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Bill Gurley and Fred Wilson have focused on burn rates as an important topic for startups. The immediate question that follows this commentary is: How much does the typical startup burn throughout its life? And what is a “risky” burn rate for a company?

I use a rule of thumb to evaluate the burn rate of a Series A startup. I multiply the number of employees by about $10-12k, depending on the location of the company. For example, Bay Area salaries are higher than most other places. If a startup’s current burn rate substantially exceeds the this heuristic, I ask why. But like many others, this shortcut isn’t rooted in analysis.

The startups that build and retain the best teams develop a huge competitive advantage. It’s no surprise that managers are the most important influencers of team development and retention.

The most frequent and consequently most powerful tool for managers to coach, develop and lead their teams are one-on-ones, weekly meetings between a manager and his or her individual reports. Most one-on-ones are ad-hoc, loosely structured 15-30 minute meetings. While extemporaneous meetings can work, leaders who manage their teams this way forgo an important opportunity to further their team’s success.

I remember many the great TED talks I’ve watched. Sir Ken Robinson’s ,“How Schools Kill Creativity” and the story of a little girl whose genius was unrecognized in school until she was allow do dance, and ultimately became a prima-ballerina, is simply unforgettable. In most of my meetings, I remember Amy Cuddy’s “Body Language” talk for a split-second. Commanding her body language changed her career. And who can forget Steve Jobs announcement of the iPhone?

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.

All of the businesses we’ve looked at in the past have been purely SaaS businesses. Today, we’ll examine Tableau, the market leader for data visualization software. Tableau sells software the old-fashioned way, with perpetual licenses not subscriptions. Customers pay a fee for the software up-front, in addition to an annual maintenance fee of about 20-25%. To offset the mostly one-time payment from customers, Tableau employs a land-and-expand strategy. The company went public in 2013 and we’ll use data from their S-1 through 2013 to benchmark the business.

Jason Fried, co-founder of 37Signals and Basecamp, published a blog post today called Faith in Eventually that captures the emotional tensions of building a product:

During the development of most any product, there are always times when things aren’t quite right. Times when you feel like you may be going backwards a bit. Times where it’s almost there, but you can’t yet figure out why it isn’t. Times when you hate the thing today that you loved yesterday. Times when what you had in your head isn’t quite what you’re seeing in front of you. Yet. That’s when you need to have faith.

Startups are intense experiences. Driven by a burning passion to change some aspect of the world, startup teams push, push, push to grow as fast as possible. Without the right balance, though, teams burn out - a terrible outcome. One of the most important responsibilities of every startup’s management team is to shepherd their teams to maximize their performance and prevent burnout. But how?

Tony Schwartz founded The Energy Project, a consultancy that focuses on educating managers on how to manage their team’s energy, to maximize long-term team performance and well-being. The company works with Google, eBay and lots of other Fortune 500s. Schwartz wrote a book on the topic called The Power of Full Engagement. I haven’t read the book, but I’m a believer in one of the frameworks within it, the 2x2 matrix above.

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.

Storage is following a similar path. Dropbox is now selling 1TB for $10 per month and Google offers 10TB for $100 per month. Meanwhile, gigabit internet has been deployed in about 8 cities in the US so far with another 12 to 15 slated for next year, typically at $70-$100 monthly price points.

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?

Using a basket of about 45 publicly traded SaaS companies, I analyzed the sales efficiency of these companies by years since founding for different average contract value (ACV) buckets of $15k, $50k, $120k, and $1M. Then I calculated the sales efficiency by year-of-founding, in buckets of 2 years, from year 2 through year 10. To eliminate bias, I whittled the dataset to a subset of the companies who had data in all three periods.