How Predictive Audiences Transform Marketing Strategies

The Gist

• Privacy-first segmentation. Predictive audiences leverage first-party data, eliminate the need for third-party cookies and make data privacy compliance easier. 

• Ad campaign enhancement. Using predictive audiences in ad campaigns optimizes targeting with first-party data and improves engagement. 

• Evolving beyond lookalikes. Predictive audience technology refines segmentation beyond lookalike models, and it adapts to evolving customer behaviors for better ROI.

The process of segmenting customers into predictive audiences is one of the newest and fastest growing marketing strategies. Made possible by the combination of AI and machine learning, predictive audiences are fueled by powerful predictive analytics and allow marketers to drive greater engagement, personalization and sales.

However, getting predictive audience technology right for your company and customers requires thoughtful planning to adhere to consumer privacy laws and expectations.

Privacy is an enormous concern for people. According to a Deloitte survey, 67% of consumers said they’re worried about their privacy while using their phones. And despite the many regulations aimed at protecting consumers’ privacy online, one-third of the survey participants reported that they’d had their data compromised or been otherwise targeted during the past year.

The good news is that it’s not impossible to set up predictive audiences without overstepping privacy boundaries. It just takes a willingness to put some safety-minded internal marketing processes into effect.

Table of Contents

Move Away From Third-Party Data

Third-party data and cookies have sparked huge security debates. Since this type of data is obtained without consumer consent, it’s becoming less popular. Marketing teams that still rely on third-party data will find that it’s unnecessary if they’re generating predictive audiences.

Technology company Nativo explains that predictive audiences can be created solely from first-party data, which has been given freely by consumers. For instance, consider all the consumers who accept your website’s tracking terms or who provide their information in a form fill. They’re agreeing to exchange their data for a more customized interface with your brand. You can add their movements, feedback and other input into a larger data pool. As your data pool grows, you can develop more predictive audiences to inform your marketing initiatives.

Moving away from third-party data offers marketers the chance to gather customer data in a more transparent way. Plus, the data they collect may be more accurate since it’s coming through a first-party source.

Related Article: Third-Party Cookie Deprecation: Preparing for Marketing’s Future

Test Predictive Audiences for Ad Campaigns

There are many use cases for predictive audiences. Two popular ones are identifying customers at risk of churning and anticipating when a customer will purchase again. However, predictive audiences don’t have to be relegated for use only with existing customers. They can also be valuable when constructing and deploying ad campaigns, especially on Google.

In fact, Google now provides a Google Analytics (GA4) predictive audience option. Ad management provider eboost recommends getting familiar with it. GA4 has the ability to draw predictive conclusions from the data it gathers on a website, such as your corporate site. The data can then be used during your online ad content creation and targeting.

What’s especially attractive about creating online ads with the help of predictive audiences is that the strategy focuses on your website’s first-party and zero-party data. Again, this helps avoid the need for you to seek out third-party obtained information (which may have been taken without consumer consent or knowledge). It also promises that more consumers will see your ads at the right time, in the right place and with the right message.

Related Article: When to Use AI to Forecast Website Data

Replace Lookalike Audiences With Predictive Audiences

To further interact with customers on an individualized level, consider substituting predictive audiences for your lookalike audiences. Lookalike audiences are based on the concept that consumers will never change their buying patterns. But consumer behavior is always evolving, which means lookalikes can get outdated. Consequently, relying solely on lookalike audiences may lower the ROI of your campaigns.

Predictive audience systems are able to take lookalike audiences to the next level. Instead of assuming that a wide range of consumers will always follow the same sales journey, predictive audience technology is designed to get granular. This makes it possible to anticipate your customers’ next moves in a personalized way. Accordingly, you can develop content to reach them in the moment and keep them from leaving.

Both lookalike and predictive audience modeling are safer in terms of customer data privacy because of their reliance on opt-in and past purchasing data. However, only predictive audiences can see the full picture. As such, they’re far more suitable to helping marketing teams meet their current and long-term conversion goals.

Being able to segment audiences using AI and machine learning puts more power in the hands of marketers. As long as your team remains within the parameters of evolving data privacy needs and laws, you’ll be better poised to deliver personalized customer experiences without overstepping bounds.