December 16, 2022
Our Startup Dataset is a constantly growing feed of thousands of company founders and information about the companies they founded. We update this dataset on a monthly basis.
Every user of this dataset has their own investment thesis and a definition of the relevance of founders and companies. Therefore, the definition of a result that would be considered valuable can also differ a lot.
Because we’re providing a full dataset, clients have the total flexibility for creating custom filters and finding signals that are most valuable to them. To help you achieve this faster, we will share a few practical tips to help you cut through the clutter and extract value from this dataset.
The dataset contains multiple derivative fields that help identify founders with the strongest profiles.
Education and experience tags
The following tags: top_university_tag, unicorn_experience_tag, top_tech_experience_tag, and consultancy_experience_tag, mark founders with strong educational and professional backgrounds.
tags_count field shows the number of all these different indicators, meaning that filtering founders based on the logic that the profile has to have at least 1 tag will provide you with data on founders that have, at the very least, a solid educational background or relevant professional experience.
Patents and publications
founder_patents_count and founder_publications_count can also help you find founders who have a good track record of innovating in the past. The title and description list fields can also help you find specific keywords in these patents or publications that you are looking for based on your investment focus.
founded_companies_count is a useful field for distinguishing new founders from experienced founders if that is important for your firm.
The field exp_length_total_months will give you a good impression of how much professional experience the founder gathered throughout their career, which can be crucial when you want to ensure that the company they founded is successful.
Don’t forget to take advantage of company-related data fields which will help you filter the data and get the desired results.
The company_industry field contains helpful information if you want to filter data with a goal to find verticals and industries relevant to you. Please keep in mind that this method can also be somewhat restrictive and provide inconsistent results.
Instead, our advice is to build search logic based on a keyword search that mainly focuses on specific data points, such as company_description or company_specialties.
Investing time to build reliable logic that works for your particular scenario can bring the biggest success when working with this dataset. It can also help you ensure that no relevant new company is missed.
For firms focused on late-stage investing, it might be useful to refer to headcount, followers, and related fields marking changes over a specific period, such as headcount_diff_monthly, headcount_change_monthly or followers_diff_quarterly, followers_change_quarterly or similar. These fields can act like proxies for capturing traction.
Similarly, you can also refer to fields related to funding information, such as last_round_date, last_round_type or last_round_amount_raised.
You can remove companies that have already attracted funding and narrow down to the ones that raised a specific amount or reached a particular round of investment and might be looking for additional funding in the near future.
If you have any questions or need any other advice on how to work with Startup Dataset, don't hesitate to reach out to our team.
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