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.
Earlier this week, I published benchmarks on [What Percentage Of Revenue Should SaaS Startups Spend On operating expense?](http://tomtunguz.com/how-much-should-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 op 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?
You might run a survey of customers at exit. You could follow with customers by telephone the day after the meal. You might ask finance to tabulate the tip amount as a percentage of the total bill and aggregate by waiter.
What percentage of revenue should be spent on payroll? 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 my first 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.
I replied that yes, I did. She said to tell her when I was ready. So I plugged in my headphones and said, “Ready.” Then she asked if she could send me a link to a website by SMS. “Yes.” The message came and I clicked on the link.
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.
The timeline above shows how the deal progressed. Five potential bidders are included in the chart including Party C, whose identity is unknown. Note at the beginning of the chart, the bids for each suitor are illustrated as non-zero to separate the lines and make clear each suitor’s behavior. None of these suitors posted offers until May.
About $1B has been invested in early stage SaaS startups as of November 1. Over the last nine months, marketing startups have raised more dollars in aggregate than any other segment. The chart above shows the early-stage investment dollars by buyer within the organization. Operations teams following second, with human resources focused startups in third. Notably, sales startups raised the least amount of capital.
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.
At my first programming job, I met a colleague who took all his notes in XML. He liked the fact he could structure them well, create programs to search and process them, and display them in many different ways. Most importantly, he future-proofed his notes. Because they were structured, he could transform his notes into any new format. That was my first exposure into the world of productivity hacking.
Demand generation is a critical limiting factor to the growth of many startups. I had the opportunity to moderate a panel of demand generation experts recently at Heavybit, an incubator in San Francisco. I asked the panelists, how well understood is demand generation, considering it is one of the core elements of business needs to sustain its growth? Unanimously, the panel concluded it’s not very well understood.