Last week, Redpoint held our annual Founder Day gathering. At the event, I listened to the stories of Felix Baumgartner’s record breaking jump from 120,000 feet, heard about the astonishing comeback of the US America’s Cup team and took part in a creativity workshop led by a Stanford Design School professor. In short, the event revolved around doubt.
BYOBI is an acronym I first heard on a telephone call with a VP of Technology at a large corporation. The word is almost unknown today, but I think that it will be one of the largest trends to impact data in the next five years.
BYOBI means Bring Your Own Business Intelligence. This VP of Technology was struggling to enable the sales people, marketers, engineers and others within his business to access the data they needed. The monolithic solution that they had adopted, (I can’t remember if it was MicroStrategy or Cognos or BusinessObjects), satisfied the needs of the one department that had catalyzed the purchasing decision many years ago. Several years on, his team had become inundated with custom data requests that the tools failed to answer and petitions to purchase new types of software. Unfortunately, his data architecture just wouldn’t allow him to respond to those needs. The image below is a simplified schematic of his architecture.
I’m a perpetual freeloader. Like a houseguest who has overstayed his welcome with hundreds of people, I depend upon the generosity of strangers - in particular, software teams. I’ve used HelloFax to sign documents for years, but I haven’t paid them a nickel. The same is true for GMail, Google Docs, TripIt, TypeKit, UberConference, LogMeIn, Evernote, the list goes on.
For all of these companies and products, harboring freeloaders like me is part of their growth strategy. At the product’s launch, free plans generate the majority of new users and eventually customers for the business and in the case of B2B companies, leads for the sales team. The positive goodwill engendered by a free product feeds word-of-mouth and the product achieves lift-off. All of this is possible because in small numbers, the marginal costs to support perpetual freeloaders are insignificant.
One of the single most effective tools SaaS companies can use in order to grow faster isn’t tweaking the product in a particular way or implementing an AB optimization framework or adopting new marketing tactic. Rather, it’s financial judo for structuring contracts and cash collection.
Cash is the lifeblood of startups. Cash empowers management teams to invest in all kinds of growth mechanisms. So, it’s no surprise that maximizing a company’s cash to invest in growth is a good thing.
At an Internet of Things conference last week, I took part in a panel in which we discussed the future of connected devices. Will simple products win or will complex products dominate in the IoT?, we were asked. I think the question misses the point and raises another problem about the Internet of Things more broadly. It’s not about Things. It’s about Services. Software-as-a-Service and Platform-as-a-Service.
Simplicity and complexity are two sides of the same coin. Great products are simple on the surface, but devote tremendous energy under the surface to handle complexity on behalf of the user. Great products are like ducks. Ducks are calm above the water despite paddling furiously below it.
Fenwick’s report on the state of the venture market and I came across these three data points that summarise one facet of the market in Silicon Valley succinctly:
The late stage venture market has been booming spectacularly in Silicon Valley. This source of capital enabling companies to remain private longer and become larger before an IPO or sale.
In this week’s New Yorker, Jill Lepore reviews Cubed: A Secret History of the Workplace, a book whose author asks the question, what is the work place of the future?
The information worker is a relatively new concept. Peter Drucker coined the term in the 50s. By then companies had already developed new ways of housing information workers. The very first information workers were accountants hunched over “Bob Crachit” desks in the back rooms of factories. Booming railroad companies demanded more organization and created offices within the new skyscrapers along the Chicago skyline. With these new offices came stacks of paper and folios, and cabinets in which to file them. Then, the Mad Men wrought an era of typewriters and mahogany corner offices. Next, Bell Labs invented the suburban office park, moving offices from the city as part of post-war suburbanization and in the 70s, Herman Miller crafted the now-ubiquitous cubicle, which was called the “Action Office” when it launched. Oh, the irony.
Yesterday, I spoke on a panel at the Gainsight Pulse conference with Aaron Ross, the author of Predictable Revenue, Jason Lemkin of Storm Ventures who authors SaaStr, and Brian Stafford, a customer success expert from McKinsey. It was great fun to be on the panel and discuss how customer success is transforming SaaS companies by increasing revenue growth, decreasing capital needs, building better products and consequently retaining more customers. I’ve embedded my slides below.
Startups are in a state of perpetual change. During a startup’s first few years of establishing product market and winning the first set of customers, this state of change is obvious. But as a startup scales, the company must adapt by learning and reinventing. Whether it’s building the processes to grow the team, creating new sales and marketing initiatives to pursue adjacent customers, developing customer success teams or handling an unforseen crisis, this process of reacting to the market and evolving the company happens at every level in each function.
Predictability is sexy. Startups that have tuned their growth engines well enough to accurately forecast their growth, presuming these growth rates are attractive, will command much higher valuations in the market, simply because there is less risk in the company. As a result, investors prize these companies disproportionately.
The challenge with predictability is predictability isn’t an end state. A business doesn’t become predictable one day and remain in that state in perpetuity. Rather, predictability is a discipline that must be practiced by managers and management teams within startups.