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Facebook Ads Algorithm Explained for Cannabis Companies, Part I

Let me answer this first: yes, there are ways to get Facebook ads approved for your cannabis company… you just must be creative. I’m going to talk a bit about machine learning. We hear all the time about how smart the Facebook algorithm is. I think just kicking it off, I’m going to explain exactly what that means, and how it kind of applies to the algorithm, and how that affects us as cannabis business owners and marketers.

When we are talking about machine learning or the “algorithm,” we’re talking about how does Facebook look at the performance of our ad-set, how does it consider our audiences, the pixel data we’re sending them, and combine all those factors to help you reach the most valuable people at the ad-set level.

Now, there are updates going on constantly in the algorithm. Think about the algorithm as like a software update. It’s like app updates, the algorithm is constantly updating, not necessarily changing per se, but I do think because the newsfeed is so crowded right now, and many, many people are bidding for a place in the auction, a seat at the auction, then Facebook is trying to maintain the user experience to not alienate its customer base, but also balance that with the best advertisers to enhance the user experience.

Since the algorithm, the machine learning, is constantly updating, not necessarily changing per se, you should update your strategy alongside of it. Hopefully, we’ll get into some real actual strategies here.

I’m not a total geek techy and honestly, I look at things way more from a strategy standpoint. Understanding more about how it works is great, but on the opposite side of that is also your mindset, so understanding the machine but then also understanding that a lot of this has to do with the way you’re thinking about it.

Think about this analogy… if you’re a golfer, or you’ve been around a driving range before, you’ve probably seen this guy at the range, or at the golf course who is swinging the club so hard, literally sweating, and putting every ounce of energy he has into trying to hit the golf ball. Well, if you watch the guys on TV, like, if you’ve ever seen Ernie Els play golf, you’ll see that it looks like he’s barely swinging but he can hit the ball 300, 350 yards, whereas the guy at the range is usually the one who’s also like topping the ball 20 yards, or chunking it. He’s barely making contact. In golf, a big saying is “Let the club do the work,” right? That’s what the PGA Tour players do, that’s what the best golfers in the world do is that title lists or whoever made that club has made it to hit the ball far. You don’t have to do that much at the end of the day to make it go far and to get it straight.

The same thing is kind of true for Facebook in that the ad-auction technology is built to find the highest-valued people for you. Sometimes it’s better, we could start off by testing and trying to manually find these right audiences, the right placements, the right creative, that’s all good.
When we’re trying to scale, sometimes it’s better to just let Facebook loose. Let them do the work. Let the technology do the work for you and be a little more hands-off because through their data science and through the machine learning, they’re going to be able to identify, looking at hundreds and hundreds of thousands of variables who out of that huge big audience they should be going after, much better than we ever could doing it manually.

Obviously, this is all about who you’re targeting in the cannabis industry, right, so who you’ve said you want to show this audience to, what you’re optimizing for, so what you’re telling Facebook that you want, whether it’s conversions, or clicks, or whatever you’re asking for.

Obviously, liking pages or commenting on certain posts, your behavior inside of Facebook has something to do with your behavior on websites too, since most websites have the Facebook pixel installed. I mean, they have to know who’s most likely to go a certain number of pages into a website, or to take a specific action. I think there’s a ton of behavioral attributes in there. We’re not just talking about more static things like age, or what you do for a living. I mean, that can be a bit more static but they’re also looking at data … Facebook’s not the only data platform out there that has this but they know what types of sites you’ve been on lately, and that’s recency. That’s really important. That’s part of how the machine learning works is that it sorts through the variables and finds out what are the most important for your particular ad-set.

I would never underestimate the power of some of the engineering teams at these companies, things like text analysis. They take your posts, they analyze it. They pull out keywords. They look at pictures that you post. You can use machine learning to figure out where the person is, what they’re doing. At a more basic level, yeah, like what sites you’ve been to, what things you’re shopping for.

