Startups provide both the luxury and the risk of having very little data on which to base the important early decisions. A business model must be created out of whole cloth, and generally it’s a compilation of assumptions piled on top of other assumptions. Every model has a happy outcome when a founder is trying to convince himself or herself, a team, an investor, or even a spouse that the venture ahead is a good idea.
How and why to build this early model are important decisions. The easiest, but the least persuasive, is working from the top down. There are about 32 million left-handers in the US, you have a special product for them, and surely you can get just 5% of that market and have 1.6 million customers. The converse of calculating exactly how you will make the first sale, and then the first 10,000, and ultimately work your way up to 1.6 million is a far more complex task. But, it has the benefit of giving a better answer to the question of why. The goal of model building is to prove that if you take all the baby steps in the right order, and you test out pricing and cost assumptions as best you can along the way, and you keep listening to potential customers as you progress, then you develop reasonable confidence that success is mathematically possible. Further, you demonstrate on your spreadsheet that a venture return to early investors is plausible at a valuation that gives you enough equity to get the job done and to take care of you and your team at exit time. This early model creation is a sanity check on your idea. If it doesn’t reach the conclusions you want, don’t decide to contort it to fit your dream. Heed the math and change your plans until you find something that works in theory. For sure it will be much harder to make it work in practice anyway, but I’ve long ago lost count of the number of plans I’ve seen attempted where the numbers just didn’t stand up to scrutiny from the start. None of them had happy results.
Very, very early in my career a friend and I looked at a small manufacturing business where we liked the product and the owner and thought we might want to participate with her. We took an advisor named Howard out there. He visited for a while, then walked us outside and said: “This is an idea. Fire the lady and get someone to make it a business.” His language was actually a bit more colorful than that, but he made his point and helped us dodge a bullet. That was before spreadsheets existed other than in the form of pencil on accounting paper, but Howard had enough practice to reach the right conclusions in his head. He was Mr. Math when it came to business decisions.
Once you’ve passed this early theoretically proof of concept, have launched, and have begun to grow, there come successive stages where your analysis needs to be revisited. You’ll want to get cozy with the numbers all over again based on some history and make decisions on operating spend and on financing strategies. You want your valuation to keep marching upward; if you took in some seed money with the promised 10X venture return, you’ll hopefully be able to prove that you are proceeding on that trajectory. This is a point where your really must be honest with yourself. The numbers all need to be normalized, for example. If the founders aren’t drawing market salaries, then true profitability may be overstated. You also need to look at your operating history to date and get comfortable that your machine is humming along as planned. If you couldn’t deliver on some of your basic assumptions in year one, then what’s to say year two will have better results? Maybe a desired distribution channel was more costly or less productive than expected. Perhaps a better angle developed to take its place. If something outside the center of your vision worked better than expected, pay attention. I’ve many times seen companies where the founders are so wedded to an original idea that they can’t let go of it in favor of riding an obvious winning strategy that has literally fallen into their laps. They allow their instincts and perseverance to overrule the slide rule when it comes time to decide what to do next. They need a Howard to keep them in line.
A warning is appropriate at this juncture. Don’t get fooled by one-time lucky deals that bump up your results. It’s too easy to extrapolate from “blue birds” that might or might not appear again next quarter. Pure math is no substitute for business insight that must always be part of your decision making. If you know your customers, you know what’s repeatable and what may not be. Plan according to your baseline trend and don’t get carried away by sporadic deviations.
When you’re attempting to create value for shareholders, it’s necessary to analyze what’s going on around you as well. Have you positioned yourself in a market niche where you are seeing high value exits, or have you fallen into a comfortable business model that puts you in the not-so-cool club where multiples are much lower? Obviously your first task is to create customers and take care of them with whatever you have brought to market. But, the manner in which you get paid can swing your valuation considerably. Certain pricing models go in and out of vogue, but nothing beats old-fashioned recurring revenue where the customer gets value and is quite literally locked in for the forecastable future. Also, it’s hard to top being in an industry category that has a lot of sizzle. You may have the world’s greatest solution for a newspaper problem, but you are going to have a hard time convincing anyone that you’re in a fast rising market. Facebook and Google are leading the charge of devouring what used to be an elite industry, and they’ve even dramatically altered the distinguished profession of journalism along with the entire world of advertising. Gutenberg was too early to make the big bucks on newsprint, and for sure anything from here on out is too late. The point of this paragraph is that not only your internal metrics count, but how your peers value what you do has much to do with your eventual outcome. Combine your internal numbers with legitimate market data and decide to keep yourself positioned where the all the charts are moving up and to the right.
If you are prone to self-delusion, be sure to keep a boring and cold-hearted financial person at hand. Your management team can all look at the same set of numbers, reach different conclusions, and make opposing decisions about what’s next for the company. The results that we all look at most closely are the numbers that directly affect our pocketbooks today. What’s good for one may not be so good for another, for example, depending on how comp plans are structured, budgets are allocated, and who owns which ideas. I remember that the controller at MSA was always awarded a trophy made from an automobile brake at annual sales gatherings. He was the one who had to say no when creativity exceeded the prescribed boundaries. I was similarly accused of constantly saying no in one of my earlier advisory roles where I was the one keeping the vision in check in favor of the math. I’m sure it was depressing for the founder for me to keep doing that, but I was right. If anything, I should have said no more often. That was a prime example of a tendency to make major decisions without modeling them first and trusting the calculations.
All that said, startups though all their stages are still people businesses, and humans will do things that defy the logic dictated by models and sometimes get lucky. Visionary leadership under pressure can make the impossible happen now and then. There’s just a fine line between vision and delusion that separates the good decisions from the ones that look really ugly afterward.