Everything You Need to Know About Predictive Audiences

Predictive audiences are changing the way businesses connect with their customers

Cookies are on their way out, but there's no need to mourn them. Although they've been the lifeblood of digital marketing for years, third-party cookies have long posed a serious problem when it comes to data privacy.

Phasing them out is the best way to protect consumers, but many marketers wonder what comes next. If you're among them, you can rest assured that predictive audiences are an excellent replacement for cookie-based audience targeting.

Marketers who are open to reevaluating their strategies and adopting a cookieless targeting solution will quickly discover that they've gained far more than they've lost. Predictive audiences are privacy-friendly and highly effective. When used correctly, they can transform a reactive and inefficient marketing system into one that's forward-thinking and consistently ahead of the curve.

Why Are Predictive Analytics Important?

To fully understand predictive audiences, it's important to look at their basic foundation: predictive analytics. Over the past several decades, marketers have used predictive analytics tools and techniques to better assess what their customers might do and how well they'll perform in the future. In doing so, they've been able to solve problems faster and more successfully.

Kinds of Predictive Analytics

Depending on the industry and purpose, predictive analytics takes many different forms. However, marketers tend to use a specific subset of predictive models, such as:

  • Clustering models: This kind of model places customers into categories based on different variables. The two primary forms are behavioral and product-based.
  • Collaborative filtering: These models are commonly used by businesses such as Amazon, Netflix, and Spotify to make recommendations to customers. They analyze user behavior to suggest things that users are likely to find appealing or interesting.
  • Time-series modeling: Time-series models look at data at specific frequencies, such as daily or monthly, to identify patterns or trends based on timing. They're useful when trying to predict when customer demand will be especially high, among other things

Each of these predictive models plays a part in a business's overall marketing strategy. They can help an organization reduce spending, increase brand awareness, and create a seamless customer experience.

Predictive Analytics in Action

There's a reason businesses rely so heavily on predictive analytics. It improves outcomes and encourages stability in what might be an otherwise extremely turbulent market. Looking at practical examples of predictive analytics underscores how vital it is to businesses.

One of the most familiar and common predictive analytics examples in real life is credit scoring. Banks and lenders calculate a score using historical information about credit card use, late payments, and loan accounts. This score indicates how likely it is that they'll make their payments on time moving forward.

This same technique translates to marketing, where businesses use lead scoring systems to evaluate different prospects and determine which ones are most and least likely to convert. Those insights guide their decision-making when it comes to dedicating time and resources to pursuing individual prospects.

What Are Predictive Audiences?

Although they're a relatively new development in the world of predictive analytics, predictive audiences have taken cues from lead scoring and are revolutionizing audience targeting. As the name suggests, predictive audience models create audiences based on predicted actions customers will take in the future, such as removing themselves from an email list or making a purchase.

What Powers Predictive Audiences

Predictive audiences are possible because of technological progress that makes customer segmentation much more detailed and specific than it has ever been in the past. This type of targeting relies on artificial intelligence (AI), machine learning, and first-party data rather than third-party cookies. Advanced machine learning algorithms build predictive audiences based on customer behaviors, content engagement, preferences, and spending habits.

For example, a company could build an audience of customers likely to purchase in the next week. This prediction is based on how frequently those customers have visited a sales page, read articles, or opened emails in a certain period in the past.

Comparing Different Approaches to Audience Targeting

Businesses that are new to predictive audiences might find it difficult to see what specific benefits they offer compared to the systems they already have in place. Distinguishing between third-party targeting, contextual targeting, and predictive audiences can help you decide which is the best option for your organization. 

Until recently, third-party targeting was the norm in digital marketing. Businesses would purchase demographic and psychographic information from data intelligence companies and use it to target certain characteristics, such as age, income level, and hobbies. However, gathering this data usually involves third-party cookies, which are widely seen as problematic in terms of consumer privacy. 

Contextual targeting, on the other hand, doesn't use third-party cookies. It focuses on aligning ads with the content and features of the website where they will appear. For example, a website for running enthusiasts might feature ads for sports shoes. 

