The advertising industry has made huge strides in attempting to understand the mind of the consumer and potential buyer. But it hasn't been able to predict the future - that is, until now. With the emergence and proliferation of predictive advertising technologies that predict consumer behavior before it actually occurs, that future may well be here.
It's well established that predictive analytics has dramatically changed the digital marketing landscape in recent years, and it’s only continuing to grow and evolve. According to a recent report, the global predictive analytics market is set to reach $10.95 billion by 2022. Predictive analytics has a variety of applications in marketing. But for digital marketers, the most valuable application will be predictive advertising.
As a subset of predictive analytics, predictive advertising is leveraged by businesses to analyze data from consumer behavior, which then uses these insights to predict future trends and optimize marketing and sales strategies.
Perhaps most importantly, predictive advertising can help you identify members of your audience that are likely to convert while automatically prioritizing leads, made possible by leveraging advanced statistical modeling and machine learning. Specifically, machine learning looks at historical data patterns and derives insights from these patterns in order to make informed changes to advertising strategies. And there are a number of benefits to using machine learning, including:
What’s more, machine learning makes it possible to achieve these goals with more speed and accuracy than an entire team of data scientists.
Artificial intelligence is a powerful technology that makes predictive advertising possible. But another key element is the wealth of consumer data available today. The growth of Internet of Things (IoT) devices is presenting a myriad of opportunities for data collection and analysis. And it’s no secret that the average consumer today leaves a massive digital footprint, which is only continuing to grow. Thus, predictive advertising strategically leverages this data to drive a plethora of consumer insights, including:
Without a doubt, the wealth of data available today enables marketers to better understand the intentions, preferences, and buying patterns of individual members of their audiences, while also making it possible to better track the nuances of the market they’re advertising in. That said, this data is only valuable if advertisers find ways to successfully unlock it. Conversely, the abundance of data can easily overwhelm marketing teams with too many relevant touchpoints to analyze, creating significant gaps and a host of missed opportunities.
Now enter predictive advertising, which can use your internal audience data as well as third-party behavioral data to identify new potential customers to target and enable you to broaden your audience base further than you would if you simply relied on regular targeting tactics.
Predictive advertising can draw correlations between demographic and behavioral factors that regular marketing analytics simply wouldn’t be able to make. Leveraged in tools like Google’s Lookalike Audience and Facebook Similar Audiences these technologies provide the ability to draw correlations between different demographic and behavioral data to identify new relevant audiences to target. For example, say predictive analysis helps you discover that members of your current customer base are likely to read a certain online publication. From there, you could then start targeting other people who also read that publication.
Most marketers today who use data to drive targeting insights rely on their own first-party behavioral data: metrics like site pages visited, lead magnets downloaded, and abandoned carts. But first-party data only tells you so much about your audience’s needs and intent to purchase. When you add in behavioral touchpoints from other sites, you get a much richer picture of a lead’s intent, which in turn makes it possible to reach leads with relevant marketing messages deep in the sales funnel, as well as improve retargeting capabilities for display ad campaigns.
The better businesses understand their audiences, the easier it is for them to create relevant, targeted ad copy that converts. Combining first and third-party audience data allows you to build more robust buyer personas to inform your marketing collateral. But can these creative changes be automated and implemented at scale? The answer is yes, but with the help of machine learning. Machine learning can drive predictive insights and in turn help you make smart choices to improve advertising copy and ad design. While machines aren’t replacing real creatives, they can help optimize marketing collateral to become more effective and relevant to target audiences.
Predictive advertising already allows you to automate ad personalization based on a variety of behavioral, demographic, and situational factors such as device ID, domain, location, purchase history, or interests, and trigger that data to reach relevant audience members. Predictive advertising insights can also inform the right kind of copy to include in dynamic PPC ads. And we’re only just beginning to realize the possibilities for its potential to unlock new, unique and valuable customer demographic insights.
According to data from Nielsen Digital Ad Ratings service, billions of online marketing dollars are being wasted in advertising -- largely because ads are reaching the wrong audiences, reaching the right audiences at the wrong time, or businesses are simply spending more than necessary to achieve their marketing goals.
