VC backed startups generally aspire to valuations in the hundreds of millions or even billions of dollars, but very few really consider all of the elements they’ll need to make it happen. After analyzing several startups I’ve worked with that have reached or are approaching these valuations I’ve boiled it down to four interdependent commonalities that always seem to exist. While they are easy to describe, they are of course very difficult to achieve. Still your best chance of achieving them is to know what they are.
Element 1: Gratification engine
Your gratification engine is the repeatable process of turning cold prospects into highly gratified customers. Whether you are aiming big or small, an effective gratification engine is probably the hardest of the four elements for a startup to get right. Tenacious execution works for a lot of things, but you can’t force customers to want, need or like what you have created. Building an effective gratification engine is an iterative process driven by a lot of prospective customer feedback. Once you get the basics right, your process of gratifying users can be optimized with tools like Performable for landing pages and KISSmetrics for full funnel tracking/improvement (I’m an advisor to both).
Element 2: Economic engine
Once you have figured out how to gratify prospects, your next challenge is creating a viable economic engine. For your business to be sustainable in the long run your average revenue per user will need to exceed your average cost per user. Beyond business sustainability, the right monetization approach will also be based on the value users get from your solution, the competitive environment and your ultimate growth strategy.
Element 3: Growth engine
Your growth engine is very dependent on your economic engine. If you have relatively limited revenue per user, you’ll need to pursue tactics with a very low marginal cost such as PR, SEO or viral marketing. With a higher revenue per user, you’ll also be able to effectively arbitrage growth through paid tactics like display advertising and SEM. The most valuable companies generally choose an approach that allows them to capture the biggest share of the market in a sustainable way. This often means a strategy with lower revenue per user. They don’t invest too much time in one off gimmicks, instead they focus on growth drivers that can be repeatable and scalable.
Element 4: Huge addressable market
The best opportunities generally have the hardest markets to accurately size. That’s because these are fast growing or whole new markets that are based on potential rather than existing customers. Perfect accuracy on market sizing isn’t important here. Instead creative scenarios that show how it will likely be big should generally suffice. You also want to breakdown potential segments and people that are new to the market or coming from an existing related market. Again, you just want to have approximations that are believable and big.
Start with a Hypothesis for Each Element
It is important to have a realistic hypothesis for each of these elements before you even get started with the business. If you are having a hard time creating a realistic hypothesis for one or more of these elements, your vision probably isn’t viable.
I can often look at a business for less than an hour and decide if I believe it is massively scalable opportunity based on my hypothesis for each of these. If I’m not confident on a specific element, I spend a lot of time vetting this with the CEO before committing to a project
Of course it won’t happen exactly the way you plan. The best opportunities have multiple contingency plans in case your initial theory doesn’t work. But if you can’t even creatively come up with a viable theory for each, you’ll likely have a very hard time raising VC funds. Once you have a theory for each, start with the practical bottoms up execution described in the startup pyramid post.
Great high-level post – agree on all points. My only concern is that most startups don’t typically understand how important it is to iterate and test.
My “a-ha” moment came when Eric talked to me about issue around local maximum and how even though you launch, learn and iterate – if you’re not testing new hypothesis around features / customer segments – you may miss the larger opportunity (again, assuming you haven’t acheived P/M fit).
He sums it up nicely here:
Learning is better than optimization (the local maximum problem) http://bit.ly/9hxaUb
This is a fabulous post. I have tried dozens of times to articulate the points made here and never come close to be so concise and hitting the real key points.
Sean, I have been thinking about this constantly since we met up on Friday. I keep testing these four points in my head against successful business models I have seen. It stands up incredibly well. Great seeing you, and hope we can catch up again soon.
I agree (and hope!) that you’re right that Engine 1 is the hardest, but not sure that tenacity doesn’t work. As the KISS metrics founder talked about in his talk on ‘pivoting’, it’s his tenacity that kept (keeps) him re-working the product until he gets it right. Seems like his idea of what the product is has changed a ton over time.. that the idea itself is only general (companies need better metrics that they can understand) and he’s just been a bull-dog about finding how to deliver something that his clients love.
I can relate: we basically failed for 3 years to hit a nerve, and launched 3 pretty serious products (flops) along the way. Only because we didn’t give up (read: gluttons for punishment) did we eventually find a product/market match that’s driving some serious viral growth.
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Hey Sean, great post! Regarding point #4, Chris Dixon had a great post on this topic a few weeks back. Any readers who are interested should definitely check it out:
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Most new web startups and existing ones are missing #2.
Venture capital and hype help bypass this element
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