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Tag: Facebook Pixel

Variable Sale Pricing & Facebook Ads

One of the readers of the blog was discussing his Facebook ad strategy and mentioned that he has to increase the sale price of the product due to expensive shipping.

This quickly reminded me of my personal experience with variable pricing and Facebook ads and I thought to write a bit about that.

When you start advertising a product on Facebook on a specified price point e.g $19 and have a ton of qualified events stored in your pixel and ad account, a change of pricing can be sometimes disastrous. When you record hundreds of add to carts, check-outs, and purchases at a $19 price point, you’ve trained the Facebook ad algorithm to bring you buyers who are comfortable to spend money in that range. Facebook looks into the historical purchase patterns of the buyers and their average cart value in order to serve your ads to the right audience.

An increase of pricing mid-way in the campaign with a lot of recorded data will have more negative impact as you’re not just going to have lower conversion rate due to the hike in price, but also because your ads will not be served to the right audience further reducing your conversion rate. Due to this reason, personally, I like to start my ads with the final sale price and not something lower.

Facebook Announces “Shops” – Native Shopping Experience on Facebook Family of Apps

Only 2 days ago I wrote this piece about e-commerce being the real fuel of the internet. I feel more confident about it today than I’ve before as Facebook diversifies away from ads and inches closer to the e-commerce space.

When we ran our network of content sites that leveraged Facebook’s influencer marketing, I realized that we were working against the force. That each day, Facebook would do something to cap, limit or control our business or business model in a certain way. I knew that they will take over the business model. So much out-bound traffic making tons of ad-revenue all outside the Facebook platform.

They let us run for a while, for a long while, because their users engaged very well with the content. But eventually they launched instant articles to get a piece of this pie. A native facebook article reading experience where Facebook serves the ads and also takes a cut.

They did the same with the videos. In the early days of Facebook, Youtube videos appeared as embedded content on the platform and not as links. As they started to prioritize their videos on the platform, they started treating Youtube videos as regular links. Eventually they launched in-stream ads, a video monetization program just like one for Youtube.

Now they are doing this for e-commerce. Every day 100s of new D2C brands are launched. Their primary source of customer acquisition has been Facebook. It is estimated that most indie e-commerce brands acquire over 50% of their customers through Facebook advertising. While Facebook already takes a massive chunk of the revenue generated (often 40-50%), having more control by hosting native e-commerce experience on the pages could mean more revenue for the company, better user experience & higher conversion rates.

Just like instant articles, each product would need to be approved by Facebook now when you import the catalog.

How do I perceive this news? troubling. I know this will improve user experience but Facebook already practices more control than I appreciate and the counter-party risks continues to increase.

While you practiced full autonomy over your woo-commerce and Shopify stores, now you’ll be on the mercy of Facebook. In the worst case scenario, which by the way is often the normal case scenario for me, no longer will I only lose ad accounts, I could also lose my “shop”, because of course my “shop” has to adhere to Facebook’s TOS. If the AI, which isn’t very fond of me, constantly throws ban hammers for allegedly violating advertising policies, why wouldn’t it do the same for the shops.

Here’s how Facebook’s Shops look like

Building Lookalike Stacks, And Why They Work

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.

At nearly $100 spent, purchase conversion value is $255 yielding ROAS of over 2.5

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.

This Seems Relevant Today

This could have been me had I stopped yesterday which by the way I wanted to.

This is me instead because I hung around longer.

I spent the past couple of days trying to optimize a new product launch. All metrics looked great. Every step of the funnel just as I wanted. I had low CPM, high CTR, low CPC, low CPATC, low CPIC, but.. also low conversion rate. For those who don’t know what am I talking about, I had low cost for everything, but the number of users purchasing were also low which was something I really didn’t expect to happen.

Due to this my cost per acquisition was higher than where I wanted it to be. Instead of making money, I was losing money until I launched the retargeting campaign.

For those who don’t know, retargeting is reaching warm audience or potential customers again. People who showed purchase intent but didn’t purchase. My retargeting campaign brought me really cheap sales. So cheap that it offset all the loss that other campaigns caused. Not just that, it turned the overall campaign around and made the product launch profitable.