On the other hand, businesses that focus on minimizing wasted ad spend get the double benefit of being able to channel their saved budget into other marketing initiatives. Predictive advertising can help with this by executing nuanced targeting and bid adjustments in real-time to drive more ROI. A good example is Google’s automated bidding platform, which allows advertisers to choose a goal (site visits, visibility, conversions, etc.) and then leverages audience data from its advertising network as well as competitor performance data to automatically adjust bids in real time.
But targeting capabilities become even more nuanced and effective when using third-party predictive advertising technologies. Because now you’re free to use any kind of relevant third-party data you want, so you can rely on site visits, social media engagements, your PPC campaign data and other variables to drive even deeper insights. And instead of relying on the one-dimensional goal options of Google’s automated bidding, you can customize the key performance indicators (KPIs) you want to target.
Thanks to smartphones and on-demand information, it’s become increasingly difficult for advertisers to target audiences at some of the most critical and relevant times. For many purchase decisions, it’s possible for people to show intent and convert into a buyer mere minutes later. By the time they see advertisements for the product they’re looking for, it’s already too late.
Google calls these micro-moments: An intent-rich moment when a person turns to a device to act on a need — to know, go, do, or buy. There are four key micro-moments that advertisers can target to help their audiences convert:
Not surprisingly, targeting ads to consumer micro-moments is incredibly valuable for businesses because consumers are determined to find what they’re looking for, and will be interested in brands that help them achieve that goal.
But successfully targeting ads to micro-moments is essentially impossible without using some kind of automation. While PPC advertisers can use Google’s in-market audiences to determine who is searching for certain categories of products and automatically target them with relevant advertising, predictive advertising can take this goal to the next level by helping you anticipate customer needs before they start showing strong signs of purchase intent. Using historical data of consumer online behavior and demographic information, predictive advertising can anticipate what kind of people could be interested in your products and services before they start the buying journey. In essence, it’s possible to start targeting these micro-moments before they even occur.
Of course, there are some kinds of micro-moments advertisers can’t anticipate in advance. Someone’s car breaks down on the side of the road and now they’re looking for auto repair services. Or someone’s relative passed away and now they need funeral home services. How can advertisers target these new “interests” when they come up so suddenly?
Predictive advertising technologies can handle the challenge because they analyze massive amounts of data in real time. Machine learning can infer intent from search queries, location, and other factors, then automatically serve relevant ads. What’s more, this is all done in the background, giving marketing managers the freedom to pursue other media-buying opportunities.
Predictive advertising is a relatively new application of predictive analytics. For most marketers, it might sound like a luxury only the most competitive enterprise businesses can afford to fully utilize. And that likely won’t change as long as adoption of the technology remains low. In the future, however, it will likely be essential for a wide range of businesses. Consider:
Over time more businesses will adopt automation and predictive advertising to optimize their campaigns. Taking advantage of the same technologies to improve campaign performance will become the only way to keep up with the competition, let alone stay ahead.
Arguing that predictive advertising is “too expensive” to fit budgets will be difficult as the technology continues to demonstrate value for businesses. Predictive advertising can identify minute bidding inefficiencies that can add up to a significant reduction in unnecessary ad spend. Not taking advantage of the technology can end up costing you money instead of saving it.
Budget reallocation opportunities
Predictive advertising helps advertisers ensure they only spend the minimum amount necessary to reach their marketing goals. That means more of your advertising budget can be redistributed to other initiatives that maximize ROAS. This is particularly important considering that for every $1 spent on Google Ads, businesses can earn $2 of revenue. Predictive advertising helps make sure each dollar of your budget is well spent.
Predictive advertising can automate a wide range of campaign optimization tasks that would otherwise need to be done by advertising managers and their teams. Rather than replacing their jobs, AI and machine learning can help free team members to focus on other marketing initiatives, such as discovering new media buying opportunities to drive growth.
Google Ads and other major advertising options are already making big changes to incorporate AI and machine learning into their platforms. And a wide variety of external tools, with more advanced targeting, data analysis, and optimization features, are also available. Given the enormous benefits, value and competitive edge predictive advertising brings, it’s simply a matter of time before all advertising becomes predictive. And tools like machine learning paired with advanced statistical analytics can help businesses of every size improve the relevancy, efficiency and ROI of their advertising campaigns.
The future is being able to predict the future. And with predictive advertising, we’re one step closer to that reality.