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This post is part of a continuing series evaluating the S-1s of publicly traded SaaS companies in order to better understand the core business and build a library of benchmarks that might be useful to founders.

In the two most recent analyses, we’ve explored the S-1s of Hubspot and Zendesk, two of the public SaaS companies with the smallest Average Revenue per Customer. Today, we’ll look at Veeva, masters of the massive enterprise sale, and one of the most remarkable SaaS businesses.

What will the world look like when cloud compute and storage are free?

Cloud computing prices are hurtling to zero. The chart above shows the logarithmic decline of the cost of a transistor cycle by 11 orders of magnitude (11 decimal places) over the past 40 years. AWS has decreased prices for EC2, elastic compute cloud, and S3, simple storage service, 42 times in eight years.

I wish I had been in Stanford’s CS183 class in 2012, the year Peter Thiel taught it. A student of the class, Blake Masters, copied all the class notes and I read every post, like thousands of other visitors to the site. In a few days, Thiel and Masters will release a book version of these notes called Zero to One: Notes on Startups or How to Build the Future.

I was given a copy at TechCrunch Disrupt. Over the past day or so, I’ve read it in its entirety. Every person curious about or in the world of startups should read it, because it contains so much original thought.

Is there an optimal price for a product to maximize a SaaS startup’s sales efficiency? As I’ve been analyzing the S-1s of publicly traded SaaS companies, most recently of HubSpot and Zendesk, I’ve been asking myself that question. Do million-dollar enterprise price points and field sales people create more efficient sales organizations than content-marketing-driven SMB startups? Or are the high-velocity inside sales teams of the pursuing the mid-market, the most efficient?

This post is part of a continuing series evaluating the S-1s of publicly traded SaaS companies in order to better understand the core business and build a library of benchmarks that might be useful to founders.

Zendesk is a 700 person company that builds customer support software. Zendesk went public earlier this year. It’s a remarkable business primarily because the founders and the team have built an incredibly efficient customer acquisition funnel. It’s important to note that Redpoint is an investor in and shareholder of the company.

Following this week’s post Benchmarking HubSpot’s S-1, Josh and Nikos raised an interesting question on Twitter. What are the right ways to benchmark SaaS companies from their early days through IPO? I have always used years-since-founding as the time axis to compare companies, because if I were a founder, that’s how I might think about benchmarks. But after their comments I wondered if there were better ones.

Some potential alternatives are:

1. Using time before/since-IPO for the time axis.
2. Grouping companies by ACV to compare growth rates
3. Grouping companies by Revenue at IPO to compare growth rates

First, let’s compare using years-since-founding to years before/after-IPO. Below I’ve plotted revenue growth for each of these two timelines.

One of the best ways I’ve found to understand SaaS companies is to pore through their public filings. A few months ago, I analyzed Box’s S-1. In this post, we’ll look at HubSpot’s IPO filing and compare their journey to a public company with a basket of about 40 other publicly traded companies, in the hopes that this data will help other founders chart their path to IPO.

In the next seven charts, we’ll explore how HubSpot built their business. We will explore revenue growth, average revenue per customer, sales efficiency, payback periods, net income, gross margin and engineering spending. In these plots, I’ve used HubSpot’s colors as a consistent legend. HubSpot company data is orange, median values are black, and other company values are gray.

Does a startup’s location impact its M&A prospects? We’ve already determined there is no material difference between the follow-on financing rates by geography. But do acquirers behave similarly to investors?

To answer the question, I’ve prepared three charts used Crunchbase data and focused on the seven states with more than 20 acquisitions since 2010. In the first two charts, we’ll compare the share of acquisitions by state to the share of financings by state in both number and dollar value, to get a sense of relative performance by state. The third chart shows the distribution of acquisitions by state.

Earlier this week, Google celebrated the tenth anniversary of its IPO. I re-read the Founder’s IPO Letter and found this passage which captured so much about Google’s values:

Google is not a conventional company. We do not intend to become one…We will not shy away from high-risk, high-reward projects because of short-term earnings pressure. Some of our past bets have gone extraordinarily well, and others have not. Because we recognize the pursuit of such projects as the key to our long-term success, we will continue to seek them out.

Though the term k-factor, a measure of the virality of an application, has waned in popularity since Facebook’s sheep-throwing glory days, the idea of spreading a product through referrals lives on. We all know a good referral mechanism when we see one. Dropbox’s invite-a-friend feature which awards free storage for both the inviter and the invited is the canonical example and resulted in torrid growth for the company. In April 2010, Dropbox users sent 2.8M referral emails. It’s these kinds of referrals, those that align the incentives of both parties and ones that are natural to the product, that seem to work the best.