Impact of Artificial Intelligence: Adapting to LinkedIn’s Discontinuation of Lookalike Audiences
As the digital marketing landscape continuously evolves, staying informed and adaptive is crucial for success. A significant change is on the horizon for marketers utilizing LinkedIn’s advertising platform. Effective February 29, 2024, LinkedIn will discontinue its lookalike audiences feature, a tool many of us have relied on to expand our reach and connect with similar prospects.
Understanding the Change:
LinkedIn’s decision to phase out lookalike audiences marks a significant shift. From this date, new lookalike audiences cannot be created, and existing ones will not be editable. Existing lookalike audience data will become static, meaning they won’t refresh, turning your dynamic lookalike audience into a fixed dataset. While active campaigns using lookalike audiences will continue with the static data, the ability to dynamically match audience profiles based on current trends and data will cease.
Why the Shift Matters:
Lookalike audiences have been a cornerstone in digital marketing strategies, enabling organizations to reach users similar to their existing customer base. This shift demands a strategic rethink for marketers, especially those in the association and event sectors, who often rely on precise targeting to connect with niche audiences.
Embracing New Alternatives:
1.Predictive Audiences: LinkedIn suggests the use of predictive audiences as an alternative. These leverage LinkedIn’s AI capabilities combined with your data sources, like Lead Gen Forms, contact lists, or conversion data, to form audiences that are likely to convert. This tool offers a more data-driven approach to audience building, ensuring high-intent targeting for your campaigns.
2.Audience Expansion: Another recommended approach is Audience Expansion, which works with Matched Audiences and LinkedIn’s attribute targeting. This option broadens your campaign’s reach by including profiles that share similarities with your primary audience, based on attributes like skills or interests.
Transitioning to Predictive Audiences:
Shifting to predictive audiences involves understanding their mechanics. These audiences use your specific data sources to identify potential leads with high conversion likelihood. However, there are constraints to consider, such as the limitation of 30 predictive audiences per ad account and the inability to share these across accounts. Additionally, Audience Expansion is not available for campaigns using predictive audiences.
Action Plan for Marketers:
1.Audit Your Current LinkedIn Strategy: Assess how reliant your current campaigns are on lookalike audiences. Understand the impact of this change on your targeting strategy.
2.Explore Predictive Audiences: Familiarize yourself with predictive audiences, their requirements, and how they can be integrated into your marketing efforts.
3.Data-Driven Audience Building: Ensure your data sources meet the criteria for creating predictive audiences. This might involve combining multiple data sources or optimizing your data collection methods.
4.Test and Learn: As with any new tool or strategy, it’s important to test predictive audiences and analyze their performance against your marketing objectives.
The Bottom Line:
LinkedIn’s discontinuation of lookalike audiences is a reminder of the ever-changing nature of digital marketing. While this change poses challenges, it also opens up new opportunities for more data-driven, AI-enhanced audience targeting strategies. By embracing these new tools and adapting our approaches, we can continue to effectively reach and engage our target audiences.
A thought-leadership piece written by the CEO of Kabloom, Richard Torriani.