Up? Down? Flat? But compared to who?
This is the first year at Paid Memberships Pro that we captured cross-team scorecard data in a real, consistent way. I plan to share our averages publicly in early 2026.
A scorecard is great… at doing one thing: comparing this period’s metrics to historic metrics. Useful, but that only answers half the question.
Like me, you may be reading a lot of year end reflections/retrospectives about this time. Finally: a reference point.
Analytics and scorecards only give you a snapshot. A sense of “how you’re doing.” A little hit of dopamine.
As a product company, we peek at the overall report every week. I peek at the sales numbers daily: “was today a good day?”
The number that defines “good” has grown over the years, but the instinct is the same: look at the chart, feel something.
The problem is that most analytics are positioned only against your own history. Up, down, flat. Better than last period, worse than the one before it. Baked into that is an assumption that things should always move in a specific direction.
Which… sometimes makes sense. And sometimes absolutely does not.
Last year you launched a feature. Ran your first LTD. Had a spike from something very specific. This year looks worse, but it is still objectively good.
I feel this hard right now digging into our YouTube analytics. Is this good watch time? Is this good subscriber growth? I genuinely have no idea.
All I can see is how the channel compares to itself. What I cannot see is whether this performance is strong, average, or weak **for a channel like ours.**
I get the same questions from our customers:
- What is a good churn rate?
- What is a good free-to-paid conversion?
- What is good LTV?
- What is good MRR?
- What is good abandoned cart recovery?
The honest answer is the one no one wants to hear: it depends.
It depends on how long you’ve been in business. How many members you actually have. What you sell. Who you sell to. What stage you’re in. Even, what your goals are.
But “it depends” really means “we lack a reference point.”
What if analytics could compare you to a boatload of lookalike yous?
Not the entire industry or unicorn outliers, but businesses like yours: similar size, age, model, audience, goals.
That would be far more useful than a chart that just tells you whether you beat last month.
Would you want it? Or would it just become another number to obsess over?
Curious where others land.
