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. 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? One could assert million dollar contracts typical of enterprise sales provide more long-term stability and total revenue opportunity. Others may contend, larger customer bases paying smaller license fees enable more predictable growth. Which is the correct argument?"
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."
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.
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."
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."
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? After all, most of these companies aren’t infrastructure companies nor are they software companies. Do any of those models hold lessons for B2D companies?"
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. In contrast, one of the companies I work with, Axial, has honed and refined their ability to forecast, promise and deliver code with remarkable consistency."
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."
As recently as six months ago, it was easy to disregard the Internet of Things (IoT) as just a theoretical market that Cisco measured in the trillions, but whose potential never seemed to materialize. That’s all changing. The past year ushered in a new era for the Internet of Things for three reasons."