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The Four Types of Startup Opportunities

May 17, 2022
Written By
May 17, 2022
The 4 Types of Opportunities
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In our last post, we discussed how data is the new general-purpose technology and that is why at super{set} we form data-driven companies from scratch. But new technologies are a promise, not a sudden phase change.

In our last post, we discussed how data is the new general-purpose technology and that is why at super{set} we form data-driven companies from scratch. But new technologies are a promise, not a sudden phase change. Take electrification. When the first electric motors came out, factory owners took the easy path of simply swapping them in for the old steam engines. The factories still had the line shaft systems that worked like the leather belt-driven ceiling fans you might see at a gentrified hipster bar. This was a good system for transferring the steam power from a heavy centralized boiler, but also wasted a lot of energy running all the machines at once from a single shaft.


Source: industrialscenery.blogspot.com/2016/11/jack-shaft-and-leather-belts-revisited.html

Over time, smaller electric motors came onto the market, which meant more efficient uses of energy but also the opportunity to swap in smaller and different types of machines on the assembly line. Industries were designed more efficiently around electrification, unleashing a wave of productivity in manufacturing. The lesson is that innovation takes time to deploy.

The venture ecosystem plays a big part in designing a new world that maximizes the productive value of general-purpose technology. We are still in the early stages of transitioning to a world where every aspect of the enterprise is reimagined around the value-add of data — just as factories in 1900 were reimagined around electrification. Data-driven company opportunities litter the ground, and we look everywhere — across industries, across contexts, across users — to find data-driven secrets we can take advantage of.

In the data ecosystem, there are sources of data and uses for data, and opportunities all along the chain to create value as a tech company. Data-driven companies derive their value to the customer from this journey from source to use — from generating, capturing, orchestrating, pipelining, analyzing, or activating data.

The data value chain from source to use

At super{set}, this is where we begin. Across verticals, we ask ourselves:

  • What kind of data exists?
  • How is the data generated?
  • Where is data accessed?
  • When is data generated and accessed?
  • Who benefits from the existing data?
  • Why is this of value to customers and/or users?

Just by asking simple questions through a common framework, we can arrive at a surface-level understanding of an industry, its problems, and where there might be a data-driven solution to those problems. We look at so many of these opportunities, applying our pattern-matching skills honed from years of experience, that we can tell from just a small amount of signal if there is an obvious solution and the kernel of a data-driven company.

Not every solution that is obvious at first will pan out into a successful company. Likewise, not every obscure solution is a dud — some may just take a little longer to nail down the right angle of opportunity. We think of new startup ideas as falling into one of four categories:


The four types of startup opportunities

  1. Mac-and-Cheese — obvious (to us) and attractive, these are our layups — we eat mac-and-cheese all day! If only everything were mac-and-cheese.
  2. Darlings — obvious but unappealing once you scratch the surface. Writers say you should “murder your darlings” — we do the same thing purposefully with darling ideas- there’s no need to be cute or possessive when it comes to bad ideas.
  3. Frogs — both obscure and unappealing, sometimes a frog really is a frog and not a prince, but you still have to kiss it to find out. We are deliberate about consensually smooching our amphibian friends.
  4. Soufflé — obscure but ultimately attractive solutions when you dig into them — soufflé is harder to put together but ultimately just as delicious as mac-and-cheese.

Our approach applies to any entrepreneur: sometimes, the first good idea is a rotten apple that you have to discard, however painful that may be to murder your darling. At the same time, don’t immediately write off difficult ideas — that soufflé takes time to bake! That is why our priority in the earliest stage of ideation is to murder our darlings and kiss our frogs.

All of this is hard work. It isn’t enough to merely come up with a data-driven idea, it has to be thoroughly explored and expanded upon. When super{set}’s founders, Tom Chavez and Vivek Vaidya, started Krux after selling their first company to Microsoft, they sat at the kitchen table to thoroughly explore and map out the idea even before writing a single line of code or hiring a single employee. They looked deeply at the pain points and problems potential customers faced. They asked those data-driven questions to determine what the vectors were for a solution. They developed a theory of the customer and who might make a purchase decision. They looked at the competition to see how others had tackled similar problems. Building out Krux was still a long, slow, difficult challenge, but the strategic planning of the solution early on is what led to a bigger, stronger outcome when Krux sold to Salesforce in 2016.

At super{set}, we apply this deliberate approach of early formation discipline at scale. Whether obvious or obscure, we have built a process and an infrastructure to put pen to paper to ascertain where on the map of startup opportunities an idea lies. We call this process the Solution Memo — and we’ll dig into the specifics of it in our next post.

Feeling hungry? You’re not alone — learn what it takes to become a super{set} co-founder here.

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read more

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read more

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read more

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read more

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Announcing Jon Suarez-Davis (jsd) as super{set}’s Chief Commercial Officer: jsd tells us in his own words why he's joining super{set}

read more

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The super{set} studio model for early-stage venture It is still early days for the startup studio model. We know this because at super{set} we still get questions from experienced operators and investors. One investor that we’ve known for years recently asked us: “you have a fund — aren’t you just a venture capital firm with a different label?”

read more

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This post was written by Ketch Sales Director, Sheridan Rice, as part of our #PassTheMic series.

read more

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read more

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The decision to start a company – or to join an early stage one – is an act of the gut. On good days, I see it as a quasi-spiritual commitment. On bad days, I see it as sheer irrationality. Whichever it is, you’ll be happier if you acknowledge and calmly accept the lunacy of it all...

read more

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This post was written by Ketch Solutions Engineer, Sahiti Surapaneni, as part of our #PassTheMic series.

read more

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read more

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Coming up with new company ideas is easy: we take the day off, go to the park, and let the thoughts arrive like butterflies. Maybe we grab a coconut from that guy for a little buzz. While this describes a pleasant day in San Francisco, it couldn’t be further from the truth of what we do at super{set}. If only we could pull great ideas out of thin air. Unfortunately, it just doesn’t work that way.

read more

How To Avoid Observability MELTdown

o11y - What is it? Why is it important? What are the tools you need? More importantly - how can you adopt an observability mindset? Habu Software Architect Siddharth Sharma reports from his session at super{summit} 2022.

read more

When Inference Meets Engineering

Othmane Rifki, Principal Applied Scientist at super{set} company Spectrum Labs, reports from the session he led at super{summit} 2022: "When Inference Meets Engineering." Using super{set} companies as examples, Othmane reveals the 3 ways that data science can benefit from engineering workflows to deliver business value.

read more

Why Head of Product is Our First Co-Founder

At super{set}, we stand side-by-side and pick up the shovel with our co-founders. Our first outside co-founder at a super{set} company is usually a Head of Product. Let’s unpack each portion of that title....

read more