How App Science Will Lead to Better Predictive AI

Every company wants to be able to predict the behavior of its customers, both to ensure the efficacy of their advertising and to sell more products in general. Artificial intelligence has often been touted as the way to give advertisers the ability to see in the future, so to speak, or at the very least detect the general trends amongst their target audiences that will then inform the way companies advertise and reach out to consumers. Any system that relies on AI, however, is only as good as the data you give it. Needless to say, it’s imperative that you input only the best data available, which means going beyond Nielsen surveys and information gathered from a select few, and embracing sources of information (such as mobile phones) that present a more holistic and nuanced view of each individual consumer.

App Science Will Help Brands

Many retailers already use some form of predictive analytics to determine how much inventory they need to stock, employees they need to hire, and so on. But that level of sophistication has so far been lacking when it comes to digital advertising. With app science, advertisers can take the millions of data points collected from people’s phones, and turn that cacophony of information into insights that will help brands reach people at the right time and place.

App science can identify not only the apps you’ve already downloaded, but also use that information and other data points to infer future behavior. For example, say you notice that someone has recently downloaded a whole host of car apps on their phone – apps for comparing models, finding insurance, and buying used cars, for instance. That, coupled with location data that places this person at or near several car dealerships in the past month, is a pretty strong indicator that this person is looking to purchase a car. If an algorithm is able to flag in-market car buyers early enough in the process, perhaps even before they visit a dealership, auto companies might be able to capitalize on that information and serve that person ads for new cars.

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Real-Time Data is the Key

With app science, advertisers have the ability to look at data from hundreds of millions of devices, allowing them to build out more comprehensive models of human behavior. Instead of looking at self-reported survey data, they now have access to both historic and near real-time observed data that can tell them where their audience goes, what they do, what websites they visit, and so on, information that can then be used to build out consumer profiles and improve the targeting and delivery of their campaigns.

Our mobile phones reveal so much about our lives – from what time we wake up in the morning to where we get our morning coffee to how long it takes us to get to and from work. As a result, they can help create a richer understanding of how we as consumers interact with the things around us, which in turn helps advertisers determine the right messaging and approach.

The growth of e-commerce has found many industries scrambling to maintain their competitive advantages and stay afloat. Advertising that is optimized for each consumer, both in terms of the substance of the ad (the product being advertised and the creative itself) as well as the channel it’s being served on and the time it’s being shown, will help companies stand out amongst the constant deluge of online advertising.

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Pattern Recognition Will Enable Better Targeting

The ultimate goal of app science is to find correlations and patterns that will enable marketers to predict what consumers need before they know they need it – for example, knowing that someone is going to want to buy a car in the near future before they even visit the dealership. Certain life stages – having a baby, changing jobs, getting married – naturally lend themselves to certain purchasing patterns, and we want to use app science to identify when someone is going through one of those life stages and send them the appropriate ads, before competitors do.

This is precisely what predictive analytics is supposed to do – use data to map out future behavior. Armed with enough data and the right technology, whole industries will be better able to target people based on app science and predictive analytics.

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