SaaS companies carry huge valuations when compared with traditional licensed software companies. It took me forever to figure out why, but I did.
At first blush, one might believe that Wall Street values subscription revenue over one-time revenue—and truth be told, they do. Why? Because it’s a hell of a lot more predictable revenue stream for a company to manage than ad hoc revenue. I get that, but the gap is still too big for what, in reality, is simply financing.
Then it hit me. The big SaaS guys have one very special secret weapon in common: They have all the data. Salesforce has not only all of your customer data, but also all of your company employees’ data—it knows who does what, when, and how. Therefore, Salesforce can run analytics across its data population and be prescriptive. It can tell you that you are using things right, or wrong. It knows that your peers are using its service more effectively than you are and can suggest things to help you improve. It knows that no one ever uses a certain feature, so it can eliminate that feature—or that only companies in some specific segment of the market use another feature, so they can make sure it shows up on your dashboard if you are in that segment.
Analytic opportunities are what makes these types of companies so valuable—not just their offerings.
There is good reason to believe that Microsoft is soon going to get into the managed service game with hardware, not just software. Why not? Microsoft already knows how the world works with Office 365 because it has all the data. Now it will be able to extend that capability to your Surface device. Microsoft knows what is going to break when, how, and why, way before it happens. So, sure, it’s valuable just to use OpEx to run my device, the OS, and the application stack for a monthly fee, but it’s more valuable to both a user and Microsoft to be prescriptive and predictive, which Microsoft can do because it has all the data.
Which gets me to the new buzz: artificial intelligence (AI) and machine learning (ML). They sound like far out concepts but what they really are is decision support at a massive scale. It’s mega-analytics and it’s all predicated on the same basic fact: If you have all the data, you can know things. You can predict things. You can make smarter decisions, faster, and you can constantly nuance those decisions based on new data inputs.
So those with all the data are valued far beyond what their revenue lines would normally indicate. It’s not what they “do,” it’s what they “could do” that makes them so valuable.