How Founders Save Their Startups
Are you prepared to do whatever it takes to save your sinking business? And, more importantly, would you know where and how to start bailing the business out?
Every company — no matter how big or how small, how new or how old — eventually hits rough seas , a point at which the money coming in is nowhere near enough to cover the money going out. We saw this over and over again as the economic damage from the pandemic accelerated quickly and broadly, dragging all kinds of companies underwater.
But it doesn’t take a once-in-a-lifetime global quarantine to cause a negative revenue trend. It can happen to an individual company at any time, for many reasons: changing economic conditions; sudden market shifts; competitive pressure; loss of a key employee, partner, or supplier; even technological advancement.
The causes can be numerous, and the cures are all unique to the cause. So where do you begin?
Been There, Saved Some, Lost Others
I’ve been through a number of the broader economic downturns. Obviously, I’ve run startups through the most recent COVID-related crisis, but also the Great Recession and the dot-com bust. I’ve withstood minor disasters that impacted either my industry, my field, or my markets. I’ve lost key employees at the wrong moments.
Furthermore, I’ve talked to better and more experienced entrepreneurs who have managed their companies through multiple crises, succeeded in most cases, and failed in others.
There are multiple ways to dive in and save a startup (or any company) from certain economic disaster.
The obvious answer is go get more money. But even for well-funded startups with deep-pocketed backers, going back to original investors and getting them to pony up more money means giving away more equity, and at a less attractive valuation. I’ve seen founding teams negotiate themselves right out of ownership before they realized that the equity left over wasn’t worth the effort they were now required to put in.
The problem with raising “bail money” is that you end up playing from even further behind. New money creates a chicken-and-egg scenario — we don’t have enough customers to generate more money, so we need more money to go get more customers — and that makes it more difficult to stabilize a business, let alone grow it and prosper.
Outside of new money, almost every single method to save a business involves a series of experiments, and each one is somewhat a roll of the dice. But one common thread across all the solutions is that the more planning that goes into starting and managing each experiment, the better chance for a positive outcome.
How to Get Into Business-Saving Mode
There are a couple hard-and-fast rules that those better and more experienced leaders have taught me.
- Know when to start planning. It takes at least a few months for most experiments to start showing results. One of the things that saved Spiffy, the mobile, on-demand vehicle maintenance startup I’m helping scale, is that the CEO started making contingency plans months before the first lockdown. We had best-case, bad-case, and worst-case scenarios drawn up with plans to attack them, and those cases and plans evolved as the signs became more real.
- Don’t panic. Obviously. But when things go south, panic can cause one leader to overreach and do something silly and another leader to freeze and do nothing at all. Survival mode should always be seen as temporary, and you should be looking to get back to growth as soon as possible.
- Focus on the problem, not the damage. Remember that the downturn in the business is the symptom, not the cause. If the root cause of the downturn is an economic crisis, the solution should focus on riding it out. If the cause is a market shift or competitor, the solution is about the response. If the cause is the loss of a key piece of the business, the solution is to quickly replace that piece.
How to Execute a Business-Saving Experiment
This is when even the best leaders solicit outside and out-of-the-box thinking. And this is where the best leaders make the best decisions — which, incidentally, is what leaders get paid to do.
When the pandemic hit in full force and the lockdowns started, Spiffy started having daily meetings to plan our responses and actions, and at the end of every meeting, we went around the room to solicit crazy ideas. There were smart people in the room, and no idea was out of bounds.
From these ideas, we picked the best of them and reframed them into a plausible reality from which we could run one or a series of experiments.
Think about these experiments as a way to transform your company, and put everything about the company on the review table. In other words, everything the company does should be seen as an experiment, and what you stop doing, even if just temporarily, is as important as what you start doing.
Speed is always a factor because when the economics go negative, the clock is ticking. Revenue is the primary marker that determines whether or not the experiment is a winner. Not profit. The key is to increase the money coming in, and how much goes out is still a critical factor, but not the primary factor. Get to revenue quickly, worry about process and viability later. In this, it’s kind of like going back to the early stages. Prove viability in the new reality.
To prove that viability, there are buckets experiments will fall into. The decision the leader needs to make is whether or not the experiment will stay in that bucket, make progress into a better bucket, or slide back into a worse bucket.
The 5 Buckets Experiments Fall Into When Proving Viability
- The loser bucket.
- The promise bucket.
- The learning bucket.
- The spark bucket.
- The pivot bucket.
The Loser Bucket
The idea is a loser, meaning it won’t ever produce revenue worth the effort. This is usually because of some physical or procedural hurdle that can’t be overcome without spending a lot of time or money making enough workarounds to get to revenue. In any case, you can spot a loser when either the customer adoption isn’t there or it becomes obvious that the solution is too costly to make it repeatable.
The Promise Bucket
The experiment has promise, but not right now. In other words, the idea itself might work, but it will take a lot of extra testing and more outreach to prove that it’s economically viable at scale. These are difficult decisions to make and hard experiments to stop, but they can always go on a back burner.
The Learning Bucket
The experiment was a learning experience, and there are some green shoots that may call for reinventing the idea and trying it again, quickly. Just as hard as it is to let go of an experiment with promise, it’s easy to fall into a trap of learning the same thing over and over. You can try an experiment a dozen different ways and get the same result, so it’s critical that the learning experience is more learning and less experience.
The Spark Bucket
Whether the experiment worked or not, there was a component to the idea that can be stripped out and reused in a completely different manner. The results here, in terms of revenue, should be promising, and the component should be a building block.
I’ll give a broad example here. If a coffee shop tries selling iced coffee in the summer, they might learn that customers want an iced drink but not iced coffee. The experiment might fail, but the spark is the ice.
The Pivot Bucket
This is the most positive outcome an experiment can have, and almost always an outcome that will require the company to pivot to take full advantage of those positive results. Then it’s further decision time: How much of your existing business gets put on the back burner, and how much of a commitment do you make to this new experiment?
On a final note, it shouldn’t take a negative change in business to conduct experiments and put them in these buckets. A lot of this is pretty key to any early startup finding its product-market fit and scaling.
It’s my belief that all companies should be experimenting all the time, and the ones that do should grow faster and for longer than the ones that don’t. It also helps to establish a culture of experimentation within the company before its needed, so that when it is needed, there’s already an infrastructure in place.