Can you imagine a world without cookies? Fortunately, we’re not talking about everyone’s favorite sweet treat. We mean the cookies that follow you around the web! With regulations all around the world closing in on such practices and Google’s plan to phase out third-party cookies by 2025, we may soon witness the extinction of these not-so-yummy cookies. In fact, many browsers have already gotten rid of them. In its place, predictive audiences are quickly becoming the alternative. But what exactly are predictive audiences? In this article, we’ll explain just that and explore its benefits and role in the future of advertising.
What Are Predictive Audiences?
Predictive audiences are audiences that have been identified and segmented using predictive analytics. This type of analysis uses existing data to form insights into what users will do next, not what they have done. In fact, using this as the campaign’s target market, you’re focusing on users who haven’t performed any action or exhibited any behavior that would have qualified them for the next stage of the campaign. They are simply more likely to perform such actions in the future.
How Are Predictive Audiences Formed?
The model you use will determine how you target the campaign. To anticipate user preferences, the following are considered:
- Behavioral data: For example, a user frequently visiting an e-commerce site is likely to purchase soon. With an ad, predictive technology can give the viewers a little nudge to encourage the purchase.
- Contextual insights: User behavior can also be affected by time, location and the device used, among others.
- Artificial intelligence: AI collects and processes all the information to identify patterns and behaviors and optimize the campaign.
GA4 predictive audiences are formed with real-time data. This means that any changes in behavior in context are immediately taken into account.
Key Differences Between Predictive Audiences and Cookie-Based Targeting
We’ve summarized the three biggest differences between the two below.
- Data source: Predictive audiences are formed using your own data. Meanwhile, cookie-based tools rely on third parties.
- Privacy regulation compliance: Cookie-based targeting is banned in jurisdictions with strong data privacy laws, while predictive audiences are not.
- Tracking: Only the cookie-based targeting requires a user to be followed around the web.
They’re practically polar opposites in how they approach targeting! All their differences will be highlighted further in the rest of the text.
Benefits of Predictive Audiences for Advertisers
Change is never easy. And with cookie-based targeting’s long-running reign, switching can seem challenging. However, using predictive audiences is worth it. Apart from the fact that it will become the norm very soon, there are a lot of benefits associated with its use. We understand that it may take some time to become accustomed to predictive audiences. The good thing is that you can start learning now and enjoy these advantages right away.
Improved Privacy Compliance Through Reliance on Anonymized Data
Major markets around the world have already outright banned the use of cookie-based tracking to gain insights. However, the new method of identifying the target market — predictive audiences — is completely in compliance with privacy laws such as GDPR and CCPA because it uses anonymized first-party data. Meanwhile, any competition that can’t keep up may be driven out of the market.
Enhanced Accuracy and Personalization Without Third-Party Data
When you think about it, following someone around the web doesn’t make sense. Even if you’re interested in working out, it doesn’t mean that you want to hear about yoga mats while reading about your favorite celebrities. Predictive audiences use contextual and behavioral signals to deliver a more personalized experience and messaging. So, even with third-party data out of the picture, you’ll still be able to accurately target your ideal leads.
Future-Proofing Advertising Strategies Against Privacy Regulations
With growing concerns surrounding privacy on the internet, using predictive audiences becomes even more valuable. This way of identifying the target for your campaign doesn’t rely on loopholes that can potentially be closed any time soon. With the formation of predictive audiences, user data is completely anonymized. So, even if strict regulations are introduced later on, you don’t have to worry about shifting gears again. Unless superior technology comes along that somehow makes predictive audiences obsolete, you won’t have to change the way you manage campaigns again.
How Do Audiences Analytics Work?
Below is a simplified version of what happens with audience analytics.
- Data sources are collected: As discussed in the previous section, forming predictive audiences need first-party data, contextual signals and behavioral insights. Third-party data can technically be used, too, but doing so isn’t as popular because many jurisdictions don’t permit this practice.
- Artificial Intelligence processes the data: AI identifies patterns, creates market segments and uses analytics to create predictive audiences.
- AI machine learning is trained and retrained: As more data comes in, predictive models become better over time.
The third step is continuous. Data is updated in real-time to account for new engagements from the current campaign.
Predictive Advertising Examples
We have discussed at length how this mode of identifying your campaign’s target market is better. But what does it really look like? Here are a few examples of what using predictive audiences could involve:
- Showing hoodies or casual sneakers to a user who often purchases casualwear;
- Advertising gym memberships or meal plans to someone who looks up fitness tips;
- Suggesting nature-based documentaries to someone who watched a show about penguins.
In all these cases, you’re preempting the interest of the user in what you’re promoting. It can be likened to a server who gets you milk just in case you find a dish too spicy.
Integrating Predictive Audiences into Advertising Strategies
The audience that you target for your campaign is only a part of the picture. So, while it’s necessary to identify the people who are likely to gravitate toward your brand and message, creating predictive audiences isn’t enough. They still need to be integrated into the bigger strategy-making process. This section will be dedicated to providing you with all the information to get started with predictive behavioral targeting, from the best practices, tools and advertiser-publisher collaboration. With your understanding of how to properly include predictive audiences, you’ll have a higher chance of running a successful campaign.
