A competitive landscape is one way of mapping out the landscape in which we are going to operate. We need to understand which other players are around, how they phrase their solutions, and whether people like them or not.

One of the advantages for scipreneurs is that by scanning papers we can very quickly identify which tools are more popular. If we are lucky, there may even be a review discussing the pros and cons of each one.

However, even with that amount of information, nothing competes with actually talking to people.

Papers suffer from large survivorship biases and tend to make very positive claims that may not materialize in real scenarios.

Competition Desperation

I have talked to many scipreneurs who get very quickly discouraged when they find a paper, a patent, or, even worse, a website, of someone making claims about the same type of results they were pursuing. Not falling into the desperation trap of studying the competition is hard to master.

I still remember when I started exploring alternatives to our product and came across a paper providing an awesome solution. Easy to use, better resolution, better publication track record. I really thought we had not a chance, what was the point of pushing forward. And now, even 6 years later, to this date, that technique is not available. No one except the authors has used it.

And the reason I didn't relent back then was thanks to one of our earlier adopters. Regardless of the claims on a paper, there are years of distance between a tool that works today and one that is just in a methods paper.

One out of many solutions

Regardless of how unique we think our solution may be, there is always a way of solving the problem we identified as valuable. The only way we can craft a compelling story is to fully understand how people are currently working around the challenges they have, and what are some underserved tasks.

Scientific instrumentation almost always falls into this pattern: we can do something with higher resolution, we can extend the range of objects we can observe, or we can quantify a new parameter of the sample.

Although we wish to believe that improvement in one dimension will be enough to convince someone, the road ahead is still long.

When I started Dispertech, the premise was that we could measure the size of nanoparticles with nanometer accuracy. That's an impressive feature for an optical method like the one we had, but not impressive when compared to an electron microscope. Our original strategy focused on price: we were much cheaper considering the quality of the results.

However, it was only when I started talking to customers that I could fully understand the nuances involved in measuring the sizes of small objects.

After dozens of interviews with researchers, some of which were key opinion leaders, some were decision-makers, and some were the real users of the instrument, it was clear that resolution in itself didn't add value to them.

The issue they were struggling with was the identification of small particles.

They needed to know they could properly quantify particles of different size ranges, without losing the small ones. They also needed information about the composition of the particles they were observing. Those were the open challenges. They were open mostly because the other solutions were always lacking crucial features: protocols were not available, devices would clog with relevant samples, and results were not reproducible even with the same sample measuring twice in a row.

Took many interactions with people, but it became clear that by pressing in specific dimensions we would get much closer to the solution people wanted.

During this process, which took years, I learned a very valuable lesson: when I first built a competitive landscape, I did it from my own perspective, by the metrics I knew would give better results for us. However, competitive landscapes only make sense from the perspective of users. And the only way to build them is by talking to them.

I also learned that competitive landscapes shift quickly: suddenly, a key opinion leader shows how to use an old tool for a new purpose and everyone is rushing behind.