Scalability
At Fluency Engineering, scalability through ad automation is one of our core principles. Our tools enable advertisers to launch and manage strategies across their entire portfolio. But when you’re the strategist managing hundreds of accounts, how do you make sure it all runs smoothly? Fluency’s Notifications and Anomalies point out edge-case scenarios that can be affecting your portfolio. We recently added a new notification to identify a problem faced by many Facebook marketers, Extended Learning Phase.
What is learning phase? It’s Facebook’s way of telling you their algorithms are figuring out the lowest hanging fruit in your target audience - the ones most likely to act on the optimization event in an ad set. It takes about 50 clicks (or other optimization event) for the algorithm to optimize. Large audiences, such as Interest Targeting or Lookalikes, can reach these goals faster because of the large sample size. Smaller audiences often have trouble getting out of learning phase. Learning phase can be reset when you make significant edits, such as large budget changes, any targeting change, and most creative changes. You pay a performance penalty while in learning phase, so it’s best to get out of it as quickly as possible and stay out.
Let’s not be afraid of it though - It’s more important that your budget, targeting and creative are accurate than it is to stay out of learning phase. What we want to avoid is excessively retriggering learning phase, over-segmenting ad sets, and using small audiences in these optimization campaign types. When you’re managing advertising at scale, Fluency can help you identify these anti-patterns.
Let’s take a look at some examples.