What Are the Best Practices for Running Predictive Audience Solutions?
Even though it’s still in its infancy, there are already practices that have worked well for others. It’s clear that predictive audiences have a lot of potential. However, this potential will only be transformed into conversions and dollars if you make the right moves. So, regardless of the industry you’re in, remember that your chances of success can improve or diminish based on what you do with this information.
Use High-Quality Data
In creating predictive audiences, high-quality data can ensure the reliability of the outcome. First-party data, such as what you have collected yourself from CRM or website analytics, is seen to have the highest value. Since you’re the source of the information, you don’t have to worry about how trustworthy it is. Most importantly, you can be sure that data from people-based audiences is clean, anonymized and complies with relevant regulations.
Don’t Just Take the Resulting Audience As Is
Getting AI to do all the analysis for you is great. However, as efficient as it can be, it still has its flaws. So, the predictive audiences that it generates may not necessarily reflect the best or most receptive market for your offer. So, while it is a good starting point, you should still monitor real-time data. For example, if the segments of the predictive audiences that GA4 created don’t respond well to the ad, you should make adjustments to get better outcomes.
Implement Centralized Data Management
Your campaign will have multiple touchpoints for your predictive audiences. And even though this may happen on different platforms, you still need to put all the data in one place. The bigger the database that will be used for modeling, the more accurate the generated predictive audiences will be. Another thing that you want to focus on here is how often the information is updated. Given the capability of tools today, you should go for real-time updates so that any shifts in behavior are accounted for.
Which Tools and Platforms Can Create Predictive Audiences?
All the big players in the advertising industry have long been preparing for the upcoming phase-out of cookie-based targeting. These tools leverage powerful emerging and trending technologies, such as artificial intelligence, to help you reach the right audience without compromising the anonymity of the users involved. Below, we’ll discuss the tools that support creating predictive audiences. However, if the tools you’re using are not on the list, we recommend directly asking their customer support about the availability of such features.
Google Analytics 4
Even though the phasing out won’t occur until 2025, you can use Google Analytics to make the switch as early as right now. For any activated audience triggers GA4 (the latest version of Google Analytics yet!) identifies, the anonymous user is included in your target audience. So, what exactly are GA4 audience triggers? Examples include likely 7-day purchases and likely 7-day churn. Just like with regular cookie-based tracking, you’ll also receive audience insights.
Meta Ads
Facebook and Instagram are popular for their robust capacity to zero in on a target audience. Instead of the approach for identifying a predictive audience GA4 uses, Meta employs Lookalike Audiences. They use your existing customer base to identify who else may be receptive to your ads. Meanwhile, conversion API lets you use your existing database to improve the identification of predictive audiences and the optimization of ad delivery.
TikTok Ads
If you already know that your audience is on the younger side, TikTok ads are going to be useful. You’ll also be glad to know that the platform offers features related to predictive audiences. The platform allows targeting based on existing interests, behaviors and other user traits. What’s great about this platform is that it’s an endless stream of short-form videos. These encourage continued engagement, which is very effective for predictive audiences. If you already know the characteristics and interests of your most engaged target, then it will be easy for you to optimize your campaign here.
How Can Advertisers and Publishers Collaborate to Optimize Results?
Even though publishers are only selling ad space, it’s also in their best interest to help you get great results from your campaign. After all, the better the outcome their publishing platform generates, the more attractive it becomes to all advertisers. So, even though predictive audiences are set from your end, the ad space seller also has an important role in your success. By having them on your team, you’ll be working with a bigger dataset.
Access to Insights That May Not Have Been Readily Available
As reliable as first-person data may be, it’s not expansive. You’re limited to the traffic or impressions generated by your advertising assets. So, it may not paint a complete picture of who you should be targeting with your campaign. By receiving data from the publisher as well, you’re expanding your insights to include those who may not be familiar with your brand or offer. This helps in making predictive audiences more accurate.
Feedback Loops
Feedback loops are self-correcting measures for any system, including those for predictive modeling. By supplying you with additional data, machine learning models can refine their processes and considerations when creating predictive audiences. The performance data given to you can be used to see where AI made the right calls and identify where things may need adjusting.
Possible Platform-Specific Training
If you’re working with multiple publishers, there’s a high possibility that their audiences are not completely aligned. This means that the predictive audiences yielding the best engagement and outcome will not be the same. Variations in ad types should also be taken into account for this training. This way, you always get to maximize the results from predictive targeting. Let’s say that there’s a segment audience unique to just one publisher platform. Having platform-specific training means the AI learns better how to optimize for them.
