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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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Aside from a startup’s internal considerations about the right time to raise money, founders should weigh the seasonality of the fund raising market when planning their raise. There’s a rule of thumb batted around the valley that the worst times to raise capital are in the dog-days of summer and after Thanksgiving. As it turns out, this aphorism is only a half-truth.

Below is a chart of the dollars VCs have invested by month of year. I’m using Crunchbase data since 2005 for tech companies in the US. There are a few notable trends in the data.

Last week, I spent some time at HeavyBit, the community for developer focused companies in SoMa, chatting with a few companies reaching scale. Across a handful of meetings, a recurring theme surfaced for these B2D (business-to-developer companies).

How should their sales and marketing apparatuses be built? Do the field sales models of infrastructure companies or the inside sales models of software companies apply when the initial user is a developer?

When I worked as an engineer, I loved crafting code and feeling the satisfaction of having built something each day. But there was one thing about coding I never grew to love, despite its importance: forecasting my coding time.

Every two weeks, I trudged into a planning meeting that exposed my incompetent forecasting. During these meetings, each person in turn would review their commitments for the last two weeks and provide an update. Inevitably, I was wildly off. Chalk it up to inexperience, exuberance or ineptitude, but I never developed the knack.

While the phrase data scientist may be growing exponentially in its usage, and the number of data scientists job requisitions following a similar trend, the definition of the term is hard to pin down precisely.

I wasn’t sure I could define it well until I watched a talk by Hilary Mason, former chief scientist at Bitly, called Dirty Secrets of Data Science at a NYC meetup. During the presentation, she highlighted a chart created by the Data Community DC team that demystifies term data scientist.