Understanding SaaS: Why the Pundits Have It Wrong. Tune into any cable network stock market channel and the airwaves resonate with one consistent theme: SaaS companies are simply too expensive. In fact, we might even be in a bubble! The argument goes as follows — high revenue growth coupled with lack of profits means these businesses are fundamentally broken. Just as we saw in 1999-2000, investors’ willingness to pay for growth at any cost will end and many SaaS companies will be left behind. image: Andreessen Horowitz But that line of reasoning conflates the lessons of the 1999-2000 tech bubble. The businesses that failed in the last tech bubble were valued on metrics that were both poor indicators of business health (“eyeballs”, anyone?) So why do the pundits have it all wrong? When it comes to SaaS, however, such simplicity can lead to bad investment decisions.
The key difference between traditional software and software as a service: Growth hurts (but only at first) Now compare that to what happens with SaaS. Here’s an example. SaaS Metrics 2.0 – A Guide to Measuring and Improving what Matters. The 5$ SaaS. The 5$ SaaS. The the relatively recent rise of two big types of software pricing & distribution is a opportunity to notice how different things in markets interact in these markets. If you spend your career in software you forget how weird an industry with n marginal costs is. An app store lends well to $1-$10 apps creating a demand creating a supply. Web based Saas lends well to $5-$50 apps creating a demand creating a supply. If you need a sales consultant or a long decision making process you need to go into the 5-6 figures realm.
These things didn't happen because consumers really wanted smartphone apps that happened to cost around $1 or webapps that happened to cost $5 per month. The marble dictated the sculpture Consumer webapps need to be sold as monthly subscriptions.
From Startupcfo. Financial Planning for Saas. Defining Churn Rate (no really, this actually requires an entire blog post) – Shopify. By Steven H. Noble If you go to three different analysts looking for a definition of "churn rate," they will all agree that it's an important metric and that the definition is self evident. Then they will go ahead and give you three different definitions.
And as they share their definitions with each other they all have the same response: why is everyone else making this so complicated? How can it be so confusing? Unfortunately, churn rate is actually an extremely important metric. Let me take you through the recent stumbling process we went through at Shopify and share with you the definition we ended up with. The accountants' dream As I say, a churn rate seems simple. The problem here is that the [number of churns over period] value is affected by the entire period but the [number of customers at beginning of period] value is a snapshot from the beginning of the period.
Consider you are calculating your churn rate for July and August. The accountants' adjusted dream We also tried: or. Deep dive: Cancellation rate in SaaS business models. I wanted to expand on the practical and mathematical implementations of the cancellation rate I referred to in last week’s post. Why cancellation rate is so important As a preamble to the metrics, it’s useful to know what you’re measuring and why it’s vital. [Cancellation rate] = [product utility] + [service quality] + [acceptable price] I put in these particular elements because I did a study of the reasons people cancel at WP Engine, and these are the main reasons for cancellation. We log every cancellation – spending time running after folks to wring out the cause — so we can deduce exactly what we can do to prevent it in future.
(Of course you should do this too and get your own data.) These three factors are, of course, critical to a healthy, growing startup, and yet individually they’re impossible to measure as precisely and easily as cancellation rate. Barely anyone on Earth will ever power through this gauntlet. And then, after all that… they cancel! LTV, my way P.S.