Lessons Learned from a Startup Setup to Fail A few months ago, I read - What I Learned Losing A Million Dollars - by Jim Paul and Brendan Moynihan. In the book, Brendan recounts how Jim, from humble beginnings, rose to become a retail broker in the futures industry, achieving early successes that led him to serve on the board of the Chicago Mercantile Exchange at a very young age. And how he lost it all in the span of a few months. The book is a cautionary tale for everyone involved in high-risk ventures. Over the past year, I have observed the patterns described in the book. It was not in the futures market but in the Startup world. It is amazing how these patterns of loss are common to any loss. In this article, I try to summarise what I saw and draw parallels to Jim Paul’s process. The context (How did I end up here?) During 2022-2023, I was enrolled in a Data Science Master’s degree. There, I met my friend JD. During the second semester, he started working (remotely) for this exciting young startup in Australia. They were trying to solve Marketing Analytics at scale for Shopify store owners. They wanted to grow fast and were looking for a Data Scientist to join them. Naturally, my friend told some of us to join them in the venture. As I was out of a job, it seemed the perfect opportunity to try something new. More risky than what I was used to, but with higher potential rewards. The Yellow Flag The first thing that felt strange, was the fact that two of the founding members (the technical team that built the platform) were on their way out as JD and I joined. The story behind this was that the initial product (a data pipeline and a dashboard) was already built. And now it was time to add some intelligence to the product through ML algorithms, which was not the strong suit of the two guys who built the initial product. The argument felt reasonable, but there was a lot to uncover. The Red Flag: The problem statement The red flag, was the problem statement. In theory, our mission was to make businesses more intelligent. That felt like a good challenge to tackle, but it did not answer the simple question - What problem are we solving for the customer? - Making a business more intelligent is an inspiring vision… but what is the urgent need that we are meeting for the customer? Forget “urgent”, what is the need we are covering? During the first couple of months, the only founder who was left (the CEO) wanted to ramp up sales. In his mind, we had 60% of the value the customer wanted, and the platform, as it was, was ready to attract an initial customer base. He hired a director of sales and a customer success person… he projected to have at least 10 conversions a month for the next 6 months. But as it turns out, conversions were hard. We were selling “intelligent recommendations”, and our potential customers were not looking for that. The few who bought into it and joined were disappointed to see that the platform did not meet their expectation of “intelligent recommendations” and ended up churning. In the first 3 months, we converted fewer than 10 customers on free trials. Half of them or so converted to paying customers (quite short of the projected 30), and the few that stayed did so because of the promise that we were building something that would eventually meet their needs. How did this happen? Two of them had technical backgrounds in consulting. The CEO, also consulting background. None of them were ever in the e-commerce industry. So the entire thing started with a top-down approach. The CEO had the desire to start a startup in the tech industry (without having any previous experience in the tech industry).
He had some experience with data from his consultancy years, so he naturally drifted to what he thought was his forte.
He analyzed the market and saw what looked like a potential opportunity - “Every day, there are XXX number of Shopify stores being created…“.
He applied what he did during his consulting years to this new potential market with a twist. This time, it was going to be a SaaS and leverage technology to be scalable.
