When to hire the sales team

When are company forecasts reliable, and when is it time, based on those forecasts, to ramp up the sales force? Investors and entrepreneurs face these two critical questions, respectively, in every venture-backed company. The wrong answer will either blow a startup's precious capital, or miss the opportunity.

Always keep in mind the Sales Learning Curve (SLC). The SLC is the invention of Stanford Business Professor Mark Leslie, who also happened to have founded and led two companies, one of which is dead and forgotten, and other of which is Veritas! Prior to Leslie's formal description of the SLC, CEOs just stumbled along, making clumsy judgments about the quality of forecasts and a company's readiness to "ramp it up" based upon the patterns we could glean from our own anecdotal data.

Leslie describes the SLC in a paper (pending publication) titled something like (it's been a while since I've read it) "It Always Takes Longer and Costs More." This paper attempts to explain why it is that Silicon Valley, which is so good and experienced at inventing new technology and building new companies, can't ever seem to craft business plans that track reality.

Leslie's approach springs from the observation that factories would never presume to produce widgets in volume until they have brought the cost per widget down to a profitable level through a commonly recognized manufacturing learning curve. Only once you start manufacturing something can you identify hurdles and opportunities, ultimately learning how to reduce cost, improve quality, and raise yields.

Likewise, technology companies shouldn't expect to sell their 1.0 product without undergoing a learning curve around the needed feature set, the competitive response, the right type of channel and sales rep, optimal pricing, etc. And yet, nearly every business plan I have seen shifts gears directly from Development to Sales, staffed by a growing number of experienced sales reps charged to sell their regular quota of product.

The consequence of skipping the Sales Learning Curve in a business plan is all too common: the company fails to meet the plan, sales reps are fired, eventually the VP Sales is fired, and the company has to raise a highly dilutive down round of capital to stay in business.

Leslie's prescription for success and capital efficiency is a plan that includes the Sales Learning Curve. Accept that until we're out there selling, we can't know what we don't know about the selling process. The goal at this point is to maximize runway, since you can't rush science. As my erudite partner and Harvard Business School Professor Felda Hardymon likes to instruct, "run the business like a one story whorehouse" (with no fucking overhead). Hire only two or three creative, guerilla-style reps and a VP who knows how to experiment with multiple channels.

It may take 4, 7, or 10 quarters to climb the curve. Only then, when your sales force is a profit center (with two quarters where contribution exceeds twice the sales costs) is it time to flick the switch. At this point, raise lots of capital and hire as many talented reps as you can possibly find! This kind of prescriptive framework for clearly distinguishing the phases of a company is so much more actionable than the wishy-washy approach of hesitantly adding more and more sales reps over time.

The SLC is least helpful in markets like telecom carrier equipment, in which it's rather straight-forward to determine the needs and buying habits of the customer. That's why these companies tend to accrue a lot of value after winning just one or two large customers.

But for enterprise-focused startups, the SLC is critical. No amount of up-front due diligence can yield the clarity that comes from actual field sales. Enterprise companies are particularly vulnerable to the delusion that they have cracked the code, thanks to some early wins. But without a proven, profitable sales force, it is reckless to ramp up the sales and marketing expenses.

The extreme value of the SLC is seen in consumer markets. Focus groups or not, you just never know. That's why, try not to guess User Behavior Risk.

Further reading: Professor Leslie's slides on a case study of applying the SLC to the Nano-Optical Customer-Adaptive Software/Hardware (NOCASH) company.