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Since the first transistor, increasing speed has been at the core of much innovation in Silicon Valley . Over more than three decades, Moore’s Law has remained the engine of progress in chip technology. I’ve been wondering if a analogous productivity law will be written for data.

One level up the stack from the chip, software has benefitted tremendously from chip speed. I learned to code in Java, which I needed to compile before I could test my program. Each compilation required 3 or 4 minutes and this latency slowed me down. In comparison, the languages dominating today’s programming stacks provide instant feedback. I saw 90% time savings to write code when I shifted to Ruby.

Most SaaS companies provide tools to help people accomplish a goal in a better way than they could before. A key part of a SaaS startup’s toolkit, then, is changing end user behavior. A startup that doesn’t change the behavior of a customer will see the customer churn in a few months or at the expiration of their contract. Customers don’t change their behavior for many reasons. Sometimes the friction to adopting a new workflow is too great. Other times, the value proposition isn’t compelling enough for users. Or, the use case is too infrequent for users to remember to change behavior.

Over the last 12 years, the number of startups founded has grown each year by 25%, according to Crunchbase data. That’s quite an acceleration each year! See the chart here. As the number of companies in a sector grows, do the odds of successfully raising capital decrease?

The chart above shows startup company formation rates, the number of new companies formed each year from 2004-2011 by Crunchbase sector. I didn’t graph the 2012 or 2013 data because the Crunchbase team told me the data sets need about 2 years to mature. Most of the categories are up and to the right. Advertising, hardware, messaging, music and PR are the notable exceptions with substantial decreases. On the whole, then, startups are competing with more and more other startups to raise capital.

Raising capital from venture capitalists at any stage can seem like a very strange, ambiguous and amorphous process. I’ve written about the way Redpoint diligences/researches a startup and its market and what questions we tend to ask at each stage. In this post, I’ll focus on the process from entrepreneur’s point of view.

When raising capital, entrepreneurs will see potential investors move through four phases of investment decision-making process: screening, socialization, diligence, and decision. I’ve drawn a schematic that illustrates this evolution above. The chart also shows a line indicating the progression from one step to the next, using data from my own investment funnels.

The process of creating the right culture in a startup has always been mysterious to me. Each company’s culture evolves in its own way. I’ve wondered whether the culture is set by the personalities of the founders, or prominently displayed value statements and mission, or biases purposely imposed in the hiring processes like Google’s googliness filter. Or is understanding the psychological forces at play among employees the most important element?

A few days ago, Simply Business published an infographic and data on the acquisition patterns of Amazon, Apple, Facebook, Google and Yahoo. Looking at that data, I wondered which acquirers pay the most for startups. Ideally, this data provides some negotiating leverage to founders selling their businesses.

I’ve prepared three charts and a table to tell the story. The first compares the average acquisition prices over the life of each of the tech monoliths. The y-axis is in log scale. A value of 2 means $10M^2 or $100M. On the whole, the average acquisition prices are all in the same ball park. Facebook and Google have outliers, demarcated by dots, which are WhatsApp and DoubleClick, Nest, Motorola for Google.

From zero to $19B of business value in five years; WhatsApp’s sale to Facebook is an important moment in the history of the consumer web. The deal proves distribution, reach and large user bases aren’t the competitive moats they once were. Apple’s App Store and Google Play have leveled the playing field to such an extent that a startup can command 10% of the market cap from a $200B company.

When a startup is confronted with the prospect of hiring a head of marketing, founders heads often spin.

What should be the day-to-day tasks for this person? What skill sets are important? Because of the seeming abstract nature of marketing, founders sometimes delay finding a head of marketing until they feel acute pain, at which point they can clearly identify the attributes of the right candidate. But underinvestment in marketing, like underinvestment in infrastructure or software or product, isn’t a good idea.

Great entrepreneurs can come from anywhere. But do the locations of startups affect their ability to raise follow on capital? Do seed stage companies in the Bay Area face lower likelihoods of raising a Series A because of more competition? Or is it that New York based startups, because of a smaller ecosystem, face more difficulty?

Using Crunchbase data, I charted the financing follow-on rates across the 12 US cities in which at least 10 seeds, 3 Series As and 3 Series Bs have occured in the Crunchbase data set from 2005-2014. The first two charts below contrast the success rates of post-seed startups raising an A having raised a seed and raising a B having raised an A. The third chart shows the success rates of raising a B having raised a seed round.

When presented with figures and numbers and statistics, it’s easy to take the conclusions as fact. Numbers in a spreadsheet carry a finality, a exactitude that belies how inaccurate they can be.

In 2005, Stanford professor Johannes Ioannidis turned the world of research and statistics on its head. He published “Why Most Published Research Findings Are False.” Ioannidis’ paper cast doubt on decades of research. More than 75% of experimental results published in the world’s best journals couldn’t be replicated. The conclusions were results of bad experimental design, biases in the data, and statistical tools used incorrectly. A crisis of truth ripped through the research community. For the first time in about 90 years, researchers and statisticians are re-evaluating their methods.