The Oil and Water of Startups and MBAs


On the pain of joining a company before it’s ready

Ask any group of soon-to-have-MBAs what they plan to do after graduating, and you’re sure to hear the word “startup.” In my experience, MBAs often have a rosy view of startup life. Early-stage CEOs are often skeptical of whether MBAs can hack it, but many startups hire them nonetheless. The resulting mismatch can be painful for both.

In choosing to get another degree, MBAs demonstrate risk aversion by opting for a known risk-reward profile: invest the money and time, and reap the nonzero career boost that the degree affords.

By contrast, early-stage startup employees are unlikely to hold an MBA. Impatient and hungry for risk, most have never considered two years of additional study. (Some, as the legend goes, couldn’t even stomach an undergrad degree.) They opt for higher risk: commit almost all of your time, cut your salary, and reap something between total failure and huge success, the former being more likely.

When early-stage business leaders look at MBAs, they sometimes see grown-up approaches that could help solve some of their most difficult problems. When MBAs look at early-stage startups, they see an opportunity to apply structured approaches and bring clarity to chaos. Alas, both rarely want what the other actually has to offer.

Optimization

MBAs come with a bag of business tools. They have pivot tables, sensitivity analyses, and maybe even Myers Briggs profiles. They’re theoretically ready to solve any business problem. In practice, however, these tools generally equip them to optimize any business process, which is very different. After all, if established textbook methods of solving a business problem were sufficient, then there’d be a correlation between successful startups and founders with MBAs. There isn’t.

To wit, if a startup is solving a novel problem, then by definition its hairiest challenges haven’t been solved in the contemporary business environment. If traditional tools were sufficient to solving those problems, then it’s unlikely the solution will be a differentiating “secret sauce” for the company, since anyone could do it. The tools may be useful, but they are far from sufficient. Couple this with an over-reliance on the tools, and you’ll spin your wheels.

Breakthroughs occur when we allow the chaos to yield unpredictable business insights; forcing structure on that process prematurely is counterproductive.

Putting an MBA to Good Use

There is, of course, a point where the skills acquired through the MBA are useful. It gets back to optimization: once you’ve discovered a business model that works, you must vigorously optimize the system. A few examples:

  • You’ve figured out a direct sales model where you’re achieving a close rate of 10%. For every 100 prospective customers you connect with, 10 ultimately close.
  • You’ve found a way to increase your user base through existing users such that you’re acquiring 1.2 customers for every new customer you acquire. (That is, you’ve reached a viral coefficient.)
  • You’ve discovered that you can spend $3 per click to achieve $4 in revenue per click.

Now you’re cooking with gas. You need to manage your model and find economically sustainable ways to pour gas on the fire. You need to answer questions like the following:

  • What are your gross margins? Does the cost of customer acquisition leave any margin to cover some of your operating expenses?
  • What’s the lifetime value of a customer you’ve acquired? How much revenue will that new customer represent over the next 18 months, and how much will you spend to service that user?
  • What’s your churn rate? How many new customers disappear after a period of time?

These questions sound like the domain of mature businesses, but the faster you figure them out, the faster you can grow profitably. And MBAs are terrific at helping you solve these optimization problems.

If your questions are more like “does this product solve a real problem for people?”, then an intelligent 23-year-old, with more naiveté and fewer biases, is more likely than the MBA to find a solution.

Hire the MBA when it’s time to optimize your model, but not before. Focus on the pseudo-random search for traction and establish the model that sets your future MBA up for success. It’s only courteous; after all, she’s got a lot of debt to pay off.

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