He gathered a team, etc. Nowhere in the process is the potential customer involved. There was no problem to be solved. And there was no real validation of the idea. At the time I joined, the team had spent more than a year and a lot of money building a product that was not validated, other than some people saying “It is a great idea”. This startup was set up to fail. The CEO had the desire to start a startup in the tech industry (without having any previous experience in the tech industry). He had some experience with data from his consultancy years, so he naturally drifted to what he thought was his forte. He analyzed the market and saw what looked like a potential opportunity - “Every day, there are XXX number of Shopify stores being created…“. He applied what he did during his consulting years to this new potential market with a twist. This time, it was going to be a SaaS and leverage technology to be scalable. He gathered a team, etc. Nowhere in the process is the potential customer involved. There was no problem to be solved. And there was no real validation of the idea. At the time I joined, the team had spent more than a year and a lot of money building a product that was not validated, other than some people saying “It is a great idea”. This startup was set up to fail. Yet another red flag: Outsourcing your value proposition As mentioned above, the CEO believed he was 60% of the way there on his value proposition. When we joined, the technical part of the founding team was on its way out. Moreover, the platform was built by an outsourced team in India. This meant that any change to the front end had to be requested to an external team. Internally, we had no control to iterate fast enough based on customer feedback. We focused on solving this problem first and spent nearly 3 months rebuilding the front end in a way that we had full control over it. This meant that we could quickly accommodate our front end to our customers’ needs. But here’s the problem: Without a well-stated problem to solve, it meant that we would be making several changes to accommodate the needs of each customer, which soon would make the front end a mess and would further distract us from solving a problem at scale. Too little, too late, and the inevitable end By the time we finished the front end and were struggling to meet the demands of every customer, we realised the real underlying problem. We were not solving any specific problem for any customer, we were not scratching any itch. There was nothing to scale because there was no problem being solved. This happened 2 years into the journey for our CEO, 2 years, and a lot of money was wasted. Upon this realisation, we decided to find a problem, validate it, solve it, and scale it. We settled on Marketing Mix Modelling (MMM). We talked about the idea with one of our customers, and he was all in. We talked to a competitor doing the same thing, and he seemed to be doing well. It looked like the problem was validated and worth solving. The issue? We had no experience in Marketing, we had the data skills, but did not possess any insight that would differentiate us or solve the problem at scale. We had a tight deadline before we ran out of money, so we had to iterate through our solutions in record time. After a couple of months, it was clear that the problem was worth solving, but it required much more than three Data Scientists with no experience and a CEO focused purely on sales. The parallels between Jim Paul and our CEO The first parallel is that both of them had early success in their careers. This led them to think that they could tackle anything, and that success was guaranteed because “it is them”. Which in turn led them to dive headfirst into a risky venture.
After an initial loss, they both fell into denial, refusing to accept the loss and instead doubling down on their positions.
They both went through the bargaining phase of the different stages of loss(Denial, Anger, Bargaining, Depression, and Acceptance). “If I convert this customer, I can win..”, “If this event happens, I will have been right all along…“.
They both go in a loop through a subset of the stages without ever getting to the acceptance phase. The first parallel is that both of them had early success in their careers. This led them to think that they could tackle anything, and that success was guaranteed because “it is them”. Which in turn led them to dive headfirst into a risky venture. After an initial loss, they both fell into denial, refusing to accept the loss and instead doubling down on their positions. They both went through the bargaining phase of the different stages of loss(Denial, Anger, Bargaining, Depression, and Acceptance). “If I convert this customer, I can win..”, “If this event happens, I will have been right all along…“. They both go in a loop through a subset of the stages without ever getting to the acceptance phase. Lessons Learned and Regrets Lessons learned The world is not about you. Wanna start a startup? Begin by noticing a problem someone has and providing a solution. Don’t start at “I want to…” start at “They need to…“.
Validate the need/idea before you start. Ideally, get your first customer before you even begin.
In any investment/bet, set a stop loss condition. Better, create a system that won’t allow you past that stop loss amount. Say, I will invest $1000 in validating this idea. If I get at least X customers, I will continue; if not, I will give up, or at least, I won’t invest anything else.
Product >>>>>> Sales. The world is not about you. Wanna start a startup? Begin by noticing a problem someone has and providing a solution. Don’t start at “I want to…” start at “They need to…“. Validate the need/idea before you start. Ideally, get your first customer before you even begin. In any investment/bet, set a stop loss condition. Better, create a system that won’t allow you past that stop loss amount. Say, I will invest $1000 in validating this idea. If I get at least X customers, I will continue; if not, I will give up, or at least, I won’t invest anything else. Product >>>>>> Sales. Regrets None. It was the most relevant professional experience of my life. None. It was the most relevant professional experience of my life.

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