That time I helped euthanise a startup
Energy gradients, big concrete wheels, and suppliers in the mist
Nobody wakes up in the morning excited about euthanising a startup. I’ve personally been in startups that were struggling, worked with startups that failed, and been cut loose when the writing landed on the wall. I wouldn’t wish it on anyone.
I prefer working with companies much earlier, when there’s still time for us to adapt and find success. But problems grow to the size needed for you to acknowledge them. Sometimes that size is too big to be able to solve it, and sometimes the acknowledgement comes too late to have time to solve it. In such cases, all you can really hope for is a quick and painless end.
So I wanted to write this piece today to help you avoid such situations.
There’s a lens I keep coming back to that reliably delivers strategic insight, clarity and decisive action: mapping the energy gradients at play. I’m going to share how I think about energy gradients, and how I applied this lens with a struggling startup.
Before we jump in, some housekeeping:
Did you know that
and I have now shipped 45 podcast episodes?! Number 42 is kind of a draft of today’s article. Do listen and let us know what you make of it. Search for Trigger Strategy Podcast in your favourite podcast tool, or find all the episodes here.Can you help me out with a referral? I’ve got room for new clients this summer. I’d love to speak with founders or internal innovation leaders who are working from first customer to first £1m. They followed best practices but their product isn’t resonating in the market like they know it should. They’re wondering what it’ll take to get it there. I help them out of paralysis and into effective action.
Speaking of such clients, let’s look at one now to help explain energy gradients.
One balmy summer day, I got a DM from a former colleague. Summarising:
Hi Tom. Our sales are struggling. We’ve tried all the usual stuff but we still can’t seem to get product-market fit. Can you help us figure out what our customers need our product to be? And whether we can even deliver that based on what we’ve built so far?
YES. This kind of product-market fit puzzle is right in my wheelhouse.
You probably have your own hunch about what might be going on behind this exec’s question. That’s where I started too.
First, let’s get the usual failure modes out of the way
Hunch 1: maybe the team didn’t have the chops
No, I was impressed by everyone I met from leadership, product and sales support. Smart, hardworking, data-driven folks following common industry best practices.
Hunch 2: no demand in the market
Always a good bet as this is the standard failure mode for startups: build something clever and cool but not enough people want it. But that wasn’t the case. Scary new regulations created a problem that big brands were throwing money at. Sadly, my client’s competitors were busily hoovering up all the money.
Hunch 3: undifferentiated or poorly positioned solution
Again, no. The idea was clear, smart and differentiated. The UI looked professional and polished, and the positioning was solid. They were booking plenty of sales calls, but something was spooking the customers.
So what the heck was the issue?
As I hinted earlier, to work out what was going on, I started mapping energy gradients.
What do I mean by energy gradients?
In your mind’s eye, picture a rolling landscape with plateaus, slopes, hills, valleys and cliffs. Next, add some people to the landscape – customers, employees, colleagues and suppliers. Then give each person a ruddy great concrete wheel to push around.
The pushing of those concrete wheels is a visual metaphor for the behaviours you need your customers, employees, colleagues and suppliers to carry out.
Your success always depends on the behaviours of people and systems that are outside your control. It’s vital that you figure out what those crucial behaviours are, and that you figure out how to get them happening. (This is the main principle behind my Innovation Tactics card deck.)
For example, your success often depends on prospective customers going to a landing page and making an online payment. In the metaphor, this is like you’re asking each of them to push a concrete wheel up a slope. When they get the concrete wheel to the top, they’ve got what they were promised and you’ve got your money. Yay!
But all slopes are not alike. The steeper, the longer or the bumpier the slope, the more energy that prospective customer has to use to get to the top. If they’re not quite keen enough to slog up to the crest of your hill, they’ll simply wander off, leaving the concrete wheel right where it is.
Another example. After a customer’s paid, you’ll need colleagues or suppliers to do things to serve that customer. That means more concrete wheels and more slopes. Colleagues and suppliers are usually prepared to put in a fair bit of graft hefting those wheels over the terrain. That’s their job.
But there’s a limit to how much energy anyone can expend on a given day. If you find yourself frustrated by colleagues not doing what you think is needed, consider what kind of hills they’re dealing with, and how many trips they have to make. If the route they’re supposed to take leads them over punishing terrain, don’t be surprised when people start to cheat. Perhaps they push the wheel on flat ground around the hill instead. Perhaps they choose to move some wheels, but “forget” the heavier ones.
So that’s what I think about when I think about energy gradients. Imaginary people pushing imaginary concrete wheels around an imaginary landscape. Quite daft. But I bet you’re already thinking of real life examples.
With that, let’s get back to my real life client and their energy gradients.
Can you push a concrete wheel up a sheer cliff?
When I started talking in depth with the client, I found their reality was worse than I’d initially thought. The company had one customer. And that customer wasn’t paying or using the product yet. And the founders had been working on this business for 4 years. Yes, that’s four years, and not even one paying customer.
This had become a do or die moment.
Working together with the team, I uncovered lots of different energy gradients.
