From Olivier Verhage – Kennet Partners (@olivierverhage)

As revenue growth is one of the dominant forces in tech investing, it’s worth looking at how growth translates into pricing. For example, a company that will do €5M of revenue this year and €15M next year might deserve a rich valuation, let’s say €45M.  Investors in this case are willing to pay forward for future growth.  On €45M of revenue the valuation multiple is 9x, but on €15M the valuation multiple is 3x.  If the business were to slow its growth but still grow from €15M of revenue to €22.5M of revenue, then in retrospect investors might feel like their original investment was a bargain at 2x [very] forward revenue. But how do we know the price of growth?

To find out how growth rates correspond to valuation multiples, I decided to pull the data on 44 publicly listed Software-as-a-Service companies and ran a regression analysis on the growth rate (-X) versus the valuation (-Y). The respective valuation we are using here is the EV/forward revenue multiple (EV or enterprise value = equity value + debt). We are using 2016 forward revenue as investors are usually willing to pay forward for high growth companies and it gives a better approximation of future value. Now, to arrive at more sound, logical and well supported conclusions I firmly believe that you should always use some statistical techniques. A case in point is the use of linear regression analysis that helps discern possible relationships between two or more variables. You’re probably about to tune out now we are talking about statistics, suffering a bad flashback to that boring college class that covered the math you would never use. Wrong! This simple technique is actually of incredible value to many aspects of business valuation and since it can come in quite handy from a VC perspective, I thought it would be worth sharing with the broader community. Obviously, the methodology used here is all based on public SaaS companies which are naturally much larger in size and raised a ton of funding (> €100M), but it gives you a good indication of how growth translates into pricing.

Here’s the data I used:

Following the regression analysis on growth versus the 2016 forward revenue multiple, we find an R squared of 35% which indicates how close the data are to the fitted regression line. However, just because we find a meaningful value for R does not prove that the relationship is statistically relevant. The Significance F measure tests whether or not the relationship between the two variables is random or statistically significant. In performing the regression, we selected a 95% confidence level. Therefore, if the Significance F statistic is less than 0.05 (5%) then the relationship is statistically relevant. The table below shows the regression metrics and we find an F-value of less than 0.05, so we can conclude that the there is a significant relationship between growth rates and valuations.
Moving beyond the maths, shown below is a graphical depiction of the relationship between the 2015-2016 growth rate and the EV/Forward 2016 revenue multiple. I highlighted some of the B2B SaaS companies that I’m sure most of us will know:
The scatter chart gives a good overview of enterprise software companies that may well be trading below their inherent worth. According to the regression data, the companies below the trend line are undervalued. However, I have to point out that there are numerous other factors to take into account. Burn rate (negative EBITDA or cash flow), gross margin, churn (and upsell), product and market positioning are probably just as important. Yet, regression analysis is a good starting point for value investing and it can come in handy for private company valuations as well. The linear formula for the trend line is as following:

EV / Forward Revenue 2016 = 2.13 + (11.64 x growth rate)

​It obviously looks very different when applying the same formula to private tech companies. Public valuations are naturally higher due to easy liquidity (you can buy and sell whenever you want) and the fact that most listed enterprise software companies bring in well over $200 million in annual revenue (size matters), so we likely have to price in an illiquidity and size discount. In addition to that, we may have to account for the fact that growth naturally slows when a company becomes bigger in size. However, I’m sure this analysis gives you a fair idea of the mechanics behind the impact of growth rates on SaaS valuations. And just shoot me an email if you would like to get the full picture with underlying data