Scaling, today

The term “scaling” is used frequently in the software industry. Have you heard it this week? Maybe today? The Google search “how to scale my business" returns an astounding 1.08 billion results. But with such heightened use comes confusion and diluted meaning.

Scaling, one might argue, has descended into that lowly pot of platitudes along with “good” and “awesome!” Today scaling seems to ambiguously mean: “all things growth.”

In the current tech market cycle—which has indeed valued growth over profitability—perhaps we’d be wise to investigate the definitive use of “scaling” so we can:

  1. Increase internal alignment across teams

  2. Understand the nuance of “scaling” as it applies to execution (and our success)

What scaling means to tech leaders

We asked a handful of software executives & founders to define scaling in their own words:

  • “Growing the infrastructure and processes that support growth.”

  • “Tactically, it's mostly hiring. More abstractly it’s upgrading your organization’s firmware in order to support another burst of 10x growth, i.e. processes, team structures, key managers, executives.”

  • “Put the pedal to the medal.”

  • “Based on X inputs, you having a working model to deliver Y outputs. Scaling is all about tweaking and tuning that working model to exponentially grow Y outputs as X grows linearly.”

  • “Scaling = growing + evolving + maturing the people + processes + organization of your company in service of predictably growing revenue.”

  • “To me it means taking a bunch of manual processes that are necessary at the beginning of a startup and automating them. This usually is a forcing function due to excessive sales interest.”

Even in this small sampling we see disparate domains: infrastructure, hiring, process, team structure, automation. Therefore, let’s start from first principles and attempt to build a common vocabulary—and questions—to (hopefully) improve our collective ability to communicate and execute toward scale.

The “official” definition of scaling

To the extent we believe wikipedia, scalability is defined as: the capability of a system, network or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth. Therefore, scalability can refer to a capability or a potentiality. The latter meaning introduces execution which is required to realize said potentiality.

Clarifying Question #1:

Are we talking about current capacity or future capacity?

For example, you’ve built an app that has attracted 10,000 monthly users. Your app could be said to be scalable if 1) your app can safely handle 100,000 monthly users right now, or 2) it has the potential to be enlarged to accommodate this growth by adding stuff*.

*Adding stuff; types of scaling

  • People: hiring and enhancing the human capital in your business

  • Process: revamping/expanding existing processes to handle accelerated output

  • Technology: increasing the capacity of a distributed system (also called load scalability), e.g. servers, etc.

Clarifying Question #2:

Are we talking about scaling our team, a process, or technology?

The nuance of scaling

The most overlooked nuance of “scaling,” it seems, is the financial part: the ability to accept increased volume without impacting the contribution margin (revenue − variable costs).

In this way, scaling resembles fractal geometry: rapid growth while replicating the economic integrity of the base shape.

Sierpinski Triangle

From this perspective, scaling does not mean “growth at costs” or “put the pedal to the medal” [at the expense of fuel efficiency]. This nuance is critically important for successful scaling and commands a deeper understanding of unit economics, a fun topic we’ll save for another article (primer).

Nevertheless, the point is: scaling must be done efficiently with regard to contribution margin, i.e. your variable costs should increase at a rate equal to—or less than—your revenue growth.

Why SaaS is unique

However, technology/SaaS business models are unique: they have HUGE gross margins, typically 70-80%. This is because the “Cost to Serve” is highly efficient: I can serve another 1,000 customers and receive another $5 million in annual revenue simply by adding a few AWS servers (which you can prepay for to reduce the cost even more).

Therefore, the most significant variable cost is almost always people, e.g. a CSM or Professional Services team, which you can ideally manage to 9-11% of revenue.

The importance of forecasting

Assuming contribution margins and unit economics are in check, the biggest challenge becomes successfully scaling people, process and technology to meet demand. It is here we encounter what Scott Belsky calls The Messy Middle in his fantastic book by the same name.

Part of navigating a messy, unknown future is getting good at forecasting. Failure to do so often results in painful realities:

  • Mid-Market Account Executives with no leads in their queue (Marketing over-forecasting lead gen)

  • Stressed out CSMs and support reps working at 10pm (GTM under-forecasting new business or over-forecasting CSM requisite bandwidth)

  • Product performance issues (ENG under-forecasting cyclical product usage and requisite capacity)

  • Laying off 20% of your workforce (C-team over-forecasting demand and/or headcount needed)

This is your captain speaking. . .

Forecasting is like a flight plan. Before a flight, pilots use data and tools (wishlist item: G1000) to approximate the flight path and anticipate changes. Attempting to scale without forecasting is like taking off without a flight plan—incredibly risky.

The best pilots also regularly monitor reality vs. plan. To low? Climb. Too high? Descend. Off-course? Course correct. Similarly, the best companies don’t just create a forecast then put it on the shelf. They use it! Monthly, if not weekly.

  • How are we tracking on leads vs. AE hiring?

  • Where should we adjust CSM capacity?

  • What happened to usage last holiday season?

  • What are the leading indicators of being over-hired?

As you plan your own flight, we hope your understanding of “scaling” is a bit deeper and more nuanced, and your forecasting habits take you to unprecedented heights.