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Has it become harder to raise money? is a question I hear all the time. On one hand, the total dollars invested by VCs is relatively flat at just under $30B per year, according to the NVCA. On the other hand, the stories of difficulty raising series As and Bs have become a steady drumbeat.

To get some sense of the patterns, I analyzed 917 companies from seed through Series B over the past 14 years, using Crunchbase data. I’ve divided the companies into cohorts by the year they raised their seed investment. Click on the charts to view interactive ones.

The average seed stage startup has a 20% chance of raising a Series A according to Crunchbase data for IT startups who raised seed and Series A rounds between 2006 and 2013. But this figure varies significantly sector by sector.

Below is a chart of the different startups’ sectors and their rates of raising Series A capital net of the mean of 20%. To contrast two diametric examples, 40% of seed-stage search startups raised Series As, while on average only 10% of hardware startups raise Series As. For an interactive chart, click here.

Naming your startup can be one of the hardest things to do when starting a company. Each founder must agree. The domain must be available to buy. Last and perhaps most importantly, investors need to like it because the first letter of startup’s name has meaningful impact on how easily the company will be able to raise money.

Whatever you do, don’t pick a name that starts with the letter J. Or K. Or Q. Instead, favor names beginning with T, O and A.

How large of a seed round should founders raise to maximize their chances of raising a Series A? Smaller seed rounds are simpler and faster to raise because they typically require fewer investors. They may also require less dilution because of the smaller investment size. On the other hand, to raise a Series A, the startup needs enough runway to hire a team and prove certain milestones to Series A investors.

In What’s Up with the Series A, Nikhil Basu Trivedi documents the bifurcation in the Series A market. While there are a handful of startups that raise blockbuster Series As of greater than $10M, the average Series A investment size remains relatively constant over the past 6 years just around $5.3M for US technology companies according to Crunchbase data[1].

After reading his post, I wondered if a big seed round is a leading indicator of a big series A. In other words, would larger seed rounds provide enough negotiating leverage in fundraising conversations to bolster average check sizes and increase pre-money valuations?

What should the optimal revenue per customer be for a SaaS company? I could say million dollar contracts typical of enterprise sales provide more long-term stability and total revenue opportunity. On the other hand I might contend larger customer bases paying smaller license fees enable more predictable growth. Which is the correct argument?

First, lets examine the relationship between average customer value and total revenues, to see if smaller customers create a glass ceiling for total revenue. Below are two charts of data from venture-backed SaaS IPOs from 2010 to 2014. The chart on the left shows the average revenue per customer in $k at the time the company filed their S-1. The chart on the right shows the startup’s trailing twelve months’ revenue at the time of their S-1.

OODA was a technique coined by John Boyd, one of the leading military thinkers of the last 100 years, based on the German’s Blitzkrieg-style warfare which prioritized speed and surprise over the traditional win, hold and grind attrition techniques of trench warfare. After @pmarca tweeted about the concept, I read one of the books on the topic called Certain to Win.

Boyd’s thesis is that leaders of successful teams have to enable their organization to move rapidly, which means empowering people at all levels to make decisions. Speed is a huge asset in confrontations in both business and war, particularly when there is a substantial size difference between two competitors, so the author writes.

Given all the momentum of the NoSQL movement, it would be easy to write off SQL-based technologies as forgotten, or simply standing still. But there’s a tremendous amount of innovation occurring in SQL databases. Amazon’s Redshift, an elastic data-warehousing solution launched in late 2012 is the most salient example.

Redshift’s ability to process huge volumes of data is breathtaking. When running Redshift on solid state drives (SSDs), one team at FlyData queried 1 terabyte of data in less than 10 seconds.

Vik Singh wrote a great post in VentureBeat last week titled “Why Salesforce Needed to Buy RelateIQ” in which he talks about a new era in SaaS, the Predictive Era, the era of intelligent software. We’ve just seen one of the first acquisitions in the category with RelateIQ*, but I believe we will see many, many more for a few reasons.

First and most importantly, prediction provides competitive differentiation in an increasingly competitive market. It’s no longer sufficient in most horizontal SaaS categories to provide a cloud-based alternative with similar features to traditional software incumbents. RelateIQ showed this to some extent in CRM. The company uses natural language processing to reduce data entry. But this trend is as true for email marketing tools as logs analysis, for calendars as lead prioritization tools.

An entrepreneur asked me the question, what is the maximum viable churn for a startup? Within that question, a few others are embedded. How should a founder think about trading off efforts to grow revenue and mitigate churn? What is the impact of account growth on net churn? Startups must walk a tight-rope to balance growth, churn and cash. Below is the framework I use for working through maximum viable churn.
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