Predictive audiences combine elements of both these approaches. Like contextual targeting, they don't rely on third-party cookies, but they do allow you to segment audiences. As a result, predictive audiences can often outperform contextual targeting and 3rd party data without the restrictions of cookies. 

Why Predictive Audiences Are Replacing Cookies

To some degree, predictive audiences emerged by necessity. Apple put limitations on data collection several years ago, and Google announced in 2023 that it would gradually phase cookies out. However, forward-thinking marketing specialists have also recognized for some time that there was a better way to target customers.

Predictive audiences are much more tailored than cookie-based targeting strategies. They use customer actions rather than attributes, such as age, gender, or location, to guide marketing decisions.

For example, sending a blanket email to everyone who has visited your website in the past month is inefficient and unlikely to yield a lot of conversions. A far better option is to send personalized emails to a predictive audience of people most likely to purchase.

This approach to targeting also allows businesses to engage with customers in a more proactive way. They don't have to wait for customers to complete an action and then react to it. Instead, they can reach out to them in advance, priming them to follow through on a desired action.

How Do Predictive Audiences Work?

Predictive audiences use cutting-edge technology to analyze customer data. Machine learning algorithms look at a wide range of factors and use them to assign customers and prospects to certain categories.

Types of Predictive Audiences

The overall goal of predictive audiences is to anticipate what your target audiences will need and want in the future. This, in turn, empowers you to deliver more effective messaging.

There are a few different predictive audience methods marketers can use, including:

  • Propensity modeling: This type of predictive audience gives you insight into how likely it is that a customer will take a certain action, such as making a purchase. This information is valuable because it helps you tailor your marketing efforts to the people who are most likely to convert or continue their relationship with a business.
  • Customer lifetime value: When focusing on customer lifetime value, a predictive audience can help you determine how much money you might earn from a customer over time. This is vital information when comparing which customers receive the highest priority and the greatest investment of resources.
  • Time to reorder: Predictive audiences can also tell you when a customer might need to order additional items. This helps you design more effective promotions centered around those specific products.
  • Churn modeling: With churn modeling, you'll develop a predictive audience that indicates whether a customer's past and current behavior indicates that they'll leave your company. When you know which customers are most at-risk, you can take pre-emptive action and encourage them to stick with your brand.

All these predictive audiences are possible because AI can analyze contextual signals an existing audience provides. However, predictive audiences use first-party data, such as a business's website or publications, unlike cookie-based marketing.

How to Create an Audience

Although the idea of predictive audiences is relatively straightforward, the technology behind them is complex. Marketers who haven't used them before might be unsure where to start.

Fortunately, predictive audience solutions can do most or all of the work. They use a predictive AI model to analyze data and gauge the likelihood that a customer will take a particular action in the future.

All you have to do is choose how you want to segment your audiences. You might separate them based on predicted future purchases, unsubscribes, or some other action that's important to your company.

For example, when a customer engages with content on your website, the system can use that information to assign them a score. A higher score means that they're more likely to take certain action in the near future. It can then group together customers with similar predictive scores, resulting in a new audience.

How Do Predictive Audiences Differ from Lookalike Models?

Marketers who aren't yet familiar with predictive audiences sometimes use the term interchangeably with lookalike audiences. While it's true that both models use data to identify and segment audiences, there are significant differences that set them apart.

Understanding Lookalike Audiences

To make those distinctions clear, let's dig deeper into lookalike models. This technique has been popular for years because it helps businesses build new audiences based on their existing customer data.

Using information about current audience preferences, demographics, and behaviors, marketers can target additional prospects who are more likely to need the same products or services. This can increase conversions and expand a business's reach.

However, lookalike modeling isn't a perfect system. It requires a substantial amount of historical data that new and growing businesses simply may not have available. In addition, it's based on the assumption that customers will continue to do the same things in the future that they've done in the past when that's often not the case, and it limits a marketer's ability to encourage new or additional behaviors.

