In Part I of our SaaSy stories series, we noted that the general question for understanding value creation for SaaS companies was no different to other growth companies:

  • How much opportunity does the company have to deploy growth capital?
  • What are the returns on this growth capital?

For SaaS companies, their high gross margins and limited capital requirements (at least in accounting terms), meant that the best proxy for these questions was sales.  In particular:

  • Empirically, the rate of growth of sales was a reasonable proxy for the longer term sales potential of the company; and
  • Top down measures such as Gross Margin, Sales Efficiency and Free Cash Flow margin were all reasonable proxies for the level of return generated on this sales growth.

Like all top down analysis that provides a good starting screen, the opportunity can be better understood by complementary bottom up analysis.  In this post we briefly review the bottom up analysis of these two questions.

Bottom Up Analysis of Sales Opportunity

Product market fit and pivots

A starting consideration is whether there is actually a replicable sales model.  The SaaS jargon for this is Product-market fit and it is closely related to that other favourite term from the Lean Startup – the pivot.  Under this methodology you release an MVP (minimum viable product) and see whether customers like it and will pay for it.  You then keep tweaking your offering until you find good uptake (product-market fit).

If the tweak is big enough, its called a pivot.  Common examples of pivots include changes from or to:  customer focus – enterprise/retail;  pricing – subscription/freemium/one off;  and monetisation – subscription/advertising.  Companies that are pivoting are saying that their current model doesn’ work financially and they are still experimenting.  Many of these experiments will pay off, but this takes time and money (usually more of  both than you expect) and so its generally a very dangerous place to invest.

Note that many companies that are still in this “pivot” phase might have sales, but they most likely don’t have SaaSy sales that are repeatable and ultimately scaleable.  The first step of bottom up analysis is therefore understanding what component of sales and sales growth comes from repeatable “product market fit” sales versus one off or experimental sales.

Benchmarking Growth Rates

The next consideration is to put growth rates into perspective.  50% rates of growth are outstanding if your revenue is $100m, but medicore if your revenue is $5m.  A startling conclusion from the McKinsey study – “Grow fast or die slow” was that “if a software company grows at [20%] it has a 92 percent chance of ceasing to exist within a few years“.

The best way to benchmark growth rates is relative to their ARR.  Smaller firms should generally grow quicker than bigger firms, although note that growth tends to accelerate around $20m ARR in these studies.  This is likely a combination of (i) survivourship bias (ii) new capital accelerating growth and (iii) “S-Curve” adoption/market penetration.

The data in this chart comes from OpenView SaaS Metrics Survey.  Other good benchmarking surveys are:  SaaS Capital and KBCM Private SaaS Company Survey.


Another way to get more detail behind the expected sales growth is to understand the marketing funnel, an example of which is shown.  Because these funnels measure the passage of potential customers through to actual customers, they can provide insights about which direction actual customer numbers might move.  (i.e. the fatter the top of the funnel, potentially the fatter the bottom).

For example, when Facebook listed, valuations at 15x sales looked exorbitant.  In hindsight, those who focussed on user metrics as a good leading indicator of revenue were proved right.

However, like plenty of analytical and forecasting tools, things that might be helpful are all too often manipulated.  The key feature of the funnel is that the further up it we move, the less connection there is to our data point and actual sales.  (And remembering that by using Sales we are already a few steps removed from cashflow – the ultimate objective).  So using website visits as a proxy for free cash flow is a very big step to take.

Of course, because numbers up the top of the funnel tend to be bigger, they provide great scope for optimism from both companies and analysts.

Volume v Price

A final step in the bottom up analysis of the revenue equation is the extent to which growth is driven by volume or price.  As a general rule, volume growth is better/more sustainable than price increases .  Volume is essentially a step higher up the revenue funnel with price being the last available lever to drive revenue growth.  There are two obvious caveats to this simplistic assessment:

  • Firstly, volume that exists purely because the service is offered at an unrealistic price point is not good (at its extreme volume that is offered for free!);
  • Secondly, the ability to raise prices is an excellent indicator of the value proposition on offer.  To the extent that price and value differ materially, price increases can go on for a long time and create huge value.  (e.g. Facebook, REA).

Note that in considering value drivers, ARPU increases do not necessarily equate to price increases,.  Where pricing is on a per seat basis, higher volume in the form of increased penetration at a customer might be reported as higher ARPU.

Bottom Up Analysis of Returns

The starting point for bottom up returns analysis is to undertake a customer lifecycle analysis.  This aims to understand the economics of each customer as follows:

  • Determine Customer Acquisition Costs (CAC or SAC – subscriber acquisition cost). The important consideration is which costs are factored in – does it include all staff and onboarding costs, or just direct 3rd party marketing costs?;
  • Determine Annual Cash flows from the customer (Revenue less COGS or Revenue x GM %).  Again, the calculation of this number may differ between companies.  Does it include just short term direct variable costs such as hardware and hosting costs, or does it extent to a share of overheads that are in fact variable over the medium term – such as customer support, product maintenance etc.;
  • Determine Average Customer Duration (= 1/Churn).  Churn is arguably the most important variable for a SaaS business. The extent of churn caps revenue growth rates and has a material bearing on return on investment.  Such is the importance of churn it is discussed in a separate post.

From this analysis, we derive three data points:

  • Customer Lifetime Value (LTV $));
  • SAC Payback Period (Months); and
  • Return on Investment.

The interpretation of these is fairly straightforward.  It is better to have high value customers with a quick payback time and high ROI.  In practice there is often a tradeoff.  Some companies have longer payback periods, but higher overall ROI due to lower churn.  As companies develop, there is often a tradeoff to invest more in marketing for levels of ROI that are lower in a percentage sense, but generate more absolute dollar value.  Like all analysis, the numbers must be interpreted in their systemic context and placed alongside valuations.

Read More:

SaaSy Stories Pt I – The Fundamental Value Story of SaaS

SaaSy Stories Pt III – Churn Baby Churn