In practice, this meant making a huge board full of different kinds of map and model. I captured narratives and data points from everyone I spoke with, collected clues, and annotated screenshots.
The map above might look a bit chaotic, but it’s all organised around one question: which steep energy gradients were making success too improbable for my client?
Product-market fit is especially challenging because it depends on getting many energy gradients working at the same time. They don’t need to be optimised, but every critical gradient needs to be smooth enough that someone can get up it.
And product-market fit is especially painful because you can’t know about all the energy gradients up front, and you see very little feedback or progress until one day you happen to get them all working. It’s nothing, nothing, nothing, oh it’s working now? There’s no single cause – it’s the combined effect of all the conditions working together in strange ways.
This makes it all too easy to invest years (four years, even) fixing all the energy gradients you can. But if you still need someone to push a concrete wheel up an impossibly steep or tediously long hill, you’ll be stuck at zero. And confused.
As I dug deeper using the messy map above, one pesky hill kept looming out of the mist. It wasn’t to do with my client’s customers or internal teams. They had lovely smooth hills to amble up. That distant pesky hill was to do with their customers’ suppliers. Because it turned out that for the customers to be successful, their suppliers would have to do some work. And their suppliers’ suppliers, and their suppliers too, several levels deep.
At first this hill looked quite inoffensive. Apparently the supplier didn’t need to do that much work. Just respond to an email or two, which they needed to do already.
But as we looked closer, the mist cleared and that pesky hill turned out to be a sheer, 100 metre cliff face.
In one session with the product manager, I mapped out what actions we actually needed all the suppliers to do, with some back-of-the-napkin estimates about how much effort that would take. It was more than responding to some emails. A lot more.
It turned out that each supplier would need to either invest in an expensive technical integration, or hire an extra employee to manage the work created by this system. And the incentive for the suppliers to do any of this was basically zero.
The devastating blow was that my client’s very differentiation, the differentiation that made their solution so much better than the competition, quietly depended on getting all the suppliers to take these actions.
Their competitors offered a worse solution, but their energy gradients weren’t functionally impossible.
I suspect that customers were turning away in subconscious horror about what harm using this product could do to their supplier relationships. As far as I know, nobody told my client this outright, but the one customer had been dragging their heels on messaging any suppliers.
I suspect my client also had a buried fear of this supplier issue. Not seeing an easy solution, it had become a can they kept kicking down the road, hoping they could find a way to resolve it later, after they had some customers.
This created a “you can’t get there from here” situation. In the far future, when the whole market was using my client’s product, there would be powerful incentives for suppliers to use it, and the cost would be much lower. But on day one, the first supplier to get involved would incur nonsensical levels of cost. I suspect any reasonable supplier would choose an easier route for their concrete wheel. The obvious one would be to simply end their relationship with my client’s customers. After all, they’d still get to keep supplying the other 98% of their customers: companies who didn’t ask them to shove wheels up cliffs.
I’ve seen this trip up many a visionary founder. They can see just how wonderful it will be in the future that they can envision, but the energy gradients for the first customers today are so steep and unforgiving that there’s no practical way to get started. (Marketplaces and social networks are plagued by this, because there’s almost no value until after everyone’s on them.) You resolve this bind by finding or creating ways to make the energy gradients work for customer number 1.
In the end, they had to face their fear of the cliff
And that leads me to the conclusion I shared with the client. They had 3 options:
Focus the whole team on a big experimental bet: could they automate that sheer cliff so the suppliers wouldn’t have to do the lifting? Like everyone else in the world, they’d often discussed using AI to handle the work, but that would be in the future. Bringing that effort forward would be risky and uncertain, but a massive win if they could pull it off. There were other approaches they could try besides an LLM-powered Hail Mary, but all were high-risk.
Hire a team of people to do the work for the suppliers. Perhaps suppliers would accept it if my client offered to do the needed integration work for free? Or perhaps my client could staff a team who would manually input information for the suppliers? (We think some of the competitors were already doing this). This would be intended as a temporary measure until the energy gradients made more sense for the suppliers to take on the work. But realistically this state of affairs could go on indefinitely, at significant cost.
Accept defeat and call it a day. The startup in question had been acquired by a multinational, and was part of their big portfolio of bets. You’ve got to know when to fold ‘em.
I put a spoiler in the title of this piece, so you already know what the client decided.
In the end, having fully explored the possibilities, and knowing that everyone wanted to avoid the worst case scenario of shutting down the startup, they ultimately decided that the level of risk involved in turning this around was beyond their tolerance. My heart went out in sympathy to the team that had put their heart and soul into this venture, but I suspect that this relatively clean and clear ending was kinder than dragging things out in confusion for another year.
I hope that mapping energy gradients helps you to avoid similar worst case scenarios.
Until next time,
Tom x
P.S. Did you know I’m talking and running workshops alongside a roster of amazing speakers at UX London, 18th-20th June? Hit reply and ask me if you’d like a code that gets you 20% off the ticket price.
Thanks to
for more editing awesomeness.