Where Are You on the S Curve?
Two forward-looking growth metrics you should track
This is nearly impossible to verify, but I’ll say it anyway: 95% of growth metrics used today tell you how you did in the past but not what it means for the future. In other words, they’re purely backward-looking.
So I’m going to propose two forward-looking growth metrics that can help complete the picture — whether you’re an investor or an operator.
Motivation
As your customer base grows, absolute churn volume will inevitably grow, too — even if the churn rate does not increase. At the same time, acquisition gets harder over time. This means that eventually churn will catch up with acquisition and the customer base will stop growing — unless we do something about it. This is the core dynamic of the tyranny of the S curve (see here).
What if we could easily measure and track how exposed we are to this risk? It turns out that we can do that with these two metrics: time-to-plateau and gap-to-plateau.
How it works
The basic idea is to approximate where you are on the S curve using the following methodology:
- Assume that the churn rate (%) and the acquisition volume (absolute number) stay constant going forward.
- Based on the assumptions in 1, estimate how long will it take until the customer base stops growing (time-to-plateau) as well as the remaining upside in terms of customer growth (gap-to-plateau).
Why calculate time-to-plateau and gap-to-plateau? Time-to-plateau tells you how much time you have to act, while gap-to-plateau tells you how much customer growth you have left at steady-state churn and acquisition.
This can be done using very simple math (described later in this post).
An illustrative example
Let’s walk through two fictitious B2C companies. I’m assuming that both companies have found product-market fit.
- Company B relies more on marketing but they also have more efficient ads.
- Company A has lower churn and higher organic acquisition.
Simulated growth numbers: Revenue growth are almost identical; both companies reach annual revenue of $620M by year 3.
LTV to CAC: Blended CACs (marketing spend divided by acquisition volume) are similar across the two companies as well. Also, as expected, CACs rise for both companies as they increase marketing spend.
The year 3 LTV to CAC ratios look good: 4x for company A, and 3x for Company B.
Annual acquisition volume: Company B is an acquisition machine.
In summary: based on the backward-looking metrics, the companies are performing similarly and seem healthy from a growth perspective.
Company A has the more favorable blended LTV to CAC ratio due to strong organic acquisition. However, they do not have much control over said organic acquisition.
Company B has a stronger and more scalable marketing engine — acquiring 33% more customers in year 3 than company A.
In short, it seems like company A has the long term edge but it’s not crystal clear.
The forward-looking metrics, on the other hand, paint a much more polarizing picture that is strongly in favor of company A:
This says that, assuming a constant 15% churn rate and constant monthly acquisition volume of 138k after year 3, company B’s customer base will plateau in roughly seven months. Moreover, the gap to the plateau is small.
This is not a healthy company from a growth perspective. Company A is in significantly better shape on this front.
Here’s what happening:
Company B is acquiring more new customers each month than company A. But it’s not enough to offset the rising churn volume from a growing customer base, despite maintaining a flat monthly churn rate at 15%.
The traditional backward-looking metrics did not catch this. These metrics only hinted at the problem.
Putting it all together
Time-to-plateau and gap-to-plateau are not an attempt to create a precise forecast of the size of the customer base. Rather, the purpose is to convert today’s churn and acquisition dynamics into forward-looking growth metrics that can help us manage the looming risk posed by the S curve.
→ If you’re an operator, add time-to-plateau and gap-to-plateau to your arsenal of growth metrics. Update them monthly and set a threshold. If, say, time-to-plateau moves in the wrong direction, take action before you find yourself at the wrong end of the S Curve.
→ If you’re an investor, these metrics can be calculated for young companies that do not yet have good forecasting capabilities — as long as it’s possible to make reasonable go-forward assumptions about churn and acquisition.
Appendix
Here’s how to find the plateau with a simple cohort-based model:
- Count the number of active customers today (T=0) for each existing cohort. Then apply the retention rate to these cohorts to guess the number of active customers for each future month. This provides a baseline of what would happen to your customer base if you never acquire another new customer. If you’re in a SaaS-type business, a simple monthly churn rate does not work and you’ll have to create staircase assumptions for each cohort based on the average contract length.
- Create future monthly cohorts using the flat acquisition assumption and apply the churn assumption in the same way we did in step 1.
- Combine 1 & 2 and add up across future months to get the total customer count. Then find the future month where you reach the plateau according to the model. Use a small positive threshold — e.g., a 0.1% month-on-month change — to find the plateau instead of searching for the point where you hit exactly 0% customer growth.
Note that we can relax the constant churn and acquisition assumptions if we have strong forecasting capabilities. But we should always be careful to avoid that these metrics (time-to-plateau and gap-to-plateau) become a reflection of what we hope will happen going forward.
To see how the math works, imagine a matrix where each row is a cohort and each column is a future month. By aggregating across the columns you get the total customer count at each future month. Here’s a simple illustration using a churn rate of 10% and an acquisition volume of 140 new customers per month going forward:
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