With programmatic advertising growing rapidly in the mobile app space, learn how machine learning is enhancing ad targeting and performance in this Q&A with Andry Supian, Head of Product at Liftoff.
In this Q&A, Andry Supian, Head of Product at Liftoff, discusses the challenges surrounding the sheer volume of advertising opportunities in the mobile app programmatic space and how machine learning can help solve one of advertising's biggest challenges: finding lifelong customers amongst the volume.
Despite widespread adoption, advertisers still struggle to improve performance in programmatic channels. Why do you think that is?
There are three reasons: volume, variety, and velocity. Liftoff serves billions of monthly users across hundreds of thousands of mobile apps. This creates over 640 billion ad impression opportunities from ad exchanges every day. Because these apps monetize using a variety of different setups, the paths to reach these end users are vast and often overlapping. For anyone looking to market their gaming or e-commerce app to this demographic, figuring out where to start can be daunting. Determining economic paths at scale is also a very complex problem to solve.
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What is machine learning and how can it be applied to mobile app advertising to solve this challenge?
Machine learning (ML) has been around for quite some time, but over the last couple of years it has become a hot topic in the tech industry. In app advertising, ML is designed to take various datasets from apps and marketers who buy ads on those platforms and use them to determine where and when to serve ads.
Machine learning is an effective way for advertisers to promote their apps through programmatic channels because it is self-learning. ML derives useful patterns from a large number of sources of information in ways that go beyond human capabilities. Using prior knowledge, it can help advertisers select the right starting point for their campaigns – users who are most likely to be interested in their product. From there, they can iterate based on observed results. ML repeatedly shows the computer what “good” and “bad” look like. This is where “self-learning” comes in. It's impossible to manually replicate this at the speed and scale required to make programmatic advertising work.
How will ML change the entire advertising industry?
Traditionally, marketers have tried to make ad buying decisions based on audience segments, usually derived from third-party data. What's different about machine learning, especially in the app space, is that it doesn't pre-determine who your audience will be. With machine learning, the machine matches the outcomes marketers want with the users who will receive their ads. With machine learning, you no longer need to specify inputs (such as a manually curated list of publishers or a defined price range); instead, you need to specify a goal (“find users who completed a form”).
What helps machine learning succeed in mobile app advertising?
ML is an effective technology for sifting through large volumes of users and bidding opportunities, but it doesn't need to be without constraints. Layering guardrails (block lists, suppression lists, custom rules) allows campaigns to leverage ML as well as the knowledge advertisers already have. But the data must be true. Truth is more than clean; data must accurately represent the environment marketers want their models to learn.
Think of a model as something that represents something, be it a mental model or a business model. ML models for the app advertising ecosystem are no exception. A model is a mathematical representation of the data it sees. As such, the data needs to be clean, up-to-date, and as detailed as possible about the state of the environment.
If you’re leveraging ML in advertising, directly or indirectly, what can you do to set yourself up for success?
An important application of ML is creative strategy. Machine learning can identify valuable pattern recognition to determine the optimal creative message. But remember, at the end of the day, an individual user sees your ad creative, elicits an emotion, and takes action. Think of ML as another trusted colleague in the room. ML amplifies your intelligence, but it doesn't replace human creativity and teamwork.
For more information, visit www.liftoff.io.
