I have been following some fellow marketers run this strategy for a while and I had tested it out myself too a few weeks ago. I’ve seen decent results with it. Unfortunately, I didn’t have a lot of data to work with so I couldn’t test it at full scale but I believe in the concept for sure.
The strategy is called lookalike stacks and it’s dead simple to implement. You create 1% lookalikes of various options like 1% of view content, add to cart, initiate checkout and purchase and stack them together in 1 ad-set. You can do the same for 2%, 3% and so on.
There are two reasons why I think this strategy works better than most other lookalike configurations.
Firstly, Facebook prefers broader audiences over narrow audience. When you let Facebook work with broader audiences, it has more room to play with, find buyers, and generate cheaper sales in turn. When you stack 1% of everything together, the audience size is much larger than 1% of individual lookalikes. It is often 2-3 times larger. Sure you could individually use 3% of purchase or 3 % of view content to achieve same audience size. However 3% is never going to be as good as 1%.
Secondly, best lookalikes are created when you not only use more qualified events but also have a large amount of data in them. For example, if you have 1000 ATCs and 100 purchases, your ATC lookalike is likely to be better than your purchase lookalike because Facebook had 1000 people to create lookalike from. Although purchase is a more qualified event than ATC, it will create a more qualified lookalike once there are large number of purchases in the data-set. When you create a stack, you’re able to leverage from the best of VC, ATC, IC, Purchase together in 1 ad-set. This kind of ad-set is the best of both worlds as it has both: lookalikes of most qualified events (IC, PUR etc), and lookalikes of events with most data in it (VC, ATC etc).
I hope I was able to explain myself just fine. If I didn’t, please feel free to ask me questions.