Scaling Digital Products
Drastic Increase In ARR over first year of work
Working with the HIITBurn team to test variations of product funnels and email follow-ups along with our paid acquisition testing allowed us to scale HIITBurn from 1.2 Mil to over 3.5 Mil ARR in under 12 months.
“What gets measured gets managed.” - Peter Drucker
The problem is, measuring something doesn’t mean that you know what is actually happening within your brand's ecosystem. You have to figure out what’s worth measuring and what’s not.
Our initial few days working with HiitBurn focused on diving deep into business analytics, pixels, tracking, and attribution set-up.
Many companies set these things up incorrectly, so we wanted to get a starting point for this side of the business.
What we found was a fragmented analytics infrastructure that was making it difficult to track audience/acquisition performance across all their product offerings.
With HiitBurn we found an incredible business with the ability to scale massively IF their numbers were accurate. They had many product on different funnels across a few different front-end and checkout platforms.
We like to call these FRANKENSTEIN Sites.
Most people unintentionally do this by:
- Adding plugins until they have too many
- Continually creating and adding tracking scripts
- Associating accounts with the wrong tracking scripts
- Having multiple sites
- Using many project based consulting teams with no inter-project continuity (for the back-end)
It’s at this point that we go from a trackable and measurable data to something that doesn’t make any sense.
On HiitBurn, the first objective was to untangle and simplify the sites/tracking scripts prior to scaling the budget.
Not only did this allow us to really understand what was happening in the business (and see the true ROAS of the ad accounts) but it gave us the understanding of where people were coming from and what was actually profitable.
Right as we started to understand the business it was time for HiitBurn’s “quarterly challenge” to begin.
Essentially, every few months HiitBurn runs a challenge for the upcoming season “Summer Flat Belly”, “New Year Resolution”, etc.
By this time all tracking was up and running, so it was time to scale.
Prior to this challenge, we were spending just enough to, keep a consistent 2-3 ROAS across the board (cold and remarketing.)
During the challenge we knew we had three things working in our favor:
- Time Scarcity
- Limited Spots
- Restricted Period to Take Action
It was these three points along with our solid understanding of sales attribution that gave us the confidence to aggressively scale.
So, we put the gas pedal to the floorboard, 5xing the daily budget while doubling ROAS during their first quarterly challenge.
These periods are perfect for sparking an ad account, accumulating valuable events data for the pixel, and creating a buzz throughout the entire brand ecosystem.
This is exactly what happened with HiitBurn. The audience testing data gained during the challenged directly impacted the performance of HiitBurn's evergreen campaigns for months after the challenge ended.
High acquisition spend period can be used for more than just accumulating short term boosts in revenue.
Over the course of 10 days, we generated $125,514 for only $28,016 with a ROAS of 4.48
The best part is… This was while running our normal daily ads and allowing them to play off the increased buzz that the challenge was creating.
A Note For The Skeptics
We do not blame you! Looking at any ads platform as your source of sales attribution truth is deeply flawed (potentially delusional). But for the purposes of demonstration we are using a medium (FB Ads Editor Analytics) many people are familiar with.
The numbers shown above accurately reflect the sales data using a 7 day last click attribution model in FB. For anyone still unsatisfied I’m providing a little more insight into how we think about attribution modeling below (just a little).
On Data - (A Slightly Longer Note For The Skeptics)
In most cases our “data source of truth” is sales data taking place in the checkout system linked to a payment processor. From there we use a combination of channel attribution data from Google Analytics and Facebook's Attribution Tool to try and get closer to a reasonable measure of per channel CAC.
While you can always get more “into the weeds” on your analysis of channel/audience attribution over time, we usually work toward one metric that is easy to think about (hard to get). That metric is per channel CAC/LTV.
If we can get a reasonable measure of that metric, we are pretty happy. To get a little more specific we are usually talking about 90 - 180 day LTV.
To do all this we use a combination of FREE and paid tools depending on the accounts we are working on and the mix of front and back end technologies each company is using. And this is where the conversation goes way off the rails into the deep dark world of attribution modeling and data engineering.
To get into a nuanced conversation about data’s relationship to ad performance and how we can help feel free to contact us using the contact page or one of the "book a call" buttons on the site.
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