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At the time of the IPO, the median Software-as-a-Service (SaaS) company generates $100M in revenue, creates $2.6M in profit and holds $85M in cash on the balance sheet. A company in this position typically raises $107M in its IPO and trades at 11x revenue, for a $1.1B market cap.

The path to getting there is revealing. Below is a chart showing the median revenue ramp of the 41 publicly traded SaaS companies by year since founding. In year 3, the median revenue is $11M. In year 4, the revenue figures more than double to $25M, and then again to $55M.

SaaS companies are the darlings of the public market. The average publicly traded SaaS company enjoys twice as strong a revenue multiple as ten years ago. SaaS companies’ time to IPO has been decreasing steadily from over 10 years since founding to under 7. Despite the decrease in time to IPO, the average dollars raised at IPO has tripled from the early nineties and grown by 50% since 2000.

I analyzed the 41 publicly traded SaaS companies comparing to understand the trends in SaaS IPO. The data set is here. Here are four charts depicting the trends.

One of the most important trends in the Internet at the moment is unbundling. Entrepreneurs are picking apart Craigslist and eBay, vertical by vertical. At the same time, other entrepreneurs have replicated the core functions and features of Facebook and LinkedIn, creating hugely valuable companies.

But simply calling this trend unbundling doesn’t do the movement justice, particularly in the transactional web. The trend is more fundamental.

Craigslist and eBay are the canonical internet market places. Craigslist is synonymous with internet classifieds. eBay coupled a payments system to the listings model, enabling user-to-user transactions at scale. Both eBay and Craigslist exposed the buyer to the seller in a transparent way. They avoid arbitrating or regulating disputes as much as possible, and push as much of the negotiation and price discovery as possible to the buyer and seller. For this, eBay charges 10% of gross merchandise value. Both companies provide large amounts of liquidity, a little bit of advice and take a relatively small stake of the transaction value.

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.