Rather what is feasible is predicting the probability of reaching the next stage. A large part of that is avoiding stepping on the many landmines and pitfalls that many companies who have tracked similar territory have fallen prey to. It is these patterns that startups can uncover by finding poor performing benchmarks in the Startup Compass and that we’ve begun to index in the Investor Compass with more than automatic 50 due diligence tests. In this sense, it could be argued that, success, at least in part, exists in the negative space of failure. In response, many have rebutted that breakthrough companies are defined by the rules of convention they brake.
Yet, if companies are breaking rules, they shouldn’t be doing so unwittingly, and that is what we can help them recognize. Furthermore, innovation does not come from entirely throwing out the proverbial rulebook of convention. Rather, innovative companies bet their success on flipping a few key assumptions on their head, and keep the much of the rest fairly standard.
At the same time we should not underestimate the power of data to enable new novel applications. Every new industry that data flows into it seems to disrupt. The game of baseball changed when Billy Beane brought in the quants. Wall Street is now run primarily by high frequency trading algorithms designed by quants. Why hasn’t data disrupted the startup world yet?
1) We haven’t been measuring the right metrics.
Most attempts to quantitatively analyze startups have failed because people have tried to apply the same financial models to startups that are traditionally used for large publicly traded companies. But startups are not small versions of large companies.
1) 我々は正しい指標を使って測定していない。
定量的にスタートアップを分析する試みの多くは失敗している、なぜなら人々は大きな株式公開企業に対して伝統的に使われてきたのと同じ金融モデルをスタートアップに適用しようとするからだ 。しかしスタートアップは大企業がそのまま小さくなったものではない。
This is the dollhouse fallacy at work. Revenue growth doesn’t count as progress for startups. The Startup Genome describes progress for a startup as evolving through 7 developmental stages on the path to becoming a large company: Discovery, Validation, Efficiency, Scale, Sustain, Conversation, Decline. In the the first 4 stages of a startup’s lifecycle, the metrics that are most relevant for measuring progress are the ones that describe how customers interact with the product. Only after the scale stage has a businesses stabilized enough for financial models to behave properly.
2) It’s been difficult to access, aggregate and analyze the right metrics.
Currently the Startup Genome Compass gets this customer interaction data through a survey, but we don’t plan to for much longer. A big wave has been brewing that is now beginning to crest.
Today, nearly every single software application a startup uses lives in the cloud. Soon all of this data will be easily accessible through APIs. This is a trend the Startup Genome will be doubling down on. Over the next year we will build APIs that will allow startups to automatically share data with us from Google Analytics, Salesforce, Quickbooks and many other applications. In return we will offer companies ever more precise insights for how they can run their company better and be more successful. Once this data stream is unlocked a new world of data driven business will soon be upon us. To hit these product milestones the Startup Genome is now raising a seed round on angel list.
If you work with startups as an investor, advisor or service provider you can sign up for the private beta of the Investor Compass here. And if you are entrepreneur you can invite your stakeholders to stay up to date on your progress here.
Let us know how you use it and how you would like to see it evolve. We look forward to exploring this possibility space with you.
そして、これをどのように利用しているか、そしてどのように進化してほしいかについてぜひ我々に知らせて欲しい。あなたと共にこの可能性にあふれた領域を探索できることを心待ちにしている。
