It’s Q3 earnings season and about half of the major public tech companies and recent startup IPOs have reported their figures. I keep track of earnings to get a sense for how these companies perceive their markets. Meeting or exceeding earnings indicates companies can forecast their growth and demonstrates how predictable these businesses are. The more predictable, the more stable the business environment and consequently, the fund raising environment for startups.
The real promise of the Internet of Things isn’t simply linking millions of devices together, just like the real innovation of the web wasn’t networking a bunch of computers. Instead, the true and still unrealized potential of IoT is to transform business models; it’s enabling companies to sell products in entirely new and better ways that benefit both the company and the customer. Around the turn of the millennium, startups began using the web browser to deliver their products to consumers and companies.
At some point, most startups will begin to measure their customers’ happiness. Customer satisfaction is an important predictor of loyalty and can foster fantastically efficient word-of-mouth growth. Many companies employ Net Promoter Score to quantify customer satisfaction. NPS measures the fraction of a customer base which are promoters and detractors of a company’s product. I’ve been told that NPS scores greater than 50 are impressive, but this is simply a rule of thumb.
When we analyzed the impact of location on a startup’s ability to raise capital, we found no statistically significant difference. Startups in San Francisco, Seattle, Pittsburgh, Austin and many other cities all demonstrated similar ability to raise follow-on rounds. But is the same true for investors of various locations? Do investors across the US invest similarly across Seed, Series A and Series B? They do not. In fact, there is a statistically significant difference in investment patterns of investors depending on their location.
I met a really smart vice president of sales a few weeks ago working in a company with mid-market customer values in the $10-100k per year range. When I asked her about her sales process, she described how her team employs statements of work (SOW), which isn’t something I hear about very frequently in startups, despite the fact they are very powerful sales tools. Statements of work describe the proposed working relationship between a vendor and customer.
Startups struggle to set the right price for their products because pricing dynamics in the field don’t obey the laws taught in the classroom. The standard supply and demand curves, drawn above, imply that as price increases demand decreases; that buyers act rationally and that this law is immutable. But this simply isn’t the case. Buyers in the market place violate the traditional supply and demand model all the time.
Bill Macaitis, the former CMO of Zendesk, articulates how a SaaS marketing team should operate better than anybody else I’ve met. At a recent Point9 conference, Bill outlined the 9 marketing disciplines of great SaaS companies and how they fit together to create a marketing powerhouse. I’ve copied my notes from Bill’s talk below. Ops & Analytics Team The operations and analytics teams is the first marketing team every SaaS company should build because this team erects the experimental infrastructure for determining which marketing tactics are viable.
Of the ten most important metrics on a startup’s financial statements, revenue might seem to be the most important. But it isn’t. Gross margin matters more because it is directly tied to a company’s ability spend to grow and achieve profitability. Imagine two startups, both selling products at $1M price points. The first has 5% gross margins and the second has 95% gross margins. The first company will be able to spend about $50k per sale on Sales & Marketing, Research & Development and general operational costs.
In “Time to Hang Up on Voice,” Sam Lessin argues voice isn’t the interface of the future for three reasons. First, voice is hard to use in public places because background noise complicates interpretation, and because many people are in earshot, voice isn’t private. Second, speaking to computers is less efficient than typing or using gestures. Third, keyboards are better tools for editing text than voice. But I think he’s wrong if for only one reason: speed.
In the past, we have benchmarked the revenue per employee of large publicly traded SaaS companies and determined that the average is about $200k of revenue per person. But, that analysis examined revenue per employee that only one point in time. As Jesse Hulsing pointed out to me, examining this figure over five years reveals quite a bit about the health of the business. Jesse was kind enough to provide data on a handful of category defining enterprise companies, which I’ve used in this analysis.