Do you want to use cutting-edge technology to target audience segments most likely to convert? Consider using predictive advertising. 

In this article, we'll review the ins and out of predictive advertising to learn why this method is becoming a go-to strategy for the savvy marketer. 

Predictive advertising is a broad term that combines software with predictive analytics, AI, and machine learning to field ads to ideal customer segments. 

Put simply by Sam Gratton, one of our senior campaign analysts; marketers use this method to reach their target audience where, when, and how they are most likely to be receptive to ads. 

Predictive advertising methods can be effective time savers. Traditionally, marketers had to dig through massive amounts of data to gain worthwhile insights. Predictive advertising software streamlines this process. Even the most experienced data scientist on the planet cannot sift through the data with the efficiency of these programs. Well, maybe they could. But it would take a lot of time and caffeine. 

The software utilizes the following three elements to run predictive ads: 

Predictive Analytics: Looks at data, builds statistical models, and predicts future outcomes.

Artificial Intelligence (AI): Technology that allows computer systems to perform regular human tasks. 

Machine Learning: This is a branch of AI that helps the software to learn progressively over time.

Predictive advertising uses data science to predict which campaign actions and strategies are the most likely to reach a specific audience segment. It all starts with choosing a platform. Most commonly, Google Analytics or Meta Business Suite. 

After choosing a platform, marketers plug historical data into the machine learning software. Then, the software performs predictive analysis on the data using AI. It also pulls external metrics to forecast future trends and analyze consumer behavior. 

Then, the ad campaign runs programmatically using the analysis data. This process is omnipresent and constantly developing throughout the remainder of the campaign. 

When Sam explained this process to us, he noted that some marketers execute predictive ad campaigns by leveraging third-party vendors that bucket users into audience segments. Using a third-party vendor allows them to run multiple ads to different ad groups and ensure they are serving the most relevant ads based on the users' past behavior. 
The process continues. Machine learning in the software tailors algorithms to optimize the campaign further. The goal here is to optimize constantly to give the ad campaign its greatest chance of success.

So, why use predictive advertising in the first place? According to Sam, advertisers not leveraging these tools and capabilities of predictive algorithms are spending money and serving impressions blindly. There are several reasons marketers incorporate predictive advertising into their campaigns:

Understand Consumer Behavior

The insights give marketers a better understanding of their consumer's interests based on historical interactions. Understanding the consumer allows marketers to serve targeted messaging when individuals are most likely to convert. Predictive advertising helps break through constant ad noise to improve the consumer experience.

Prioritize Consumer Leads

Predictive analytics allows marketers to qualify and prioritize leads. Analytics predict how likely a consumer is to convert, enabling marketing teams to devote attention to audience segments closer to conversion. The process minimizes wasted ad dollars on consumers who have not historically responded to campaigns. 

Increase Customer Lifetime Value

Finally, marketers can better understand their consumers' needs with predictive ads. In turn, companies can build products that better match their customers' interests. It's a win-win. 

The main challenges with predictive advertising for agencies are experience and time.

If an agency wants to use predictive advertising, they must ensure that they have a member on their team or programmatic partner with experience in the field. 

Programs need time to develop the models of data for analysis. It can be a slow process, and a deep understanding of the nuances of these programs is necessary for optimal results. 

Finally, predictive advertising is not a solve-all solution. Although, when executed property, it is a very effective addition to a full-funnel marketing strategy. 

Agency and in-house teams should consider using predictive advertising because it promotes relevance. 

An example from Sam:

"If I am on the web checking my fantasy football team and looking at flow reports of the Smith River all while watching the Yankees on my phone, I want to be served relevant ads. 

Imagine Instagram served me two ads: a predictive ad for the new 2023 Maravia Catarafts and a non-predictive ad for Tampax feminine care products. Based on my web and phone browsing behavior, do you think you can tell me which one I am going to choose? Unsurprisingly, it will be the predictive Maravia ad that analyzed my behavior and served me a relevant ad."

Although this example may be extreme, this happens every day. Marketers who are not running predictive ads are at the risk of fielding ads to irrelevant individuals. 

By investing in predictive ad methods, marketers give their campaigns the greatest chance of reaching individuals most likely to convert. 

At Pathlabs, we work with our agency clients to pinpoint the best audiences for their campaign objectives.

Then, we set up our campaigns on various platforms that use predictive analytics to maximize the potential of every impression.

Unlike the example above, we aim to minimize wasted impressions to maximize budget, ad spend, and ROI. 

Are you ready to explore the benefits of working with a digital media agency partner? Then, get in touch with Pathlabs to learn how we can become an extension of your media team.