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February 16, 2023 (Updated: March 8, 2023)
You may have heard the saying before, “there’s no such thing as bad press.” That sentiment may apply to celebrities, but it doesn’t apply to your paid advertising. Generic or mistargeted ads not only waste your resources but can annoy and turn away your target audience. Luckily, a predictive advertising strategy can help you hit the mark every time to get the most return on investment (ROI) out of your ad placements. Today, we’re looking at what predictive advertising is and why using it in your strategy is an asset to working smarter, not harder, with your marketing:
Predictive advertising is a paid marketing strategy that uses artificial intelligence (AI) to make educated guesses about how people interact with your content. It uses past consumer data to predict how future searchers and browsers will respond to tailored ads with different content and on different channels. This strategy grew out of predictive analytics, a mathematical concept developed in the 1930s. Predictive analytics uses historical data, machine learning, and algorithms to make educated guesses about future events like weather patterns or economic changes.
Paid advertising takes these predictions to another level. It focuses primarily on identifying new leads and targeting them with paid ads on the right channels, with the right content, at the right times.
Predictive advertising is a subsegment of predictive marketing. In predictive marketing, your team uses machine learning and AI to make guesses about any future marketing activities. That could include securing conversions, segmenting your audience, or optimizing content for search engines. Predictive advertising focuses solely on paid marketing techniques like ad placement, social media ads, or search engine marketing.
Depending on the types of marketing campaigns your company runs, you might use both predictive advertising and predictive marketing together without a distinction. In other cases, especially for companies that invest heavily in paid marketing, you may have a separate paid ad team that develops predictive advertising strategies separate from other predictive marketing activities.
Related: What Is Predictive Marketing and Why Use It?
This might sound like a silly question, but predictive advertising is different from traditional digital advertising. If there wasn’t a difference we’d call it all, simply, advertising. Although all advertising involves some form of prediction to guess how your audiences will receive your content and what it encourages them to do, the way you go about it differs.
Traditional advertising involves humans reviewing past performance data and drawing conclusions about what went right and what could have worked better. It’s often done on a small scale with limited data because people aren’t computers. Getting large-scale data results could take months or even years when people do the work themselves.
Predictive advertising leverages technology capabilities like AI that expedite this process. It allows you to draw insights from data on a bigger scale that the human brain and work capacity just can’t handle. You can look at all customer attributes, behaviors, and signals, sourced from a variety of different data collection sites. Most companies often use a combination of traditional digital advertising and predictive advertising. Which methods they choose depends on the campaign size of the target audience, and the amount of time they have to conduct their analyses.
We just got through telling you that predictive advertising is a job for computers and AI. But that doesn’t mean humans have to stick to just traditional digital advertising. The benefits of technology and AI are the amount of data collection and analysis you can do and how fast you can do it. But computers and AI aren’t humans. They can only do what they’re programmed to do with the parameters you’ve plugged in. Because they have a limited scope, technology could miss a key part of any analysis: context.
This is why it’s important for human quality assurance professionals to always check over the data and predictions that AI makes. These QA specialists check the data to make sure it’s logical and fits with what they already know about their target audience segments. Skipping a human review could make your predictive advertising efforts moot by allowing a campaign to run in the wrong context.
Though a lot of predictive advertising focuses on your audience, that’s not the only information you may want to review to get insights. Human behavior isn’t an exact science, and many factors may influence how your audience makes decisions. Aside from common audience data like demographics and psychographics, here are some other types of data you can use for predictive advertising:
First-party data is any information your company stores and collects. This could include information about website or app traffic, customer surveys, and feedback, or even industry studies your company conducted. First-party data is unique because you can’t get that information anywhere else. No matter what big data sources your AI programs crawl and analyze, these insights only come from you. The more first party-data you have, the more personalized you can make your advertising experiences for your target audience.
Historical data is any information collected from the past. It includes both first- and third-party data from a variety of sources. The more historical data you have, the better chance you have of finding patterns in audience behavior over time. This allows you to draw more conclusions about future interactions and engagements.
Where your audience lives, works, and travels can also have an effect on their ad interactions. For example, someone on vacation may be more open to buying trivial things they don’t need. The weather or events in a town could affect users’ browsing or buying habits, too. The more you know about where your audience lives or spends time can help you understand why they make the decisions they do when shopping online.
Real-time data is any data you collect on your channels or from your ads as they happen. For example, Google Analytics has a real-time dashboard that tells you the number of people currently viewing your website. It also shows you what they’re looking at or clicking on. Most ad programs offer this real-time data so you can see how your audience is viewing and interacting with your content as they see the information online. Real-time data helps address micro-moments, which we’ll talk about later in this article.
You might not think the weather has anything to do with how your audience interacts with online ads, but it can. A power outage could mean your viewers have to browse the internet on a mobile device. A pending natural disaster could mean they’re looking for survival products like nonperishable food or batteries and flashlights. Looking at weather data in connection with audience behavior can help you understand why they make some of the choices they do. It provides additional context for their browsing intent.
Predictive advertising helps you use insights to build marketing strategies based on millions of data points. Without the help of AI, trying to make these predictions alone, or even as a team, would be nearly impossible. Aside from the technological strides, here are some additional reasons to consider using predictive advertising in your marketing strategy:
The amount of data people create every day will blow your mind. In 2021, sources reported that people and machines generate 2.5 quintillion bytes of data each day. That number is likely even larger now because big data just keeps growing and growing. Every piece of content you develop, every Tweet you share, and any time you use location services online—all these things contribute to big data.
Humans are smart, but the sheer volume of data on any topic, including the kind you need for predictive advertising, is more than our brains can handle. With the help of technology programs, the bots can analyze large volumes of data and give you a report with all the need-to-know information. This allows you to understand the reach and use of big data better without breaking your brain.
One of the most common purposes of predictive advertising is to refine and strengthen your audience segments. Predictive advertising is all about learning who sees your ads and where, if they click them, and what they do beyond the click. The more you know about who does what, the better you can target them with messaging and persuade them to make conversions and move through the marketing funnel the way you want them to.
While these insights are helpful for paid advertising, you can use them for other forms of marketing too, such as influencing your content creation or content placement strategies.
If you’re not using AI regularly (or you don’t know you’re using it regularly) the concept may seem a bit scary. Robots that can think like humans and work faster than us? I think we’ve all seen the ending of this movie before. All joking aside, AI isn’t a human. But a supercomputer can do some of your menial tasks to save your brain for things it can’t do, like understanding context
What AI can do for your predictive marketing is find and analyze a variety of data variables like audience behavior and demographics to predict future behavior based on past performance. It can model the outcomes of different campaigns for multiple audience segments based on changes to or reconfigurations of those variables. It can also understand some context based on the searcher and browser intent.
For example, through different data points, AI could find that people searching for artisanal coffee beans also look at thermoses or even breakfast sandwiches. Through machine learning, AI can make connections between these seemingly unrelated categories and help you discover which ads to share on the heels of another. Once you learn what AI can do (and what it can’t), you can better optimize your strategy and research sessions with the technology and keep your humans on projects that AI can’t do.
Reactive marketing takes place when an unexpected event happens for your brand and you have to take action to prevent problems. The COVID-19 pandemic is the ultimate example of reactive marketing for most companies. Many people never saw that event coming and had to scramble to stay relevant in an uncharted world. Proactive marketing is planning for unexpected or unforeseen events, like COVID-19, before they happen. And predictive advertising can help you pinpoint and plan for events you wouldn’t see coming without big data analysis.
In predictive advertising, many of your unforeseen events come from changes in user behavior, sentiments, or search intent. And these things may change based on external factors your company can’t control, like politics or the economy. By using predictive advertising, you can let your tech tools review large sets of big data. Look at information about not just your audience but about your market at large and your competition. The more you know, the more hints you can have about upcoming changes. Then you can develop contingency advertising plans for all situations that could arise.
