Ceilings in CSS
By: Nicolas Wittenborn
Tags: Subscription, Consumer, SaaS, Mobile, Consumer Behavior

Ceilings in CSS

In a lot of ways Startups = Growth whether they are venture-funded or not. Creating something from nothing and putting it in your users' hands, sometimes many millions of users' hands, is what makes the journey of starting or funding an early-stage venture so thrilling. Sure you can fail (and then try again), but who knows how far you can grow? :)

Around 3 years ago I published a post called ‘Ceilings in SaaS’ that discussed the idea of recognizing where early-stage SaaS companies will end up hitting a glass ceiling and stop growing by roughly losing as many customers as they are adding new ones. In this post, I want to apply the same concept to consumer software subs (CSS) or mobile subscription startups, as the higher churn in that business model also makes it more important to identify those that can push through those ceilings and get to of $100m+ yearly revenue.

As a refresher, I used the above graph to describe three rough categories of scale reached, with the tricky part being that they look the same at t(???):

Feature scale at ca. $1m ARR+

Product scale at ca. $10m ARR+

Company scale at ca. $100m ARR+

Now let’s look at some of the differences when evaluating consumer subs. For simplicities sake, I am assuming 100% of annual subs (and no monthly/quarterly plans) in my example.

Got habitual users?

Since retention is typically a lot lower for consumer subs companies, the most important thing in my eyes is to look closely at the long term cohort retention. Consider this graph for a second and tell me which number is more important to know: Y1 or Y2 (retention)?

C = Cohorts i or %

My answer: Usually Y2, though it seems like most VCs disagree. 🙃

Why? Because user churn is typically not linear for consumer subs. Whereas Y1 retention might give you a very good indicator for what Y2ff churn will be for SaaS companies, this is not usually true for consumer subs. Y1 retention often is a very steep drop and it could almost be considered a (long) paid trial in which a user either decides to stick with the product — or churns. However, once a user has been using a product for 24+ months and got hooked, I consider her a habitual user and expect her to stay around for a long while.

That’s why Y2 is more important to me. As indicated in the graph above, there is a degradation in churn in the outer years, and in fact, the best consumer sub-companies’ retention can look very close to SaaS if you normalize it in Y2 or Y3. This means that churn vs the prior year gets into the 90%+ range after the Y1 churn has occurred. So if you do the math and consider that the Apple tax drops from 30% to 15% in Y1ff, you might just find out the majority of LTV is actually driven in the outer years — by habitual users that stick with the product for multiple years. Sure, this is even more true for SaaS companies, but the big difference here is that most investors (and founders) wrongly assume cohorts to decrease linearly and therefore overlook opportunities to fund or build a business based on long-term sticky cohorts. Plus, I advise all consumer sub companies I work with to steer paid marketing so that they break even on the paid acquisition on Day 1; therefore cash for users that churn in the 1st year is recovered instantly vs SaaS, where payback is considered good if it is below 12 months.

But, but…

You might ask how to figure out Y2 retention at an early point in time? Good question! It is certainly not an easy feat, but there are tools you can use to get a sense for Y2 retention early on. However, they do require at least a few months of engagement data. Besides comps from other companies with similar user behavior and best guesses (both derived from external factors), I generally dig into the user behavior in M6 of the cohorts. (You can also use M3 but it will be less accurate, the longer out in the cohort, the better.) At that point in time you can usually differentiate between 3 different user cohorts:

Habitual users that use the product very regularly Casual users that use the product infrequently Breakage of users that will churn over time

I then use a formula like 90% of the cohorts in segment 1, 50% of segment 2 and 0% of segment 3 to get a proxy for the retention in Y1 and a lower weighted formula for Y2 and beyond (e.g 80%, 30%, 0% etc). This is, of course, a forecast and won't be 100% accurate, but it is a lot better than having to wait for 2 years and typically pretty close to the real thing.

What else?

I will save some of the below points for future posts, but to give you a high-level idea of what other factors can determine where the ceiling for your CSS business will be: