Ceilings in SaaS

Software-as-a-Service is a one of my favourite business models and it’s not hard to see why:

  • Recurring subscriptions lead to more predictable revenues
  • Digital distribution means high margins, relatively easy internationalisation and a flexible cost base
  • Compared to consumer software, churn is typically lower, ARPAs higher and LTV therefore higher

At Point Nine (where I spent the past 4 years) we invested in over 35+ companies in this sector alone and Christoph and Clement regularly publish our learnings on the Angel VC blog and P9 Medium channel. Sometimes I like to pitch in. :)

Feature, Product & Company–Market Fit

Something that I spent time thinking about recently are what I like to call ceilings in SaaS: How to properly evaluate how far a SaaS play can go in terms of MRR/ARR and therefore what exit potential it can achieve. One investor I discussed this with differentiated between three kinds of SaaS: Features, products and ‘realcompanies. While this classification is certainly a simplification (and could be offensive to smaller, profitable SaaS businesses that are still real companies), the idea of differentiating between three kinds of SaaS/market fit is a very helpful rule of thumb for assessing the potential of a SaaS company. To make it clearer, take a look at this:

Obviously these ranges are up for discussion, but the core question should be clear: Where is the invisible ceiling at which point the company starts to grow slower (below some 5% per month) and begins to plateau. In my eyes the rough levels are:

  • $1m+ ARR for feature/market fit
  • $10m+ for product/market fit
  • $100m+ for company/market fit (or public/market fit)

Of course there is nothing wrong with building a $1–10m ARR business and naturally a $10m+ ARR companies can already be very profitable and achieve great exits. If you are out to raise from growth VCs and chase the holy grail of an IPO, you will likely have to aim for $100m+ in ARR though.

The tricky part for investors of course, is to determine which of these categories a SaaS company falls into. This proves particularly difficult in the early stages, when all movements are still quite similar (see left box in the graph). While the relative movement (or momentum) to get to $30k for instance can be an indicator for feature or product/market fit and good execution, it doesn’t reveal too much about when we will hit the ceiling. As the company grows and more data becomes available, this gets easier, but it will still depend on the eye of the investor at the end of the day. That’s why having a more accurate sense for how big a company can get gives savvy growth investors a big advantage vs their peers — if they are right. :-)

How low high can you go?

Alas, there is never a silver bullet, but here are some data points that can help you to assess what kind of company you are looking at (and please feel free to add to the list):

  • The # of potential customers and assumed spend (bottom up) and overall market spend in this segment (top down) if possible, alternatively reasonable proxies
  • Market share achievable (typically 10% is already very optimistic)
  • Reference calls to understand how engrained the software is into the customers workflow and how it could be expended
  • Inspecting the incumbents product portfolio and gauge whether it is vulnerable (because of a platform shift or outdated tech for example)
  • Evaluate the competitive landscape and indirect alternatives (to get a sense for the ‘uniqueness’ of the solution)
  • Get a demo account and play with the product yourself, get a sense where it could go and how likely it is to expand the offering
  • Discuss the (mid/longer-term) roadmap and vision with the founders

I usually try to get an answer to most of these questions to come up with i) a more conservative estimate of where the ceiling could be (i.e ‘the baseline scenario’) and ii) a more optimistic one that factors in optionality (i.e ‘the breakout case’). Ideally, the baseline scenario already satisfies your investment strategy and the optionality scenario can then be seen as additional upside and follow on decisions, as more data becomes available.

So, how high can you go?

Any feedback? Feel free to chip in by responding or reaching out!