Related: The 5-Step External Marketing Audit Process
According to Google, “micro-moments” are marketing and advertising windows of opportunity where you can provide an audience member with the information they need, but only for a limited time. Micro-moments develop out of the idea that your big data insights aren’t valuable forever. Because so much new data becomes available every day, the value of your insights starts to decay the further you get away from the initial analysis.
When you’ve got money on the line, like with paid advertising, you don’t want to wait around to run a campaign with data that dies the second you read the analysis report. Many marketing platforms help you optimize your content for these micro-moments so that your AI tools and platforms can reach your audience before the data expires, right when they need it most.
One of the most common micro-moments you could target is life events. Birthing resources, funeral preparations, and hospice care are things people need but don’t want or need to think about until that life event happens. When it does, they need those services immediately. With the help of predictive advertising, your brand can find ways to target these audience segments when they need your information, not when you think they do.
You might not think of paid advertising as a cost-effective marketing tool, but with predictive advertising, it can be. Running models and scenarios before you spend money on advertising helps make sure you’re advertising in places where you get enough impressions and traffic. It also ensures you’re getting enough clicks to make the advertising worth the cost. When you’re only putting your money into campaigns that have a high probability of working, you’re preserving your marketing budget for other activities rather than blowing it all on a strategy that might not bring results.
Doing predictive advertising also helps with your ROI, or what some paid marketers call return on ad spend (ROAS). By running predictive simulations and models before you spend money on campaigns, you can learn which techniques are most effective for getting clicks and conversions.
You can calculate an estimate of how much money you might get back from each ad in relation to how many clicks and impressions it gets. The one with the highest engagement rate and the lowest payout is usually your best choice. But without predictive advertising, you might waste portions of your budget by advertising in less ideal locations.
Advertising bias is a marketing fallacy that relies on using assumptions, rather than objective truth, to run your campaign. It’s no secret that because so much of advertising strategy is unknown and fluid, it’s hard to find an objective truth on which to base your campaign. But predictive advertising helps you make the most educated guesses possible to eliminate some of this bias.
Using the right tools and data, you can find as many facts as possible about your audience. This includes their pain points and their behaviors. Then you can eliminate the bias of what you think you know about them to make better predictions about what they want to see online.
Predictive advertising isn’t just for attracting new clients or customers to your brand. It’s also about re-engaging with your current audience. Repeat and loyal customers matter just as much to your bottom line as new customers. Maybe more. With predictive advertising, you can learn what brings people back to your brand. Is it making them aware of new products or services? Is it sales? Maybe it’s providing for a new need they didn’t know they had. Predictive advertising helps you avoid discounting those ever-important repeat customers by only focusing on people who haven’t interacted with your ads before.
When you know what people want to see from your ad content, you increase the chances of them engaging with it. Predictive advertising gives you the insight you need to learn more about your audience and their behavior and expectations. Then you can use what you learn to provide something they can’t resist stopping, viewing, and clicking. When you boost audience engagement with your paid ads you also increase the chances of earning more sales and conversions.
As most of the internet stands right now, companies use tracking cookies to follow their audience’s movements online. That’s how you’re able to collect user data about what people browse online, how they interact with content, and if they’re likely to convert after clicking an ad. But Google plans to phase out third-party cookies in Chrome by 2024. And what Google does usually leads other search engines and services to follow.
Now, there’s been talk of eliminating cookies for years, so it’s not 100% certain they’ll disappear in 2024. But it’s better to stay proactive and prepare if the time comes. That’s where predictive advertising comes in. If you lose access to third-party cookies, you’ll have to rely on other data collection and analysis making this possible without tracking cookies. They allow you to provide that personalized experience your audience wants without the invasion of privacy of cookies.
Your team is busy. And if you don’t have a subsegment of your marketing devoted just to paid advertising, sometimes putting in the effort to get it right falls by the wayside. With predictive advertising and the use of AI tools, your team can work smarter, not harder, with its paid marketing. By allowing the bots to sift through the data and using algorithms for content placement and addressing micro-moments, your team is free to handle other tasks. This way you’re still putting an important focus on your paid advertising, but without stretching your human resources too thin.
