How I Invested $100,231.74 in Facebook Ads and Generated 597,342 leads in 33 days.

Are you running a business with a proven concept? Do you have a business model that works and provides real value for your customers? Are you profitable and feel that it is time to scale?

If you answered yes to the questions above this article is for you.

AN DIGITAL AGENCY CASE STUDY

Business: Digital Business School

Product: Digital marketing and business courses and mentorship. Domain and hosting.

Time: 33 Days (April 1 – May 3, 2020)

SUMMARY

Ad Spend:

$100,231.74

Cost Per Lead:

$0.17

Leads:

2,138,446

Background

This business, which is the largest school worldwide specialized in Digital Business & Marketing for Spanish speakers, had been doing Facebook ads for years. April 27, 2017, to be more exact. During this time, they had spent $168,246.22 in total and generated 591,644 leads for a cost of $0.28 each.

The maximum ad spend for one month had been $8,000. Their maximum KPI for Cost per lead was $0.25. That is why they have never been able to scale higher since the Cost per lead always has gone up. It was one thing only that was stopping them from growing getting more leads into their funnel at a low cost.

The Approach

I wanted to scale this fast and basically exceed all expectations and see them grow faster than they ever had. My goal was to scale this up to $100,000.00 in ad spend with a cost per lead less than $0.25 in 30 days.

To do this, I had to go broad… Extremely broad!

I had to go broad, both with audiences and with the ad creatives. I know for a fact that Facebook’s algorithm has its own strategy, and I (as a human) have my own. Therefore, I had to be smart in how I would structure the accounts, so we were aligned. Things that I had to keep in mind was:

Campaign Structure

If I were to be able to maximize the scaling process, I had to collaborate with the algorithm in the most efficient way. I have to work with Facebook, not against it. My approach was to use as many automated settings as possible to allow Facebook to select who and where to show the ad by itself.This may sound easy, but when you really want to scale BIG one can’t simply set up 1 campaign and try to scale it vertically. It won’t work. Each campaign has its own optimization and “style” when it comes to performance. Therefore, I had to create a lot of different campaigns so each one could have a unique optimization process. This was also the only chance I had to scale this FAST and have a stable cost that would be below the target KPI.What did I do?I set up 50 different campaigns in total to start with. 50 different campaigns would end up to be 160 different campaigns I tested after the first month.

Campaign Structure

If I were to be able to maximize the scaling process, I had to collaborate with the algorithm in the most efficient way. I have to work with Facebook, not against it. My approach was to use as many automated settings as possible to allow Facebook to select who and where to show the ad by itself.

This may sound easy, but when you really want to scale BIG one can’t simply set up 1 campaign and try to scale it vertically. It won’t work. Each campaign has its own optimization and “style” when it comes to performance. Therefore, I had to create a lot of different campaigns so each one could have a unique optimization process. This was also the only chance I had to scale this FAST and have a stable cost that would be below the target KPI.

What did I do?

I set up 50 different campaigns in total to start with. 50 different campaigns would end up to be 160 different campaigns I tested after the first month.

Ad Set Structure

With 50 different campaigns, I filled them with 5 ad sets each. No more, no less. Here’s an important thing to keep in mind: Each campaign had a CBO budget (Campaign Budget Optimization Budget).

The reason for this is because I wanted to use Facebook’s algorithm to optimize the ads by itself, which would give me an indication of how it would optimize by itself if I were to scale the ads. Sometimes I am using ad set budgets in the testing phase and insert these audiences into a CBO campaign in the next step. But for this aggressive scaling, I had to see how the algorithm would distribute the spending by itself and understand how the optimizations would be on a later stage.

How did I sort the audience?

When you are using CBO it’s important to have similar-sized audiences to avoid overspending on one ad set, and underspending on another. What I also like to do is to use similarly typed interest in each CBO, to not only test the interest but compare categories and sizes with each other.

Ad Structure

Each Ad Set will have its own “Ad tail”. I refer to ad tail as the winning ads you are using for all the testing and are glued to the ad sets. Simply when duplicating different ad sets and campaigns to test new, you are having an ad tail. In the testing phase, I had 10 ads in each ad set. Later on, when I had tested my new creatives, I would have 30.

Ad Structure

Testing Creatives

Since we were to move fast, I had to test both audiences and ads at the same time. I couldn’t have one phase of ad testing, one phase of audience testing, one phase of scaling. No, everything had to be done at the same time.“The most important thing to remember is how the performance is over time.”The structure of the ad creative testing was very straightforward: 1 campaign that simply was a “Creative Testing Campaign”. In this campaign, I inserted 200 different ad creatives, separated into 200 ad sets. Each ad set had a budget of $5 per day.The reason why the budget was that low is that I prefer distributing the ad spend over multiple days. Instead of doing a test of $50 for one day, I would do 10 days of $5. Each day will perform differently, and the most important thing to remember is how the performance is over time.

Testing Creatives

Budget Distribution

Each campaign had the same type of budget. It was not that high for each campaign and distributed over multiple audiences and ads. I wasn’t interested in one day with outstanding performance with the risk of losing it the next. No, it was all about finding profitable ad sets that would perform over time.

Budget Distribution

Rules

I set up a few rules beforehand that would allow me to make strategic decisions without emotions. Just like trading forex or stocks, you have to make up the rules beforehand. “Like the grandmother who never measures the ingredients, but does it based on intuition. It always gets better for some reason.”

These were the most important metrics when starting out. I want to mention that it’s always a game when you are starting. You should have a strategy, but you have to have a “feeling for it”. Don’t get emotional, but have a “feeling for it”. If you are doing FB ads you know what I mean. Like the grandmother who never measures the ingredients, but does it based on intuition. It always gets better for some reason.

Rules

Scaling

Each campaign that was profitable and had good KPI:s after the testing phase was duplicated 3 times.

I set out new rules for each level of scaling and made sure they were either following the rules, or they were paused. I only increased the budget on the campaign without duplicating it in one case: If the $300 campaign was 30% below max CPL KPI after 7 days. I increase it with 10% per day as long as it was not increasing in price. I did this with all my campaigns and it was a wild journey. It took me 9 days to reach the previous record of ad budget spend for this specific business and I spent the rest of the $100,000 in the coming 24 days. The reason why I was able to scale this fast was that I tested what Facebook wanted to optimize, not what I wanted. I surfed with the algorithm and was going extremely broad and fast, all while Facebook was doing its job. I was 3 days late with my goal, but it was a huge success. This allowed the business, which is the largest Spanish-speaking Digital Business School in the world, to scale quickly and build a large community of new members.

Scaling

Each campaign that was profitable and had good KPI:s after the testing phase was duplicated 3 times.

In 33 DAYS

In total I tested 160 Campaigns, 705 ad sets, and +200 ads

Ad spend

Cost per lead

Leads

You can also find this case study HERE

Do you want results like this?

Connect With Us Today