As each year comes to a close, we’re met with a slew of Nostradamus wannabes who think they can predict what’s to come. So far, they haven’t provided much in terms of valuable, actionable insight, evidenced by unpredictable Super Bowl wins, the growth of lotto jackpots, and that shocking “Lost” finale.
The lack of predictive validity is something all marketers can relate to. Consumer buying behavior is increasingly complex, and although more touchpoints breed so much more consumer data, marketers have so little clue of how to use it to pinpoint future actions.
But word on the street is that’s changing – right now – thanks to advancements in predictive analytics. Or was it advancements in AI? Or both?
What’s the difference, anyway?
What is Predictive Analytics?
In short, predictive analytics is the practice of using historical data to predict future outcomes. It combines mathematical models (or “predictive algorithms”) with historical data to calculate the likelihood that (or degree to which) something will happen.
For example, let’s say I’m a cat lady who adopted 1 cat every 3-6 months for the past 5 years (and for some reason logged details about my feline obsession in excel). A predictive model could use my crazy-cat-lady data to predict things like (1) when I’m most likely to adopt my next fur baby, (2) the breed of cat I’m likely to abduct, and (3) the rate at which I become a lonely spinster. OK, maybe not the last one (depends on the data).
For marketers, the same rules apply. Marketers can apply predictive algorithms to data in their arsenal to predict outcomes that inform business decisions. For example, historical data can be used to predict changes in site traffic over time, or the likelihood that increased ad spend will influence order volume.
How Does Artificial Intelligence Make Predictive Analytics Better?
Predictive modeling and analytics has been around for a while. But it’s lacked three major things that are important to drive true marketing value: scale, speed, and application. That’s where AI comes into play.
Artificial Intelligence (or AI) is a type of computer science focused on creating systems that automate “intelligent” processes – i.e. human-esque tasks like decision making, problem solving and learning. Basically, AI enables computers to do things that – without it – require human intervention. But since computers can act at lighting speed, AIs can work with speed and precision that mere mortals can never hope to achieve.
When artificial intelligence is used for predictive analytics, the combined power means improvements across scale, speed, and application.
With AI, predictive models can account for an incredible volume of real-time information. For example, Magnetic’s AI platform continuously analyzes over 1 petabyte of consumer data to inform decisions and actions (for context, Google processes 20 petabytes per day). This means predictive models can consider much more information than ever before, making their outputs more precise and actionable.
Speed & Application:
AIs can accomplish years’ worth of “human” work in mere moments. Magnetic’s AI evaluates billions of variables in real-time, and makes simultaneous decisions to analyze over 1 million marketing opportunities per second. I don’t know about you, but I can barely walk and text at the same time, so color me impressed, AI.
When this speed is applied to predictive modeling, the result is [close to] real-time decisioning and real-time actions. If I search for “LICKI Brush,” a marketing AI can process my info (past and present) and serve me an ad for a self-help seminar – all by the time the LICKI Brush homepage loads. Without AI, that wouldn’t be possible; alone, a predictive model can’t make sense of that volume of data that quickly, nor do predictive models have the “cognitive” ability to take action (i.e. determine how to serve the ad + serve it).
What it boils down to, is: predictive analytics and artificial intelligence are two different things. When combined, they bring out the best in each other (aww…). AI empowers predictive analytics to be faster, smarter, and more actionable than ever before.
What Does This All Mean For Me TODAY?
AI and predictive analytics are a dynamic duo, with a wide variety of high-value applications. How you apply them will vary based on your marketing goals and the AI platform you’re using.
At Magnetic, our clients are reaping the benefits via our new AI optimizations. Our AI platform supports each step of campaign execution, from audience characterization to post-bid optimization. Campaign performance soars because our predictive models can calculate the precise value of each auction opportunity, and our bid optimization uses that info to cherry-pick the right bids, and determine the right price to pay for each one.
Better yet, our clients can optimize campaign delivery based on a variety of outcomes. Each optimization type values users based on their likelihood to take the desired action, and targets the highest-value users at the lowest possible cost to our client. For example:
AI-enabled optimizations like these are game-changing. Soon, their application will be table-stakes for any marketer.
Stay tuned for more from our “Explaining AI” series; we’ll continue to demystify the lexicon of AI-related terms, and more importantly, let you know why you should care.
Magnetic is a digital marketing and artificial intelligence company. We use machine learning and AI to deliver smarter, faster, and more effective advertising. Our powerful AI platform continuously analyzes the attributes of 320 million live user profiles alongside real-time inventory supply and bid opportunities to deliver highly performant and profitable campaigns for our clients.