Your brand isn’t the only one using predictive advertising to get better at paid ad placement across the internet. If you’re using it, the chances are that your competitors are, too. And if you’re not engaging in predictive advertising then you’re going to fall behind. You’ll lose out on the best ad placements, engagement, and potential conversions to companies that have incorporated it into their marketing strategies.
As we talked about, one of the benefits of using predictive advertising is refining the target audience segments you try to reach with your strategies. Using predictive segmentation techniques helps you see which customers are most likely to click on and interact with your ads before you spend money on placement. Doing this can help better optimize your ad campaigns and increase the ROI you get from this type of marketing. There are a few common types of predictive segmentation techniques that you can use to do this. They include:
Classification modeling looks at the negative characteristics of people who don’t fall within your target audience. It examines your existing customer segments and helps you understand why someone wouldn’t fit into the pre-established group. Some of the classifications could be simple. For example, maybe your fashion boutique only sells women’s clothing in sizes 12 to 24. Anyone who doesn’t wear those sizes wouldn’t fit within your target audience or segments.
Other classifications can be more niche. For example, maybe the same clothing store markets a line of politically aware graphic t-shirts. People with differing political views or those who don’t like graphic t-shirts wouldn’t fit in this more specific segment.
Click-based optimizations focus on the likelihood that any member of your audience will click on your ad or make a conversion. This strategy uses AI to pinpoint the user intent of engaging with content. First, it looks at home many people click on your banners or links and what those people have in common. Then, it follows those clickers to your website or eCommerce store to figure out what they do next.
How do they browse your site? What do they click on when they reach your site? Do they sign up for events or make a purchase? All this data helps the algorithms learn who your most qualified visitors and leads are based on their segment similarities.
Related: Does Link Trust Affect How People Click Content?
Forecast modeling looks at past trends to help make future predictions. For example, you can look at different audience behaviors interacting with your past ads and content to determine the likelihood of people clicking your ad content. You can also bring in data from other sources to align with customer behaviors. This shows if any outside influences change the way they interact with your ads.
What types of devices are they using? What was the weather like in their geographic location at the time? While these variables may not seem like they’d make a difference, anything can affect human behavior. The more correlations you can make, the more you can learn about your audience.
Look-alike modeling reviews your current customer segments to find other potential leads that fit within the groups. You do this most often within ad platforms that allow you to target audiences by segment. Google, Facebook, and LinkedIn ads all have similar capabilities. Your audience engagement helps the program learn what type of people are most interested in your ads. Then it uses machine learning and algorithms to find other similar accounts and share your content in those users’ feeds.
While most forecasting and modeling look at cumulative data from extensive time periods, that doesn’t always work in every case. For example, during the COVID-19 pandemic, companies’ past advertising data weren’t going to help. People were living their lives differently than they had in the past. That old data wasn’t going to help make future predictions in uncertain times.
Instead, during the pandemic, companies could have chosen to look at data in shorter spurts, such as from the first few months of the pandemic, to influence future trends. Instead of looking at a bunch of data that didn’t serve them, they looked at smaller-scaled models of information that could.
The uplift model takes click-based optimization a step further to see how company intervention affects the likelihood of earning more conversions. You’re already one step there by using a paid ad, which is considered an intervention. But from there, the uplift model looks at more specific characteristics of how that ad affects how people interact with your site and content. It may look at the types of ads you share, their content, and any additional interventions you use on your site, like pop-ups or retargeting, to affect how people browse your site.
While any company that uses paid advertising can use predictive methods to guide its strategy, companies, in certain industries may benefit more than others. Here are some of the industries that see the best results from predictive advertising:
Predictive advertising for the automotive industry can help companies learn which audience segments are most likely to buy which vehicles. It can also tell you where they’re most likely to buy them. For example, car manufacturers likely sell more convertibles in the southern and western United States rather than in the northeast. Weather and practicality both play a role in sales. But what if there’s a niche market for convertibles in Pennsylvania? Predictive advertising can help these companies learn that information and advertise accordingly.
