Zilke has developed a particularly effective democratic republic of the congo email list strategy she calls "similarity mash-ups." This approach helps combat the declining effectiveness of traditional lookalike audiences.
The process involves creating multiple lookalike audiences at different percentage levels (1%, 3%, 5%, 7%, and 10%) from a list of your highest value customers and combining them into an ad group.
When implementing this strategy, use the best data sources, such as buyer lists or high-quality leads.
A combined approach often makes the Meta algorithm perform better than a single lookalike audience alone. Zirker has found this approach to be particularly effective at recovering lookalike audience performance across industries and account types.
Monitor lead quality and conversion rates
One of the most critical aspects of using Meta’s AI capabilities is keeping an eye on lead quality and conversion rates. By analyzing large numbers of accounts, Zirker found that while AI-driven features may sometimes generate more leads, the quality of those leads can vary widely.
This is especially important with Advantage Plus Audiences, as you may see excellent lead generation numbers, but ultimately lower conversion rates. Zirker recommends implementing comprehensive lead tracking and maintaining comparative metrics between different audience types and targeting methods.