR is essential in mobile app advertising.

For a long time, creative has been the holy grail of advertising. Something that is a pure art and cannot be quantified or optimized. Even with the proliferation of digital media and cheap computational power, optimization was something that was done after the ad creative was finalized. But this basic premise is being increasingly challenged.