For hundreds of startups in the as-a-service world, the scores of product launches at last week's Amazon Web Services Reinvent Conference each were a warning shot across their bows. We are coming. We are coming right after you, with tens of billions of dollars on our balance sheet, hundreds of salespeople, and the broadest suite of software and infrastructure since Oracle. Anything that's open source with traction, we will host. Any business where we see margin is our opportunity. We are coming fast and hard. At least, that's the way I interpreted it.
In June, Frank Bien and I published our book, Winning with Data. It describes through case studies how some of the most successful startups use data to create sustainable competitive advantage. Since then, we've sold thousands of copies. Today, we're releasing an Audible version of the book.
Bookings, MRR, Revenue. All these metrics form part of the financial statements of SaaS companies. For as long as SaaS companies have existed, we've used one way of counting revenue, called GAAP. Starting in 2017, revenue recognition for SaaS companies will change, and SaaS startups will have more flexibility in the way they record revenue than in the past.
As a SaaS startup begins to reach critical mass, the business generates more of its revenue from upsells and expansions, reaching about 30% at between $40-75M in revenue, which is in line with some of the models we've created. Many times startup teams ask how to compensate a sales team for renewals and upsells. The 2016 PacCrest Survey contains a wealth of information about these types of go to market questions.
Numbers provide us a certain certainty. With their precision, they offer a sense of black and white, in or out. But, metrics alone aren't enough. All the quantitative analysis in the world won't lead me to the next great idea for startup. Those figures can't create empathy, develop the right culture, or hire the right people. I've been thinking about this quite a bit because in both the recent Software Engineering Daily podcast I did with Jeff, and the presentation I gave at Launch Conference, the question of the limits of metrics surfaced.
Earlier this week, I published benchmarks on What Percentage Of Revenue Should SaaS Startups Spend On operating expense? Several founders asked to see this data broken down further. What fraction of operating expense is spent on sales & marketing, and what fraction of operating expense is spent on engineering? Most businesses spend 2x more on sales & marketing than engineering.
Imagine you've just been named the head of a bustling New York City restaurant challenged by one issue - customers complain about the customer service. A data-driven person, you search for a metric to evaluate the current customer service to validate the complaint and then track as you experiment with the restaurant's operations. What metrics would you employ?
In 2001, Salesforce spent $35.6M on payroll and generated $5.4M in revenue. NetSuite spent $38M on payroll generated $17M in 2004. as both of these companies scaled and approached IPO, the operating expense ratio (OER) or operating expense divided by revenue, asymptotes to 0.8. For every dollar of revenue, both of these companies spent $0.80 in payroll at scale.
A few weeks ago, I had first my customer support experience of the future. I was in a meeting when my Android's caller ID told me American Express was calling. I stepped of the conference room and answered the call. A machine-generated woman's voice identified itself as the American Express fraud department. "Do you have a bluetooth headset or headphones you can use with your phone?" she asked.
Public companies are often required to disclose the process of their acquisition. LinkedIn's sale to Microsoft is described step by step in an SEC disclosure and it offers both a peek into how these massive acquisitions are consummated, and also illustrates the best practices for how to run a process, both acquisitions and fundraisings.