Calculating SaaS Startup Business Value with the Formulas used by the Experts

Leveraging SaaS metrics are key to plotting out a company’s success; make sure they don’t rely on assumptions that don’t match the fiscal reality.

The SaaS industry is not unlike the English colonization of the new world. Early colonists left no permanent settlements, only ruins and mysterious carvings in trees. Later colonists successfully created the colonies that became Boston and Philadelphia. In the same way, some SaaS companies barely made it off the ground while others have achieved startling growth and profitability. While we know why some colonies failed, we’re still not 100% sure why some SaaS companies become financially viable and others did not. Here’s a deeper look into what we do know.

A company that doesn’t keep its CAC to LTV ratio above water is a company that drowns. For a SaaS company to make the right decisions, nothing is more important than metrics—metrics can tell you what position your company is in, what your advantages are, and what moves to make in the near future. Metrics are the difference between becoming that shining city on the hill and being nothing but a word carved in a tree for future generations to stare at.

Some leading influencers have been looking at just how to benchmark the success of SaaS startups. Tom Tunguz, David Skok, and Brad Coffey have been using rigorous mathematical analysis to quantify various aspects of the SaaS world. From basic issues of financial viability, to cash flow issues, to our fundamental assumptions on how to measure growth, there are many questions that can be answered, or at least wrestled with by modeling and mapping SaaS trajectories.

David Skok observes that SaaS businesses face a risk of significant losses in their early years as “they have to invest heavily upfront to acquire the customer, but recover the profits from that investment over a long period of time.” In his view, it is crucial to model the potential rate of growth and see when the investment into a customer base turns into actual cash flow: there is usually a sweet spot when extra investment can push you into exponential growth. A good rule of thumb is that the LTV should be at least 3 times the CAC, and the CAC should be recovered in under 12 months.

Skok has seen exceptions to the rule, but usually on the other side: some companies have an LTV to CAC ratio as good as 7 or 8 to 1, and some companies recover their CAC in only 5 months. It’s possible for a company to fail to meet that benchmark and succeed, but it’s by no means likely.

Skok sees opportunities to overcome the churn problem by maximizing negative churn, a phenomenon where the expansion revenue from existing customers is greater than the lost revenue from churned customers. Expansion MRR added to new MRR from new customers, subtracted by churned MRR from lost customers subtracted gives you your new net MRR. This is your key metric.

Skok recommends that you track this metric using a similar graph to the one displayed below.

MRR picture

 

 

 

 

 

 

 

 

 

 

 

Coffey has a conservative approach to SaaS accounting practices: he recommends including total costs (salaries, overhead, etc) in CAC calculations: ignoring these costs is a ‘simplifying assumption’. While these calculations do make certain assumptions about cost per sales rep and price of leads, it can still be refined to deliver more accurate data. Brad Coffey also examines CAC by dividing it according to segment. Coffey calculates segmented CAC by separating marketing CAC from Sales CAC. Analysis with this methodology means you can tell which segments have the lowest cost and which have high returns on investment.

 

Sales and Marketing CAC

 

 

 

 

 

 

Furthermore, he writes that “although COCA (CAC) is valuable, real insights come when compared to other unit economic measures such as the lifetime total value of the customers in that segment.” Coffey also notes that the common targets of a 3:1 LTV to CAC ratio and Tomasz Tunguz has been pondering when to begin using metrics with a SaaS company. Out of habit, he’d been using years-since-founding as the origin on a time axis, reasoning that if he were a founder, that’s how he would think about it. But recently, he’s been considering several alternatives to years-since-founding; using time before/since-IPO for the time axis, grouping companies by ACV to compare growth rates, and grouping companies by Revenue at IPO to compare growth rates.

He’s found that using the IPO as the origin point is less useful than years-since-founding: while most companies fit into a linear distribution on a years-since-founding graph measuring growth rate, the graph that uses time since IPO is fair more irregular and full of outliers.

Grouping companies by sales efficiency and ACV likewise fails to produce patterned or useful data. Tunguz concludes that, as of now, growth rate over years-since-founding is the most useful way to quantify and bench mark the success of a SaaS company, although he is open to alternatives, should people discover them.

Although SaaS is a relatively new industry, it’s not so new as to be undiscovered. This is a blessing and a curse. While new SaaS companies may not discover an untapped gold mine or be the first to set foot in the new world, they can also avoid the mistakes of earlier SaaS companies that left them metaphorically starving during that cold, hard winter. The colonists who came to Boston and Philadelphia had seen the failures of the Jamestown colony. They had data on their side. It made all the difference.

Yael Slater