How good is your grasp over the pirate metrics?
You’ve seen them mentioned in blog posts. You can probably recite them—acquisition, activation, retention, referral, and revenue—and you might even remember where they come from—thank you Dave McClure.
But how deep is your understanding of these metrics?
If you’re anything like me, you understand each independently, but how they affect one another is beyond immediate comprehension. Think for a second, can you visualize how a 10% increase in retention compares to a 10% increase in referrals with any amount of certainty? How about the impact of activation vs retention on your bottom line?
As aspiring masters of growth, our lack of mastery over this subject matter leaves us blind. In order to know where to optimize our user journey, and understand the impact of those results, we need a better grasp on how these metrics affect one another.
We Built a Model to Compare the Pirate Metrics
The order of the original priate metrics resemble a freemium revenue model—McClure put revenue after retention and referrals like freemium apps do. But nowadays a free-to-paid revenue model is more popular. Our model reflects this by placing revenue right after activation.
The good news for pirates is that our rearranged model leaves ‘AARRR’ acronym unharmed.
Using the free-to-paid model, we built a calculator to measure the impact each metric has over annual recurring revenue (ARR) and learn more about how these metrics affect one another.
Here’s a look:
Want to play with our calculator? 🤓
To compare each metric’s impact on ARR we’ll calculate the difference between a benchmark of each metric to a 25% lift on that benchmark. Again, that’s:
The percent difference in revenue between:
benchmark of metric vs.
benchmark of metric + 25% lift
If this isn’t clear yet, that’s fine. Let’s put it into context with acquisition.
In our model, acquisition measures the number of users entering your free trial. For our acquisition benchmark, let’s plug in 5,000 free trial sign ups (5,000 is a made-up number), and calculate how a 25% lift on our acquisition benchmark impacts ARR after one year:
As you can see, the 25% lift in acquisition (top right cell) resulted a 25% lift in ARR (bottom right cell). It may seem odd that these lifts are equal until you re-consider what acquisition measures. Acquisition determines the size of your funnel.
Therefore, if you 10x acquisition, you’ll 10x your ARR. And that’s pretty uninteresting. Don’t worry though, other metrics will have greater leverage over ARR.
Activation is the Upstream Bottleneck
Activation takes place when users first achieve the value you promised. It’s calculated as a percent of activated users out of total acquired users.
As a benchmark for activation rate, we’ve chosen 30% based on the sad reality of mobile app onboarding and our experience working with companies on their onboarding. Here’s how activation impacts ARR:
A 25% increase in activation yielded a 48.6% lift on revenue. That’s almost a 2x return on ARR. A massive difference compared to the 25% lift with acquisition. Tweet
Surprised? I was. But I thought about it a while and made sense of this. In our free-to-paid model, activation directly determines how much revenue is coming in the door. That makes activation an important upstream metric that impacts not only revenue from new users but also recurring revenue from existing users.
Revenue Sets the Funnel Size in Terms of Dollars
In our model, revenue represents the ACV, or average contract value, that a user pays per month. To keep things simple let’s set revenue at $100 and calculate the impact revenue has on ARR:
As you can see here, a 25% lift in revenue corresponds to a 25% lift in ARR.
Retention is the Downstream Bottleneck
Retention measures the number of users continuing to pay for your product month over month. Retention is typically a much higher percentage than activation because it’s easier to get users to maintain a habit than to start one.
Using a 97% benchmark on monthly retention borrowed loosely from Jason Lemkin, let’s calculate the impact a 25% lift would have on ARR:
It may seem odd that we’re experimenting with a 121.3% retention rate, but it’s actually possible to have a retention rate greater than 100%. That’s because most products encourage users to upgrade and spend more with a business.
As you can see, retention has a similar effect on ARR most similar to activation—they’re both percentage-based bottlenecks.
However while retention yielded 41.8%, activation yielded 48.6%. Activation’s advantage over retention exists because in our model payment occurs at activation. If payment was occurring at the retention cohort, activation would have have less impact on revenue. Because that isn’t the case, retention has less of an effect on revenue.
Referrals Are Icing on the Cake
Referrals measure the percentage of current users who successfully bring new users into your funnel. That means we measure referral as a percentage of your entire existing user base—not just your new users.
We did some math based on referral benchmarks, and found 22% to be a good referral rate benchmark. So let’s calculate the impact of a 25% lift on our referral benchmark:
As you can see, a 25% lift off of our referral benchmark resulted in an 18.9% ARR increase. This is a bit underwhelming.
For growth people that believe that viral loops are the holy grail, this might come as a shock. I was surprised the first time I saw the results of this calculation. So I ran it again with our referral benchmark at 100%. This resulted in a similarly underwhelming lift of 45% and has changed my perception of the value of referrals.
When It Comes to Growing ARR, Activation Reigns Supreme
Here’s a table to compare the impact each metric has on ARR with a 25% lift—the bottom right cell in the tables above:
When comparing each pirate metric’s impact on ARR, it’s clear that activation—at a 48.6% lift—takes the cake.
When comparing activation directly to other pirate metrics, it’s 16% greater than retention—the next most impactful—and 74% greater than the average of each other metric’s impact on ARR. Tweet
These findings are hugely important to the way experts in growth should view their user journeys. Each pirate metric is not equal in their impact. And they should not be treated as such.
The smartest and most effective growth people should spend far more time optimizing activation than other metrics.