This posting is spectacular in quality compared to what we usually get to see here.
1) It uses proper graphs for information display. Note the box plots with quantiles, and properly scaled axes. And the first plot which denotes the confidence intervals.
Yes, it makes it possible to clearly see all the errors.
Someone remarked that the numbers add up to more than hundred percent for the first year, and the confusion is because the y axis is mislabelled. It should say "Cost for team as percentage of revenue".
Also, the first two years shouldn't be plotted as part of the line as they are just two data points in total for these years and since there is considerable spread in better represented data we know any single data point is probably not representative. At least not if you insist on using decimals.
It's also a bit unclear what the line for "more than seven years since funding" comes from, since the provided summary table only shows up to year 7. It seems the data table truncates, but there are two companies that cover age 16-24 in the spreadsheet, once again giving a very small sample.
On that first graph, for year one the percent of revenue spent on the team (using the means) exceeds 100%...? The mean for R&D is ~50%; for S&M it's 90%. How does that work? Is this due to fundraising?
This is one case where it would be nice to see the number of votes on a comment--I upvoted you because I agree immensely, and even though I also upvoted the submission there is no way for the community to know why things are getting up voted so we have to write follow-on comments like this one.
Many thanks to OP for an excellent write-up and I, too, would like to see more of this quality content on HN!
Unrelated to content: please, please, please do not add on select-actions like the tweet button that appears after selecting text. I habitually select text as I'm reading, and I know there is a significant group of people like me who do the same. Having a button appear on selecting text is sincerely annoying.
It was typically true that engineers have high leverage, productivity and scale, whereas sales people do not. When traditional enterprise software was sold, it was done primarily through the "hunter" sales model and sales people were compensated accordingly to bringing in big deals. Sales grew revenue, which meant that if you hired more sales people at an established company, you'd have more revenue. This is why in the 1990s, if you met a tall white guy at a bar in San Carlos driving a nice car, he was probably an Oracles sales person (a bit of hyperbole).
The nature of enterprise sales is changing, not just because it's gone to SaaS/subscription. One of the key changes in the last few years is the growth of measurable analytics in marketing. You see, marketers and sales people are not the same. In the old days, marketers couldn't measure the impact of their marketing investments. There were loose correlations between marketing and the leads they generated for sales. But now, because of analytics derived from marketing software, you can actually draw conclusions on which marketing campaigns were the most effective from impression to conversion.
What does this mean for this post? 1. I think we're going to see fewer sales people per engineer. We likely won't see a huge growth in R&D spending; we will see less dollars spent on expensive sales people. 2. Marketers will become more hybrid product-sales-analytics people (like growth hacking), which means there's a great opportunity for data-driven commercially-focused engineers to transition towards this greenfield area in marketing.
TL;DR: Selling enterprise software has changed so the ratio of sales:engineers will go down in the next 10-15 years.
First, these ratios aren't a recipe for success, just data to inform decisions. Each business is different because of the industry they serve, the go-to-market strategy and the particularities of their customers acquisition processes.
I wonder how much of the prioritization on spending fed back into the success of these SaaS companies, and how much of it was due to prevailing trends in management theory that influenced the course of the startup as it was given funding and ramped up? In other words: how much of the ratio of spending on developers v. sales and marketing was data-driven, and how much based on fad? I get the sense that there is a bit of an echo chamber in the tech industry that can influence what businesses do when they receive angel funding and VC.
I actually really like the graph in the article because I think it portrays the value of engineers. They're expensive up front, but if they use good programming practices, the work keeps paying dividends again and again over time without a whole lot of additional investment.
Nice information, but what would be more useful is to add a line showing gross profit. We would amuse that profit would be low in the first few years and then increase as the years pass, showing the decline in the money spent on engineers and sales staff. Although it would be interesting to see if gross margin is falling 20+ years in, as sales staff focus is increased.
1) It uses proper graphs for information display. Note the box plots with quantiles, and properly scaled axes. And the first plot which denotes the confidence intervals.
2) Actual numeric data is shown in tables.
3) The source data is avialable.
Well done! More of this, fewer "Infographs" plz.