tl;dr Seed startups are business model experiments and should apply the scientific method.
In 2011, my co-founder and I set out to build PrimaTable. Our vision was to bring yield management to local businesses. Our belief was that mobile technology had the potential to unlock local inventory, providing value to both consumers and small businesses. Revenue management techniques, previously seen in travel and ads, would now be possible. While PrimaTable didn’t become a stand alone business, it was a successful seed. We rapidly tested our assumptions for the business and market. We iterated through a series of products and models.
There were a handful of testable hypotheses wrapped up into our vision. Our seed round was used to test these hypotheses, mitigating risk, as fast as possible. As a startup, we sought rapid growth and were predicated on the belief that smartphones represented a market dislocation that newly enabled SMB merchant acquisition to be cost effective.
A startups is a business model experiment. As a recovering engineer / data scientist, experiment has a specific meaning. Wikipedia has the definition as “an orderly procedure carried out with the goal of verifying, refuting, or establishing the validity of a hypothesis.” A seed stage startup, is a means for testing the viability of a business model.
At a seed stage startup, there are a plethora of risks that should be understood. By investing time and or money, both founders and investors should explicitly recognize the testable risks and seek to validate or refute as fast as possible. This maximizes value creation.
A minimal viable product is in essence applying the scientific method to the process of product development. As a former product guy, the product is important, but is ultimately slave to the model. There are many minimal or even maximal viable products that aren’t venture scale companies. By applying the scientific method to a business model, the sum total of the product, market, go to market, a startup can be determined to be viable.
With the benefit of seeing many more companies from the other side of the table at Redpoint, most startups have only a few principal risks. It is a worthwhile exercise to build a business model (that will be wrong) to make sure the points of leverage are determined. For a social product, it might be the K factor. For a marketplace, it might be cost of acquisition of supply (or demand). Some risks aren’t testable until you achieve scale, and ultimately require belief. But more often, startups can test the linchpin risk very quickly. Mitigating that risk will create value.
Naturally, most experiments will fail, but the mentality should be to assume success and viability and test as fast as possible. The opportunity cost of time is the biggest danger in a rapidly changing field.