Scale-stage firms are new ventures making around $500K a month in recurring revenues. They’re big enough to attract the attention of market incumbents, but small enough to still innovate. That means they have some unique problems. I’m gathering a few hundred people in October to dig into these issues, and hopefully generate practical solutions.
Rate of innovation
By definition, these companies have hit product-market fit; that means they’re no longer consumed with finding a sustainable, repeatable business model, but rather, executing on it. And that can be their undoing; they’re attacked by incumbents on one side, and upstarts on the other.
Uber, for example, is a big company, but it’s embattled by regulators and taxi firms on one side, while new entrants try to create Blockchain-based, decentralized rideshare companies that take far less money from drivers.
These organizations need to scale without hurting margins. That often means using tech on the back office to sustain growth and reduce error rates or increase consistency. But they’re frequently the shoemakers’ children, reluctant to adopt tech themselves.
Pricing your services
Emerging technology can be hard to price. For example, Machine Learning-driven solutions are often nondeterministic. When you can’t easily quantify the benefit a customer will enjoy, it’s hard to know what to charge.
Every time a company hits an order of magnitude change in number of customers, recurring revenues, or number of employees, something has to change.
Consider, for example, how much you earn a month. At $100 a month it’s a hobby; at $10M a month you’re probably a public company complying with regulators and industry norms:
Similarly, as you grow the number of employees in your organization, things have to change on hiring and HR fronts:
And if you’re a B2C company, things change as you get more customers:
The management team doesn’t necessarily need to change at each stage of growth, but the company will shift dramatically.
As Paul Graham points out, most startups are probably tech companies. And when you deploy emerging tech you’re running ahead of legislation. That means you may have built a house of cards atop, for example, a GDPR-violating business model. Or that your new biotech startup is about to run afoul of the FDA. How should companies account for, and mitigate, risk in these situations?
Technologies like differential privacy, distributed ledgers to fight counterfeiting, and homomorphic encryption can help here, but it’s a constant race between new tech and the resulting constraints on that technology.
The value of a startup is often about what might be — not just brand equity and IP, but understanding how much its users’ attention is worth, or the value of data it has collected. Whether pursuing an IPO or an acquisition, quantifying this information is critical.
Applying analytics and learning
In the earlier stages of a company, analytics are about exploration and experimentation (what Rumsfeld called unknown unknowns.) Bigger firms look at known unknowns — did we make this quarter’s sales? — but need to employ analytics and data science to identify threats and opportunities outside their usual scope of awareness.
ADT, for example, makes house alarms, but probably missed Amazon’s burgeoning home delivery business and Key product, which effectively reframed home security as “manage access to my private spaces.”
Launching the Scaletech discussion
This October, along with the team at Georgian Partners and other colleagues, I’ve invited a number of people from academia and industry to come to Toronto and speak about these topics. It’s a one-day series of talks and Q&A discussions for executives from scale-stage companies around the world.
We’re calling it the Scaletech Conference.
While the event is invite-only, we’ve reserved a few spots for qualified participants who want to attend. You can apply for one of those tickets on the event website.
What did I miss?
Are there other major challenges unique to scale-stage companies? How are the best organizations dealing with them?