Read thought provoking articles on the use of machine learning for improving mobile app marketing performance.


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Bid Landscape Evolution

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.

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Aarki Encore Inside Out

Mobile advertising continues to grow at a rapid pace, thus, industry leading companies are adopting more innovative technologies and new strategies for acquiring and retaining high LTV users.

See how Aarki redefines mobile programmatic app marketing.

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Using App Embeddings to Improve ROI Optimization Performance and Solve Cold Start Problem

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.

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Case Study: Multiplicative Algorithm - Machine Learning Optimization to Drive Higher ROI

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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).

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Leveraging External Datasets for Probability Prediction by Decomposing Probabilities

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.

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Using Non-Attributed Events to Build Lookalike Audiences

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.

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Transfer Learning: An Approach for ROI Optimization

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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.

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Bayesian Approach to Transfer Learning: Predicting Rare Events

By Igor 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.

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Differentiating Between Open Auction, Private Auction, & Preferred Deal in Programmatic Advertising

Programmatic advertising has become one of the fastest growing channels of digital marketing due to its inherent abundance of media inventory and buying efficiency. It offers multiple ways for mobile app advertisers to find the right media inventory for their ads. But with a few different ways to acquire media inventory programmatically, app advertisers are sometimes confused about which is the best way. To lessen the confusion, let’s shed some light on the differences between three different ways: open auction, private auction, and preferred deal.

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Using Machine Learning to Predict Campaign Performance

Wouldn’t it be great if 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.

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