Challenges and Limitations in Using Predictive Audiences
There’s no doubt that this mode of creating your target market is promising. However, it is not without its setbacks. We hope that what you learn here won’t deter you from exploring predictive audiences. Instead, use these as opportunities to work out how you can overcome the challenges, since the only way forward is to incorporate predictive audiences into your strategies. Understanding its weaknesses will also help you make better decisions for your campaign. A few suggestions for minimizing the impact of the limitation of predictive audiences will also be included here.
Access to Data
The main requirement for cookieless advertising is access to first-party data. If you don’t have enough coming in from your existing digital assets, the accuracy of the predictive audiences may be compromised. For your campaign, this can mean many things:
- Missing key market segments that will have engaged more with your content and ad;
- Needing to cast a wide net to find audiences that are interested in your brand or offer;
- Losing out to competition with a bigger market and database.
What Can You Do to Address This Issue?
Fortunately, the solution here is pretty simple: get more data! This can be done by:
- Collecting surveys and conducting interviews;
- Collaborating with partner publishers to widen your database;
- Developing more assets where you can gather data.
The great thing about all these solutions is that it does more than improve the quality of the predictive audiences that you get. The information you gather can also contribute to your efforts in product development, boosting online visibility and even building a relationship with other players in the industry.
Transparency of the Algorithm
Algorithm transparency is a huge challenge in adopting audience modeling because it functions as a black box. This means that you feed it your input (in this case, your data), and then you get your output (the predictive audiences), but you don’t know what happens in the process. This lack of transparency poses a challenge for advertisers because it means putting all their faith in the algorithm. You have no solid basis on whether or not you’re making the right targeting decisions or if all possibilities are being exhausted in finalizing the predictive audiences.
How Can the Issue of Transparency Be Addressed?
Unfortunately, it’s not reasonable to expect complete transparency here. After all, the inner workings of the machine learning model are considered trade secrets. The next best thing is to direct your business to vendors who provide insights about how their technology works. Another approach is to test the effectiveness of the predictive audiences. While it only allows you to make targeting decisions after the fact, doing so will still help you get more out of your budget.
Need for Significant Investment in Technology and Expertise
Creating predictive audiences requires specialized technologies as well as the expertise to operate them. This can make it harder for smaller enterprises to move forward. With limited budget and resources, they may have to cut corners to remove these barriers. Of course, this almost always means subpar campaign results because they are in the dark when it comes to tech, data and potential profitable predictive audiences.
How Can the Investment Issue Be Overcome?
You can use any or all of these three solutions to address this.
- Slowly develop in-house resources and talent: Since predictive audiences are here to stay, this may be worth exploring.
- Use tools with built-in cookieless features: For example, Meta has these features, and they’re free to use. This is a good place to start. The advertising platform itself is also easy to use.
- Outsource the task: This can potentially be cheaper than developing in-house talent since you don’t need to pay them a full salary. Experts and agencies already have the resources, so they may be able to price their assistance more reasonably than hiring your own team.
The Future of Predictive Advertising: A Cookieless World
Cookie-based tracking is practically out the door, and we’re already seeing the initial effects in digital marketing that predictive audiences have been able to deliver. Of the many benefits, the ability to reach a high-intent audience is the biggest change that we’ve seen. Since predictive audiences are identified based on their potential actions, reaching users who are ready to act now is practically inevitable! The improved efficiency that you’ll get from your campaigns is made possible thanks to the emerging technologies that complement the use of predictive audiences. These innovations also change the relationships between advertisers, publishers and tech providers.
Which Emerging Technologies Complement Predictive Audiences?
The effectiveness of a predictive audience is enhanced by these innovations.
- Clean rooms: Clean rooms allow various contributors to put all their data in one place while still ensuring the anonymity of any user. This allows contributors to collaborate in just one place.
- Native advertising: Predictive audiences are already formed partly from contextual insights. So, with native advertising, the ads presented to the user are based on the content they’re consuming. That’s what makes it a great match,
- First-party integration: The generated predictive audiences are first-party data insights. Platforms that support the integration of such data allow you to widen the database from which further analyses are made.
The Evolving Relationship Between Advertisers, Publishers and Technology Providers
These three key players are expected to work more closely with each other to maintain their effectiveness in their respective roles. Predictive audiences are inherently privacy-centric, and this is reflected in the actions and interactions among advertisers, publishers and technology providers. Between advertisers and publishers, there may be more collaboration to ensure that the predictive audiences captured through machine learning models help get the desired outcomes. Meanwhile, marketing tech providers will continue to innovate to respond to the changing needs of their clientele.
Master the Use of Predictive Audiences and Attain Sustainable Success
There’s no denying it: the world is finally catching on to the intrusive practices of cookie-based targeting. With the old ways almost out the door, it’s best to prepare yourself for the future. Predictive audiences will soon be the norm. By pairing this with native ads, you can create a contextually relevant and engaging campaign that still protects user privacy. If you want to see the magic that predictive audiences and native advertising can make, sign up on MGID. Access everything you need to make this future-proof targeting method a success — high-level tools, creative specialists and a personal manager dedicated to your campaign. Let’s see the magic we can create with predictive content targeting, shall we?
