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.
One of the things that people tell me is that when they run ads they get a lot of irrelevant traffic or leads although they are confident that their targeting is accurate. When you’re selecting a large interest with an audience size in millions, you’re obviously going to reach many irrelevant people just because of the sheer size of the audience.
One of the things that we do to improve this traffic quality as well as our conversion is to do case studies on the pain points and their solutions. For example, if you’re trying to sell a SaaS subscription, instead of trying to reach your potential customers directly with the ad of your product, you should do an ad of the case study.
If your product is an e-commerce product discovery tool, you should do a case study about “how a store owner made $37,000 with this product discovery strategy”. Once you run an ad for this case study, you’ll be able to collect very relevant clicks. You can then retarget this traffic with your product ad. You could also create a lookalike of this case study audience, and then run your product ad for them.
The more expensive your product is, the more number of case studies I recommend you to do.
Super lookalikes isn’t officially a type of audience by Facebook. It’s just a term used by performance marketers to describe lookalikes made using top percentiles of the audience.
This kind of custom and lookalike audience is generally not publicly made available by Facebook but it’s kind of a hidden gem. If you don’t know about lookalike audiences, please check this out. And if you do, read ahead.
This is where you generally create or find your custom and lookalike audiences
But super lookalike audiences are created here.
Using the percentile option under activity in analytics section
You can create a filter to reflect the top percentile of your readers or buyers and save as custom audience
You can then use this custom audience to create lookalikes using the standard way. I call these super lookalikes.
Because results for super lookalikes speak for themselves.
Facebook has very advanced machine learning capabilities. More often than not, you’re better off reaching your customers for a cheaper cost by reaching a broader audience instead of a narrow targeted audience. But how is that possible? In theory, targeted audience should work better? But with strong ML, the broader audience delivers better and cheaper results provided that the initial customer dataset was correct.
But what happens if you get the initial data wrong? It puts their ML chase your customers in the wrong direction. Let me explain.
When building a Facebook page, growth is going to depend a lot on you first 100s or 1000s likes. Hence getting your first subscribers or customers wrong, can put you altogether in the wrong direction. I can think of 2 reasons why that could happen. Firstly, your upcoming page subscribers are likely to come from the network of your existing subscribers due to sharing and other engagement. And secondly, the engagement behavior of the first data set of subscribers with your content will define how engaging your page is and eventually define the placement of your page in the newsfeed and other Facebook algorithms.
So getting the initial dataset of subscribers/customers is extremely important. It is why I’m generally way more careful in the start when building a Facebook page or an e-commerce store through Facebook ads but later on take the liberty to test all kinds of traffic. It keeps my seed-data clean. The data that is going to be used to build the entire user-base later on.
If you have a question, please feel free to ask in comments.
I have yet to meet a performance marketer who has not fallen in love with Facebook’s LookAlike audience feature. If you provide minimum data (100 users) to Facebook about your leads or customers, it can find more potential customers for you that lookalike your seed data.
It doesn’t just sound sexy. It works. It works wonders. It’s the most amazing feature I’ve seen on any ad platform thus far. But you can make it even more amazing by following a simple trick.
If you have worked for even a few weeks in the internet marketing industry, you’d be aware that the advertising marketplaces work on bidding and competition. Since more and more people are trying to reach customers in US, UK, Canada, Australia & Europe, advertising is generally more expensive in these geos compared to Pakistan, India, Philippines, Mexico, Brazil etc.
And to take advantage of this location arbitrage, all you have to do is begin your ad campaign by targeting customers or audience in a cheap geo-location like Pakistan or India. Once you have 100+ leads or customers from one country, you can use that data to create LookAlike audience for any country including the US. This saves you serious costs in data acquisition which is often done by losing money. And you end up with a valuable data for very little ad spend that allows you to scale your campaigns in any country.