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What could be more natural than a marketer selling a product to other marketers? Or an engineer pushing a new devops tool to other developers? Or a customer success person pitching CS tools? After all, they both speak the same language, come from the same domain, will develop trust quickly. Consequently, they will sell faster and more efficiently. This might seem like a very logical argument for differentiating on sales processes, but it’s a fallacy.

We’ve entered an era when computers can understand speech, computers can synthesize speech, computers can develop music, author encryption algorithms, create novel art, respond to customer support questions, and even generate new summaries and reports from data. Increasingly, humans will struggle to distinguish between computer-generated and human generated. Consequently, here’s an opportunity for startups to lead, not just technologically, but more broadly.

DT is a creative digital agency business based in Australia that developed a prototype that distinguishes between human speech and synthetic speech. Shaped like a hearing aid, the Anti AI AI chills the listener behind the ear using a Peltier device when the onboard TensorFlow model detects computer-generated speech. It’s one of undoubtedly many technologies which will use one machine learning model to detect another machine learning model.

When I was taught Michael Porter’s value chain analysis, I learned to analyze at the industry level. A beer supplier sells to wholesaler sells to distributor sales to retailers sells to customer. But as I went back last night and reread Porter’s Competitive Strategy, I was surprised to learn that Porter’s intended value chain analysis to be used also at the business unit and at the company level.

In January 2010, Andrew Parker wrote a post called the Spawn of Craigslist. Andrew identified companies that had built businesses by unbundling Craigslist. The vacation rentals link gave rise to AirBnB and HomeAway. Etsy dominated the arts and crafts for sale. This same unbundling is occurring to Excel.

Microsoft Office has more than 1 billion users globally. Assuming a 33% penetration of Excel, that’s a user population 300M users. Like Craigslist in the consumer world, Excel became the tool for nearly everyone to get stuff done at work.

In Bias Against Creativity, a team of researchers at Cornell discuss the bias against creativity they revealed in their study. Originally published in 2010, the article resurfaced yesterday on Hacker News. It raises the question of how to evaluate creative ideas and how to engender internal incentives to support creativity.

From the Cornell paper:

People often reject creative ideas even when espousing creativity as a desired goal. To explain this paradox, we propose that people can hold a bias against creativity that is not necessarily overt, and which is activated when people experience a motivation to reduce uncertainty.

As the number of SaaS applications has exploded, the SaaS ecosystem is responding to data fragmentation with middleware. This isn’t the middleware of the early 2000s, which was focused on helping developers build software. This is middleware that is focused on helping end-users unify data from the vast numbers of data repositories now existent.

Ten years ago, sales teams captured basic data on customers and prospects. Names, addresses, budget, time frame. All of it collected over the phone.

How much revenue do you want to book for your SaaS startup next quarter? And in 12 months? It is one thing to put a number down on the financial plan. It’s another thing altogether to have the sales team staffed to close that amount of business.

The bookings capacity of the business is the amount of business a sales team should book in a certain period. Bookings capacity is calculated as:

In January, I wrote The Hardest Round to Raise which argued Series B rounds would be the most challenging early stage round in 2017. Irrespective of the annual vicissitudes of the fundraising market, Series Bs are always the most challenging rounds to raise because they are in-between rounds. The Series B is the pimpled and gangly adolescent phase of startup evolution.

If the aim of every round of venture funding is to prove a hypothesis - which I believe is true in Series Seed, A and B - then the Series B is the last milestone before proving cash is the business’ limiting factor.

When building a SaaS product for salespeople, a startup’s price will inevitably be compared to Salesforce CRM’s cost of about $150 per seat. How expensive is this new product compared to Salesforce? In diligence calls, I often hear buyers say: one-half of Salesforce’s price seems expensive; one-third might seem more reasonable. This is the price anchoring effect in the real world.

A founder emailed me last week to raise the question of whether performance pricing for SaaS companies is an effective technique. Performance pricing means explicitly pricing of product in terms of the customers’ revenue gained or cost reduced from its use.

Conceptually, performance pricing is very rational. The buyer should be willing to pay between 10 to 15% of the revenue or cost savings for the use of the product. And as classical economics instructs us, this type of pricing mechanism optimally aligns the incentives of the buyer and the seller. But the reality is more nuanced.