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In 1964, IBM announced a mainframe computer family called the System 360. The mainframe wouldn’t ship for another three years, but the announcement reduced the mainframe sales of their competitor, Control Data Corporation, sufficiently to warrant an FBI investigation. And so a new marketing technique was born.

To be clear, there are many different forms of vaporware. Coined in the early 80s by Esther Dyson to describe software companies preannouncing a product, the term vaporware can refer to three different types of these announcements. There’s IBM’s practice of pre-announcing a product with the intent to gain competitive advantage, which violates antitrust law. Second, vaporware also refers to products that were announced but never shipped like the iPhone predecessor, Apple’s Wizzy Active Lifestyle Telephone or WALT.. Last and most useful, vaporware is software that’s pre-announced to gauge customer demand.

The purpose of a price is to tax usage of a product. That’s how companies generate revenue. Discovering how to tax a product properly is a perpetual challenge. It’s a moving target and so it requires an ongoing discovery process as the company and market evolve together. These are some mistakes I’ve noticed.

Complex or unintuitive pricing model. A good pricing model appears simple and logical to the customer. It may be complex behind the scenes, with different prices for varying customer sizes, product complexity and add ons, but the tax align itself to the customer’s perception of ROI clearly. Customers have their own unit of measure. Often for applications, it’s people - hence a pricing model by seats. Other times for infrastructure, it’s bytes for storage or cycles for compute.

Over the weekend, it seemed to me that the sentiment in the valley seemed to change abruptly. Dan Primack wrote Fear and Sadness in Silicon Valley, and Danielle Morrill punctuated her post Somewhere Over the Brainbow: The Unicorn Window is Closing images of unicorn cannibalism. While the attitudes may be changing, the effects haven’t yet revealed themselves in the data. So, is the environment truly changing?

In sales processes, showing a product is always better than talking about a product. Better still is co-customizing the product with the customer during the sales pitch. This customization could be as simple as integrations or changing colors. There’s no better way for customers to understand a product, imagine how it would fit their needs, and become committed to the purchase than customizing their instance during the sales process. I’ve watched this brilliant sales tactic fuel tremendous growth at Looker, a fast growing analytics company.

When I was a PM at Google, we conducted customer research often to understand our customers’ opinions on AdSense. In 2005, Google, Yahoo and Microsoft were vying to win dominant share of advertising pages across large publishers. Customer knowledge, both qualitative and quantitative, informed product development, and that research became a key part of AdSense’s success.

A few years later when I joined Redpoint, I learned that venture capitalists perform similar customer research during diligence. While the ultimate use of the data might differ, the actual investigations and interviews are remarkably alike.

Starting on October 21, I’ll be hosting a bi-weekly event from the Redpoint San Francisco offices called SaaS Office Hours. During these two hours, we will discuss the tactical issues and questions facing seed and Series A SaaS companies in a small group. That’s why we call them Office Hours.

Rather than deliver presentations, SaaS Office Hours are meant to be casual, tactical and collaborative. Sometimes, we’ll invite guests for off-the-cuff conversations and Q&A focused on focused questions like how should I build my startup’s marketing team? How can I evangelize my product to developers? How do I create the right kind of recruiting process?

How big is your SaaS startup’s sales pipeline? How big does it need to be to achieve next month’s bookings target? What is the ratio of the sales pipeline to bookings? What should it be?

When asked these questions during a fundraising pitch, one CEO responded with the number of demos per account executive per day to attain next month’s bookings, impressively conveying his command of his business. A startup’s leadership should know the number of customers, sales and marketing qualified leads to meet or exceed plan at all times. If you haven’t performed this exercise this before, use the interactive worksheet below. Change any of the text boxes and the figures will update. If you’re reading this post by email, please click through. Some email clients don’t allow JavaScript for security.

Customer pull is an amazing feeling for a startup. Because the customer demand is visceral, everything seems to move quickly: sales is booking deals quicker than can be on-boarded; product and engineering are rushing to build the features customers request; marketing’s efforts to raise awareness of the business are magnified by word of mouth; and recruiting is doing all they can to fill the looming vacancies.

“What is the one equation that describes our business?” asked Scott, our new director at Google, during one of our first meetings. I had been there only for a few quarters, so I was startled when he asked. I had never viewed our business this way, but after he asked the question, I wondered why I hadn’t. It seemed obvious in retrospect.

Today, 70% of startups in the US that raise a Series A have raised a seed round. That’s up from 50% ten years ago. In the same period, the amount of seed capital invested in the US has increased about 10x from $200M per year to $2B. What does this imply for early stage founders?

First, it implies greater competition at the Series A. Larger seed rounds enable a seed stage company to achieve more - more growth, more revenue, more hiring. In addition, these larger seed rounds enable successful startups to raises larger series As, and the data supports it. The correlation coefficient between seed round size and Series A size in the Crunchbase data set is 0.89 across all startup sectors.