Related: Your Guide To Digital Marketing for Automotive Businesses
B2B marketing can be tricky, especially because you have to appeal to a team of decision-makers rather than one consumer. Predictive advertising can help. By learning who your decision-maker segments are within a company, you can develop different messaging and ad placements to target each one.
For example, you may create ads that target the CEO of the company who gets the final say in making a purchase. But you could also develop other ads for social media or emails that come to lower-level team members. You might try to convince them to bring up your products or services in their next meeting for a team discussion.
Healthcare, pharmaceuticals, and wellness products are things that people only think about when they really need them. Similar to the life event micro-moments we discussed, health and wellness companies can take advantage of meeting people where they need their products and services. The more they learn about audience segments that share certain conditions, symptoms, or search intent, the better they can get at placing their ads on the right channels.
Retail companies, especially those that target a variety of audience segments, can find plenty of value in predictive advertising. For example, stores like Target or Sam’s Club offer goods in a variety of different segments, from food to furniture, and even clothing. That’s a lot of different audience segments and competitors to consider when doing paid advertising. By engaging in predictive advertising, these companies can learn about all their audience segments and how to target each one best without wasting time and resources to collect and analyze the data.
You still might wonder, “all right, but how does predictive advertising work in action?” Here are some of the ways companies use predictive advertising to reach their brand goals and better connect with their audiences:
Many companies use predictive advertising to forecast customer behavior in a variety of situations. To return to discussing weather data, looking at the forecast in certain locations can help advertisers know which content to push based on where their audience lives and works. For example, a clothing company may take advantage of real-time browsing data and weather forecasts to determine whether to share advertisements for winter jackets or jean jackets with different audience segments.
Because predictive advertising tells marketers more about their audience segments, data collection helps them create better messages for each one. When you know who fits into each audience segment (and who doesn’t) you can review their similar characteristics. Then you can research what words, images, and products appeal to them so that you’re always sharing the right information with the right people.
Though predictive advertising is mostly for paid campaigns, the insights you collect can help with any marketing strategy. Use the data you collect from across sources to find themes and patterns in how your audience behaves online. Then apply those insights to your SEO, content marketing, and other efforts to get more mileage out of your research.
Omnichannel marketing is the concept of creating a seamless user experience across all your marketing channels. It relies on hyper-personalization and a deep understanding of branding and brand voice to make it work. Predictive advertising can help you learn more about what your audience wants and needs to influence that hyper-personalization marketing. Then you can decide how best to create seamless ads across all your platforms, such as social media channels, search marketing, and display ads.
You can also use predictive advertising to scope out new audience segments and potential leads. For example, companies launching a new product or service may not quite understand the market for them right after launch. If you haven’t done any research, you may end up marketing products to the wrong people, making the entire effort a waste.
But predictive advertising can help you review data from comparative products and services your competitors offer. It can target lookalike audiences and develop profiles to help you understand who’s going to be most interested in these new ads and where they want to see them.
You can use predictive advertising methods throughout the sales funnel to target and retarget your audience based on their current wants and needs. For example, the analysis may help you figure out how to develop the right brand awareness ad for the top of the funnel. This might get a lead to click your ad and visit your site, but it doesn’t lead to a conversion.
But once they’ve visited the retargeting can begin. You can learn more about these audience members to target them again at key moments in their customer journey. From comparison shopping to looking for a place to purchase an item, you can use search ads and other paid marketing tactics to put your products and services in front of their face when they’re ready to move from one funnel stage to another.
Predictive advertising is a great strategy framework to use if you engage in any kind of paid marketing. Not only does it help preserve your budget, but it tells you as much as possible about your target audience and their behavior. The more you know, the more you can apply these insights to other areas of your brand from marketing to research and development. Because predictive advertising is an ongoing process, you can always update your findings with new data to make sure you’re pulling from the most recent and trustworthy sources to get the best results.