The NEW Facebook Lookalike Audience Approach

TL;DR

New strategies for Facebook lookalike audiences post-iOS 14.5.

Transcript

hi guys it's ben heath from lead guru and in this video i'm going to talk about a new approach to facebook lookalike audiences a lot of people have been complaining recently that since the ios 14.5 update was released and the changes that have accompanied that have been put into effect they've complained that they haven't seen as good results with ... Read More

Key Insights

• The iOS 14.5 update has significantly impacted the performance of Facebook lookalike audiences by reducing Facebook's ability to track user activity, leading to less effective audience targeting.
• To improve performance, advertisers should consider using larger source audiences for creating lookalike audiences, as more data helps Facebook's machine learning create more accurate matches.
• Testing different types of source audiences, such as add-to-cart events versus purchase events, is crucial to determine which performs better post-iOS 14.5.
• Uploading customer lists can be an effective alternative to using pixel data for creating lookalike audiences, especially since it allows for more extensive historical data inclusion.
• The shift in strategy involves moving from highly specific source audiences to larger, broader ones, which seems to perform better after the iOS update.
• Broader targeting and larger retargeting audiences are proving to be more effective in the current Facebook advertising landscape.
• Facebook's data limitations post-iOS 14.5 suggest a need for advertisers to adapt by providing more data to Facebook's systems to enhance targeting accuracy.
• Testing various source audiences, including video engagement and page interactions, can help identify the best performing lookalike audiences for specific business needs.

Questions & Answers

Q: What impact has the iOS 14.5 update had on Facebook lookalike audiences?

The iOS 14.5 update has significantly impacted Facebook lookalike audiences by reducing Facebook's ability to track user activity. This reduction in data has led to less effective audience targeting, as Facebook's machine learning relies heavily on data to create accurate lookalike audiences.

Q: How can advertisers improve the performance of lookalike audiences post-iOS 14.5?

Advertisers can improve lookalike audience performance by using larger source audiences, which provide more data for Facebook's machine learning to create accurate matches. Testing different source audiences, such as add-to-cart versus purchase events, is also recommended to find the best strategy.

Q: Why is uploading customer lists recommended over using pixel data for lookalike audiences?

Uploading customer lists is recommended because it allows for more extensive historical data inclusion, which can improve the accuracy of lookalike audiences. With the iOS 14.5 update reducing the effectiveness of pixel data tracking, customer lists provide an alternative way to supply Facebook with the necessary data.

Q: What shift in strategy is suggested for creating lookalike audiences?

The suggested shift in strategy involves moving from highly specific source audiences to larger, broader ones. This change is necessary because larger source audiences seem to perform better after the iOS 14.5 update, providing more data for Facebook's machine learning to work with.

Q: What types of source audiences should advertisers test for better performance?

Advertisers should test various source audiences, including add-to-cart events, purchase events, video engagement, and page interactions. By experimenting with different source audiences, advertisers can identify which ones perform best for their specific business needs post-iOS 14.5.

Q: How has broader targeting affected Facebook advertising performance?

Broader targeting has improved Facebook advertising performance, as it aligns with the current need for larger data sets to compensate for the reduced tracking capabilities post-iOS 14.5. Larger retargeting audiences and broader targeting strategies are proving to be more effective in the current landscape.

Q: Why is providing more data to Facebook's systems important post-iOS 14.5?

Providing more data to Facebook's systems is crucial post-iOS 14.5 because the update has limited Facebook's ability to track user activity. More data helps improve the accuracy of Facebook's machine learning, resulting in better-targeted lookalike audiences and overall advertising performance.

Q: What are the benefits of testing different source audiences for lookalike audiences?

Testing different source audiences allows advertisers to determine which ones perform best for their specific business needs. By experimenting with various options, such as video engagement and page interactions, advertisers can optimize their lookalike audiences for better performance in the post-iOS 14.5 advertising environment.

Summary & Key Takeaways

• The iOS 14.5 update has affected Facebook's ability to track users, impacting the effectiveness of lookalike audiences. Advertisers are encouraged to use larger source audiences to improve performance. Testing different source audiences, such as add-to-cart vs. purchase events, is advised to find the best strategy.

• Uploading customer lists can help create more effective lookalike audiences by including more historical data. This shift from pixel data to customer lists is recommended due to reduced tracking capabilities. Broader targeting and larger retargeting audiences are also performing better post-update.

• Advertisers are advised to test various source audiences, including video engagement and page interactions, to determine the best performing lookalike audiences. The goal is to provide more data to Facebook's systems to enhance targeting accuracy amidst data limitations post-iOS 14.5.