November 3, 2022
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If you’re a performance marketer, it can feel like your options for effective advertising are narrowing.
But here’s the good news: despite privacy regulations, LAT, and ATT, you still have great options. Machine learning (ML) is proving highly effective at taking whatever first-party data you have and using it to identify and target the right audience at scale.
Here are five key reasons why you should leverage ML for in-app performance advertising.
A key benefit of a quality ML engine is that when data scientists provide the ML models with raw information, it will determine which data is relevant, the degree and weighting of that relevance, and a prediction of an outcome.
In the advertising world, the models will learn which data points are relevant (channel, device type, time of day) and how much importance to assign each data point, and use that analysis to predict an outcome, such as whether this user is likely to install an app or take a specific in-app action.
Moloco’s ML engine uses deep neural networks to handle deeper analysis for the desired outcomes.
ML can process more bits of data per second than humans, and they can do so at a much faster rate. Plus, they don’t miss important connections because they’re fatigued.
In order for ML systems to continue learning, they must be as unbiased as possible. ML systems work best when the model doesn’t make assumptions and certain data isn’t treated more or less favorably.
Some degree of change is natural over time, which is one of the reasons why a really well-trained ML model is critical. Where a human observer or biased system might treat previously unlikely observations as outliers or exceptions to the rule, an unbiased approach to data applies the appropriate weight to new information and continues to learn.
Unlike other ML approaches, Moloco’s ML engine constantly (hourly) ingests new data and quickly adapts to any changes the new data introduces.
It’s essentially future-proofed.
Moloco’s ML includes bid price optimization, which ensures you don’t overpay for inventory or lose valuable impressions because we didn’t bid high enough. Additionally, by enhancing bid-processing infrastructure efficiency models, Moloco keeps the cost of bidding down, which enables deep learning to occur in commercial settings.
Different UA teams will invariably have different benchmarks and key performance indicators. Apps that rely on volume to monetize, like hypercasual games, will focus on install volume and cost per install (CPI). In contrast, an app that monetizes through in-app purchases or transactions may care more about return on ad spend (ROAS).
Moloco’s ML models are adept at finding profitable users — people who install an app and take a desired, usually monetized, actions – whether that’s buying in-game currency, funding a crypto wallet, watching ads in-app, or shopping in a marketplace.
Interested in learning more about ML and its role in performance advertising? Be sure to download your complimentary copy of 5 Key Aspects of Machine Learning for Performance Marketers, Moloco’s comprehensible primer on the science behind ML. Not all machine learning engines used by demand side platforms (DSPs) today are the same. Our ML primer highlights Moloco Cloud DSP's unique differences and why they are important. Download your copy of our ML primer today!
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