What do you think when it comes down to a conversion standpoint, like the photo, that’s interesting, figuring out where you are. Because I have noticed when I’ll post a picture on Facebook, or when I land in a new city it’s like “Hey, here are other friends who have visited Dallas.

It’s like, “See what they’ve done.” In terms of the conversion aspect, from a purchase or an opt-in standpoint, what do you think they’re looking at? When you tell Facebook, “I want conversions. I want people to buy, or opt in,” what do you think they’re looking at there? Again, everybody, we’re totally speculating. This is not from Facebook, but it’s a good discussion and stuff I think about a lot.

At the end of the day, the machine learning, or the data science, like no matter where it is, Facebook, Tesla, whatever company it is, whatever data scientist team is there, there’s typically a set of commonly used methods to do this type of stuff. It’s just around prediction, right? There’s several ways to do it. I don’t know which specific one they’re using.
I will say that when they’re trying to predict conversion, what they really want to do at the end of the day is say okay, we have multiple objectives here. We have multiple competing goals. The advertiser wants return on ad spend. We need to get them that for them to continue to advertise with us. They want in this case CPA, they want to lower their CPA.

The platform, Facebook, us, we don’t want to burn through our user base. We want engagement, and stickiness, time on platform. Then the auction in and of itself must then sort through everyone’s objectives and then figure out okay, a lot of advertisers want this placement, who gets it? There’s several factors that go into that, but in terms of the conversion, how they decide based on a conversion objective, they’re going to look at the historical converters first.

That hard and fast rule that we used to talk about you need to have X-number of conversions per day, it’s not 100% true… especially in the cannabis industry. It’s not like if you don’t get it your ad’s just going to turn off and stop delivering in most cases. What they’re doing is they’re taking the people who have converted, so let’s say that over the course of three days you get 30 conversions. Now they’ve got 30 people, each of those 30 people have a huge set of attributes that Facebook knows about them. They look through it and they say, “Well, what of these sets of attributes is common?” They can weigh those and then they look at the people who converted and the people who didn’t convert. They go, “Alright, where’s the pattern here, where’s the big differences?”

That’s what machine learning really… the data science, what the predictive modeling really does is it looks at these attributes and then tries to figure out okay, what’s predictive about this data set. How can I predict an action from underlying features or attributes?

It’ll look first at the historical converters and go okay, now from this set, I can kind of start to predict a conversion rate for the rest of the user base, or the rest of the target in that ad-set, so when the auction comes up they already know Molly has a 4% chance of converting, Keith has an 8% or whatever.

Then your percentages will be different than every other ad-set in the auction. You can imagine the size of this data. It would be insane, but part of it is like, okay, well, at the end of the day, we want to deliver what’s called a true value or expected value. That’s where the bidding comes into play and that’s where the return part comes into play.

When we teach at TCML, we’re just like yeah, optimize for conversions. Facebook is going to show it to people who are most likely to convert. That’s all I can really explain. Like thinking about how that correlates with the auction, and that also goes to show why ad costs are so different between markets.

It also goes to show why certain offers, in my opinion, have a viral effect. Especially when we’re running conversion campaigns to optimize for leads, some of our Lead Magnets, the campaigns just never get off the ground.

So, once you have some data, let’s say you’ve done some testing. Well, whatever testing you did to start off, you figure out what audiences work. Maybe you got a couple look-alike audiences, maybe you got four or five different interest groupings.

Once you actually figure that out after your initial testing data-set, then it sort of depends on your budget. So, what would be the next level of scale and leverage to be able to get the result or the expected outcome that you really want with regard to website conversions? How would you handle it to the next step?

Well, once I’ve gotten an idea of testing different placements, and audiences, and creative, I would be focused on learning what creative is working there. Whether you’re doing it through the Michigan Method or you’re using AdEspresso or some third-party software, once you get those learnings and you’re ready to go to phase two, phase two would be more about give Facebook room to work, let the club do the work.

Later in the week, I’ll put out Part II of this article. I didn’t want you guys to glaze over. LOL 😉


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