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How to work with the Smart Search list

In this text we describe the recommended flow for working with the sample file. Please note that it is the average action plan, you can modify it depending on your time constraints. The first step is to divide the sample into 3 segments: A — first 10 places in the list B — from 11 to 50 places in the list C — from 51 place to the end of the list For segments A and B, you need to do your homework. Using the fund’s profiles on the Unicorn Nest website, social networks, the fund’s website, and its mass media publications, try to find fragments that clearly describe the fund’s attitude towards your type of startups. It could even be a simple Google request: “Sequoia’s partner comments on the food delivery sector”. Collect these fragments in a table, you will need them when composing letters. Do not forget to […]

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How the Unicorn Nest scoring algorithm works

Unicorn Nest is a startup-centric service. This means that a founder doesn’t need to guess what funds might invest in their startup. We just ask them to tell us about themselves via our questionnaire and our algorithm does all the matching.   All the matching we do is based on the data we have collected about funds. Each fund in our dataset has data that describes it in dozens of categories: investment stages, portfolio company indicators and their geography data, industries of strategic focus/success and their neighboring industries, founding rounds where funds participated, general fund performance data, fund key persons geography data, etc. After we have collected all the inputs from the user, they are passed to our matching algorithms. All 20+ of our current scoring rules are built on this principle: Our fundraising expertise or user feedback generates a request for a new matching rule. The essence of these rules […]

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