Startups are temporary organizations that are designed to evolve into large companies. They move through 6 stages of development throughout their lifecycle: Discovery, Validation, Efficiency, Scale, Sustain & Conservation. Early stage startups are designed to search for product/market fit under conditions of extreme uncertainty. Late stage startups are designed to search for a repeatable and scalable business model and then scale into large companies designed to execute under conditions of high certainty.
Every startup has an actual stage and a behavioral stage. Actual stage is measured by customer response to a product. We measure it by looking at metrics like numbers of users, user growth, activation rate, retention rate and revenue. The behavioral stage is made up 5 top level dimensions that the startup can control. The 5 dimensions are Customer, Product, Team, Financials and Business Model. Each dimension, both the actual and the 5 behavioral dimensions are always classified into one of the 6 developmental stages.
A startup is classified as inconsistent when any behavioral dimension is at a stage that is different than the actual stage. When a behavioral dimension is at a stage larger than the actual stage we call this premature scaling. Its lesser known sibling, dysfunctional scaling, occurs when the stage of a behavioral dimension is smaller than the actual stage.
A clear example of premature scaling would be a web startup that rapidly scales up its team to 30-40 people before it has any customers. In this example, the actual stage of the startup would be in Validation (Stage 2) but the behavioral stage of the team would be in Scale (Stage 4).
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A pivot is a change to one or more component of your business model. This includes: Value proposition, Channels, Key Partners, Key Activities, Key Resources, Customer Relationship, Customer Segments, Cost Structure or Revenue Streams.
Our foundational structure of startup assessment is the startup lifecycle. Understanding where a startup is in their lifecycle allows us to assess their progress. The startup lifecycle is made of 6 stages of development, where each stage is made up of levels of substages. This creates a directed tree structure and allows for more granular assessment by being able to pinpoint the main drivers of progress at each stage. These stages make up the Startup Lifecycle. However, in this report only the top level stages are discussed. Our first four top-level stages are based loosely on Steve Blank’s 4 Steps to the Epiphany, but one key difference is that the Startup Lifecycle Stages are product centric rather than company centric.
Virality vs. Network effects
Many startups misunderstand network effects—self-reporting they had network effects when they didn’t. To clarify network effects are when the value to a user increases when other users join. If the product has network effects it should have little to no value if there is only one person using it, and the value should continue to increase exponentially at least until there are thousands of users, if not indefinitely.
There were 2 primary ways people misunderstood network effects:
1) People confused network effects for being able to improve their product because they had more user data or feedback.
Pandora and Google are good examples of this. They can improve their algorithms when more people use the product but the core value proposition of the product is not altered when more people use the product. All Internet products have the opportunity to gain more feedback the more users they have but some companies are able to better use user feedback to improve the product
2) People confused the difference between network effects and virality
Zynga and Groupon both have slight network effects but are driven primarily by virality not network effects. Virality is when users acquire other users, usually through some referral mechanism built into the product.
Many social games you can play by yourself but Zynga created many viral in-game incentives that reward you for inviting your friends. There are some social games where a few hundred to a few thousand people are necessary for the game to be interesting, but that is both a weak network effect and very rare. Most social games are played with 1-10 people. Groupon is similar in that in regard, as most deals require less than a few hundred people to buy for the discount (i.e. the value proposition) to be realized. The discount threshold is also reset on every deal, which is another behavior antithetical to network effects. But Groupon’s greatest channel of user acquisition is Facebook, which shows the strength of their virality.
Ecosystem throughput refers to the size of a startup ecosystem and how many startups are moving from the beginning Discovery phase to the high growth Scale stage.
Startup Output Index:
The startup output index basically represents the total activity of entrepreneurship in the region, controlling for population size and the maturity of startups in the region.
The funding index measures how active and how comprehensive the risk capital is in a startup ecosystem.
The performance index measures the total performance and performance potential of startups in a given startup ecosystem, taking into account variables such as revenue, job growth, and potential growth of companies in the startup ecosystem.
The mindset index measures how well the population of founders in a given ecosystem think like great entrepreneurs, where great entrepreneurs are visionary, resilient, have a high appetite for risk, a strong work ethic and an ability to overcome the typical challenges startups face.
The differentiation index measures how different a startup ecosystem is to Silicon Valley, taking into account the demographics and what types of companies are started there. Since Silicon Valley is the #1 ecosystem it is assumed that other ecosystems will perform better if they differentiate themselves from Silicon Valley and establish their own strengths
The trendsetter index measures how quickly a startup ecosystem adopts new technologies, management processes, and business models. Where startup ecosystems that stay on the cutting edge are expected to perform better over time.
The support index measures the quality of the startup ecosystem’s support network, including the prevalence of mentorship, service providers and types of funding sources.
The talented index basically measures how talented the founders in a given startup ecosystem are, taking into account age, education, startup experience, industry domain expertise, ability to mitigate risk and previous startup success rate.