Perhaps the biggest difference between predictive and lookalike audiences is the fact that, up to this point, companies have built lookalikes with third-party cookies. Lookalike models find cookies that share the same characteristics as existing customers and use them to build a new list of prospects. While popular in the past, that strategy will be seriously hindered when Google and other companies place restrictions on cookies.

What Makes Predictive Audiences Different

Compare what you now know about lookalike audiences to predictive audiences, and it quickly becomes clear why they're dominating conversations in the marketing world. Rather than assuming that customers will continue to repeat the same behaviors indefinitely, predictive audiences give you a broader and more adaptable view of their future actions.

As a result, predictive audiences open new doors for marketers. Lookalikes involve building audiences that look like the ones you already have in terms of demographics and past behaviors. Predictive analytics, on the other hand, allows you to create audiences based on other factors, such as:

  • Product interest
  • Discount affinity
  • Lifetime value
  • Lifecycle stage

To put it in more practical terms, let's imagine a hypothetical scenario. You're using a predictive audience solution to evaluate a set of indicators, including recent engagement with your website and email open rates.

Based on that data, the model creates an audience of customers who purchased from your company in the past 30 days but haven't visited your website in more than six months. It also generates an audience of customers who made a purchase in the last month and visited your website in the past week.

Other targeting techniques and tools might not differentiate between those audiences because they would technically qualify as active. However, a predictive audience model picks up on their granular distinctions. This helps you decide how to approach the two audiences differently to achieve the best results.

For instance, you might be better served by allocating more of your resources to the audience that has made a purchase and is more actively engaged on your website. Their behavior tells you that they're high-value customers who are more likely to have a long-term relationship with your brand.

How Can I Use Predictive Audiences?

Marketers can put predictive audiences to work in many different scenarios. Whether they need to improve their retention rates or identify users, businesses can employ predictive models to achieve their goals.

Rescue At-Risk Customers

In the past, marketers have relied on static time periods to determine where people are in the sales funnel and on their customer journey. For example, a customer who made a purchase within the past month is active, while a customer who hasn't made any purchases in the past year is lost.

That approach is problematic because it ignores the reality that every customer's journey is different. As a result, a business might list customers as active when they're actually at-risk because they haven't hit a predefined time constraint. Companies often miss their chance to retain customers because they don't realize they're at risk until it's too late.

Predictive audiences remedy that issue by using a more varied combination of indicators to analyze customers, such as engagement with emails, ads, and web pages. This enables you to create messaging that they receive at precisely the moment when it's needed. At the same time, you'll avoid sending unnecessary and potentially annoying emails to customers who are happy where they are.

Find Your Next Audience

Predictive audiences offer a valuable tool for businesses seeking to identify and engage with a new audience. By analyzing patterns and behaviors from data sets you have, the predictive audience models can anticipate the preferences and interests of potential customers. This enables businesses to tailor their marketing strategies and reach out to demographics they may not have considered previously.  This proactive approach helps in finding the next audiences and also facilitates the expansion of market reach, fostering growth and diversification in your customer base. 

Minimize Unsubscribes

In a similar vein, predictive audiences can also help you lower the number of customers and prospects who hit the unsubscribe button on your emails. While a certain number of unsubscribes is inevitable, too many of them can quickly become a drain on your marketing budget.

Rather than sending out blanket emails and hoping they hit their target, generating predictive audiences allows you to tailor messages to specific kinds of customers. You'll have an idea of whether they're likely to open, click, or unsubscribe when they receive emails and can approach them appropriately.

For instance, you might exclude individuals who have a high likelihood of unsubscribing from upcoming emails. This isn't an attempt to alienate them, but rather a way to reduce their chances of leaving your email list.

Before sending them additional messages, you can look for other ways to increase their engagement with your company and build a stronger relationship. Once they're in a better place, you can resume sending them emails and monitor how it affects their engagement levels.