This showcases two things. 1) Retargeting is really really powerful. 2) When you’re thinking of giving up, hang around just a little bit longer.

Why Should You Always Duplicate Your Ads

If you’re familiar with Facebook advertising, you may have seen that some people always run multiple copies of the same ads in an ad-set. Those unfamiliar with this strategy always wonder, why would someone create 2 identical copies of the same ad and place them in an ad-set. Here’s the reason why.

When you target a large audience (for example 1 million to 100 million) which Facebook also encourages you to do so, not every person in your audience (interest/behavior) is going to be identical.

When you place two identical ads in an ad-set you’re hoping that your first copy will be seen by a small pocket of your large audience, and your second copy will be seen by a different small pocket. Based on the performance of the audience in those pockets, Facebook will continue to find similar audience using it’s machine learning capabilities.

It is obvious that one of the pockets of the audience would be superior to the other one and by having multiple copies you’re giving their machine learning a better chance of spending budget in your interest in a more optimal manner.

I found this difficult to convey over the text, but I hope that I’m able to do so. If you have any questions, please feel free to ask in comments.

Here Are My Favorite Resources To Learn Facebook Advertising

I have profitably spent hundreds of thousands of dollars on Facebook ads. I have been doing this heavily since 2016. I could attribute most of my basic learning to Travis’s free resources that he put up on YouTube.

If you’re interested in Facebook advertising, I recommend that you complete these tutorials. You should also go through these.

Travis has been playing this game for over a decade so he’s pretty good at what he does. Much of his content may be dated although still very useful. This is still my favorite resource for getting the basics right.

For intermediate strategies, I’d recommend that you check out Verum. To understand what he’s saying, you need to be well aware of basics. If you’re well aware of the basics, you’d love his content and find it very easy to digest. Otherwise you probably wouldn’t understand much of what he’s saying.

The most advanced players, however, are the AdLeakers. I don’t think there’s any value for anyone here unless he’s already spending a lot of ad budget profitably and wants to further up his game by working on cost reduction strategies to achieve lower cost per acquisitions.

I don’t suggest that you invest in any course if you’re just starting. Investing in the actual ad budget might be a much better idea. But before you even do that, I strongly recommend that you consume Travis’s KingPinning tutorials.

Protecting Your Ad Campaigns From Yourself

Lately, I’m writing only about Facebook ads & e-commerce. That’s because since 1st January, we’re busy with the launch of our new store. We took a big part of 2019 off and planned to do some work from 1st January 2020. It has consumed us so far. But today is a good day because today we made it profitable.

In 12 hours, we have done $415 in sales. We’re projecting to close the day at $700.

Our ad spend for today is $154.81 which is about 37% of our gross sales. The net-margins are low right now but since we’re just getting started and the pixel is developing, we’ll see improvements with conversion rates over time. We also haven’t introduced any kind of lookalikes at this point in time.

These aren’t our desired results. We have to make up for the losses incurred with ads in the first 15 days. We need to optimize the ads after removing whatever segments aren’t converting to bring the costs down. We also then need to scale this campaign. If everything goes right, we should be able to do that in the next 7 days.

The reason why I’m not touching the ads right now despite that there’s room for improvement is because campaigns take time to optimize. Many people suggest not to touch your campaigns for 24 hours after you make them. While this could work if your campaign is completely off, I generally suggest 48 hours if there’s some sign of success. And so I’m going to let these go on for at least 48 hours and will not modify them.

After 48 hours, I’ll decide how to modify them by looking at breakdown data.

So if you want your campaigns to work, you need to protect them from yourself. Stay away for 48 hours, be patient. The cash might burn and it is going to hurt you, but the profit lies after that.

Facebook’s Super Lookalike Audiences Using Top Percentile

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.

Growth Risks With Facebook’s Machine Learning

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.

Facebook LookAlike Marketing Hack That Will Save You Tons of Money

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.

To build LookAlike audience, you’ll need a customer file, engagement on your Facebook page including video views, or website data using pixel. To learn about pixels, watch this. To learn how to build custom audience, watch this. And to learn how to create LookAlike audience from your custom audience, watch this.