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Every software company competes with another — if not directly, then at least for budget. With global IT spending flat to down in 2015 and 2016, software businesses are fighting for share of wallet. At this point, the critical marketing imperative is to start a conversation with a receptive buyer, and do it thousands of times per year. But how?

I met a master software marketer last week, and he shared some of his wisdom. A luminary, he has built the positioning, packaging, and pricing for several multi-billion dollar software companies.

Esquire writer and master storyteller Cal Fussman describes the experience of interviewing his childhood hero, Muhammad Ali, in a podcast with Tim Ferriss. Fussman spends a week with Ali, during which he the Special Olympics and boxes with the great champion. But there’s one story that stood out to me.

Fussman and Ali must catch a plane, but they are running late. Later in his life, Ali suffered from Parkinson’s disease, which slowed him significantly. Fussman is worried about missing the plane, and is trying to hurry them through security.

The public markets have changed the way they value SaaS companies. The median forward revenue multiple for SaaS business reached its peak in February 2014, fell to its nadir two years later, and has since recovered, hovering at around five times forward revenue – where it has remained with little variance over the last six months. However, that’s not the whole story.

All the while, this brand building effort is a substantial investment in reducing cost-of-customer-acquisition.

Dale Carnegie’s seminal book How to Make Friends and Influence People introduced the powerful notion of social proof to millions. Social proof is the psychological phenomenon behind the power of word-of-mouth marketing. The old trope “No one gets fired for buying IBM” is a manifestation of social proof in an enterprise sales process.

Social proof is an incredibly powerful force. The Buffer team wrote a great post detailing the five types of social proof: expert, celebrity, users, wisdom of the crowds, and wisdom of friends. Sales and marketing teams already leverage social proof because it’s so effective. Many SaaS websites feature customer stories very prominently.

Draw an image of a bicycle that depicts how the bicycle works. You might draw something like this bike above.

Or this one.

Or this one. But as Gianluca Gimini discovered when he asked 50 people over the course of six years to draw a bicycle, most people cannot - despite their great confidence of the contrary. Gimini rendered these drawings (on the right) to highlight incorrect most people’s understanding of a bicycles anatomy truly is.

There are three ways to create negative churn that I have observed in the market. First, usage expansion. Second, feature expansion. Third, product expansion.

Usage expansion is the most common way to create negative churn. Utility based pricing models like buying SMS credits on Twilio, or compute on Amazon or data processing on Mulesoft lend themselves to gradually increasing account sizes, as customers use more and more of the product. Per seat pricing also leads to usage expansion if product lends itself to growing within an customer. Slack and Expensify might win more seats as word of the products get out and the company grows.

Founded in 2006, Mulesoft is an 850 person company based in San Francisco that builds data integration tools. The company started originally as an open-source product and then focused on its paid offering. Today, the business generates nearly $200 million annually in revenue, and is growing at 70%. The business filed to go public last week, and the documents reveal a very impressive business operating at scale.

We’ve taught computers to do many things. We’ve researched how to teach them to identify cats, spot fraudulent charges, even categorize cucumbers. But what can we apply in our daily lives that computers have taught us? That is the premise of the book called Algorithms to Live By. Which of the advances in computer science can be applied to laundry, choosing an executive assistant, picking the best strategic plan and optimizing your schedule?

From the millions of Amazon Alexas to the self-driving car, new products are coming to market infused with machine learning. The innovation offered by machine learning techniques are real, and they will changed the SaaS world. But how? How can startups use machine learning to their advantage?

There are four broad applications of machine learning:

1. Optimize - this morning, fastest way to travel from Sand Hill Road to South Park in San Francisco is highway 101. The job requisition for an account executive on our website uses too many clichés. To close more business, speak slower, talk about pricing later in the call, and use this case study.
2. Identify objects - the photograph you just took with your smartphone contains a cat. Find all red plaid woolen shorts in an ecommerce store. The CT scan shows high likelihood of Parkinson’s Disease.
3. Detect anomalies - your credit card shows a $10,000 charge for a piano from a store in Nairobi. Your server cluster is operating at historically high CPU usage. Customers are responding to this morning’s lead generation email at 25% greater rates than last week’s campaign.
4. Segment data - customers who come to our product through the mobile app store show 15% higher engagement.

These applications alone make for tremendous advances. But, combinations of these applications lead to incredible things. Object identification + anomaly detection + robotics = self driving cars. Or brick-laying robots that erect walls three times faster than humans.

One of the hardest thing to do in sales, especially for early stage SaaS companies, is to disqualify customers. When a startup disqualifies a customer, they turn away a revenue opportunity, a chance to add $1k of MRR or $3k of MRR, and meaningfully grow the top line. But if the customer isn’t the right customer, that incremental revenue bears a hidden cost.

In the earliest days of the business, those potential customers waving checks promise an attractive revenue boost. Imagine a startup at $15k in MRR. A customer prospect worth $2k in MRR is a chance of growing monthly revenue by 13% in one shot. In addition, a seed stage or Series A startup typically has a narrow customer pipeline. Because the number of interactions is “rare,” each interaction with prospect seems precious. The business feels an urgency to grow, so the temptation to accept an non-ideal customer increases.