Send Targeted Product Recommendations

Another use case for predictive audiences is making specific, relevant product recommendations to customers. Because you have access to information about their preferred products and affinities, you can limit your recommendations to items that they're more likely to need and enjoy.

This means that customers won't have to waste time clicking and deleting messages that have no relevance to their lives. They'll appreciate receiving information about products that are well-suited to their preferences. This increases the chances that they'll purchase the items you recommend and encourages them to explore your other offerings.

How Can Predictive Audiences Help Me?

Even though marketers realize that the days of cookies are almost over, many of them feel unprepared to shift to a new strategy. That may be, in part, because they don't realize what they can gain with predictive audiences.

Companies that are already using them see enormous benefits from multiple angles. Some of the biggest ones include:

  • Cutting costs: Predictive audiences allow you to direct your money and resources to fewer consumers and invest in those with the most value. You can typically achieve a lower cost per conversion and per click compared to third-party targeting. Additionally, it removes the extra cost of using a 3rd party data set that an advertiser typically has to pay for. 
  • Maintaining customer loyalty: Knowing your customers' preferences and responding based on those insights fosters a more positive relationship and a more solid sense of trust. Customers will feel compelled to stick with a company that sees and understands their needs.
  • Increasing click-through rates: Predictive audiences could be the way to go if you want customers to click on your ads rather than ignore them. Ads that are tailored to a customer's predicted actions and interests will naturally be more appealing to them.
  • Decreasing data input: Predictive audiences don't require cookies to function. They also involve less overall data than lookalike models, which require a substantial initial dataset to work effectively.
  • Speeding up your processes: The days of spending hours or days manually analyzing customer data are long gone for most companies. Because machine learning and AI take on the bulk of the work, predictive audiences can accelerate your marketing efforts and each customer's journey even further.

These advantages and the ability to quickly switch from cookies are excellent reasons to integrate predictive audiences into your marketing plan.

How Do You Use Predictive Audiences in GA4?

Google is just one of many companies that are leveraging the power of predictive audiences. Specifically, Google Analytics 4 (GA4) includes predictive metrics that companies can use to build audiences.

GA4's Predictive Metrics

The predictive audiences feature in GA4 depends on three metrics:

  • Purchase probability: This metric looks at the likelihood that a user who was active in the last 28 days will convert in the next 7 days.
  • Churn probability: This is the probability that a user who was active at some point on your app or website during the last 7 days won't be active within the next 7 days.
  • Predicted revenue: This metric predicts how much revenue a business will earn over the upcoming 28 days from an active user in the previous 28 days.

Businesses can use one or more of these metrics in the audience builder. It allows them to segment users with set metrics, dimensions, and event data that they select.

Suggested Predictive Audiences

Predictive audiences have to meet at least one condition based on one of these predictive metrics. GA4 offers templates for five predictive audiences:

  • Likely 7-day churning purchasers
  • Likely 7-day churning users
  • Likely 7-day purchasers
  • Likely first-time 7-day purchasers
  • Predicted 28-day top spenders

However, businesses aren't limited to these pre-set audiences. They can also create custom predictive audiences based on the tool's predictive metrics.

Using Audience Triggers

In addition to targeting, you can also use predictive audiences in GA4 to evaluate the performance of your marketing efforts. First, you set a condition that users have to meet to fall into a specific audience segment. When a new user meets one of those conditions, GA4 automatically adds them to the existing audience and can trigger an event.

Audience triggers allow you to track specific behaviors, such as spending a certain amount of time on your website or clicking on a particular page. This is a good way to evaluate how well you're meeting your targets and whether you need to refine your strategies. You can also monitor audience growth using the event reporting feature in GA4.

Go Cookieless With Nativo

Predictive audiences are changing the way businesses connect with their customers. They use innovative technology to analyze existing customer engagement and find other pools of quality prospects — all without a single third-party cookie.  

At Nativo, we're proud to help businesses respect their customers' privacy without weakening their marketing strategy. Check out our on-demand demo to see how Nativo Predictive Audiences can help you expand your reach and increase conversions, all while cutting your acquisition costs.


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