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


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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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Programmatic Advertising: Re-engaging Users Through Mobile Retargeting

Back in the early days, app marketers’ biggest challenge was getting users to install their apps. Today, app install is still one of the most common key performance indicators (KPI) in an app marketing campaign. However, as the mobile app industry continues to mature, app marketers are faced with an even bigger challenge.

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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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