4 Feb 2020

Europe’s Series A Funding Landscape: A Snapshot

As part of the annual State of European Tech report, we asked Netbase Quid to help us visualise the techlash conversation in the media. Netbase Quid is a research platform that searches, analyses and visualises the world’s collective content, including news, product reviews, company investments, and forums, and we’re proud to say that we’ve also invested in the platform. Read more about them here. Last year, they helped us map the conversation around diversity in tech and uncovered some shocking statistics about how the media covers diversity.

Julie Kim, Netbase Quid’s data analyst in London, also analysed the Series A ecosystem as part of our initial research for the report. This research was not included in the final report, so we are happy to share it now here with Julie’s expert analysis. For more information on how to read the visualisations, please see the footnote.

Who are the Series A startups in Europe? Where are they headquartered, what are they focused on, who is investing in them, and how is money flowing from one country to another? Netbase Quid used our embedded organization dataset and the explicit network analysis to paint a comprehensive landscape of European Series A startups.

Among European-headquartered companies founded since 2014, there are 709 companies that have received funding amounts between $7 million and $15 million. The largest number of them are developing Big Data and AI/Machine Learning technology (10%), followed by Energy and Renewables (9.2%), Medical and Healthcare (7.6%), and Blockchain and Fintech (7.3%). While BioTech and Medical startups are building up their own area by using unique language to describe their businesses, Big Data and AI, Blockchain and Fintech, Automotive and Transportation were consistently used similar language.

35% of the companies were based in the UK, followed by the 16% in France. While the UK, France and Germany showed large variety in startups’ focus, other countries were more homogenous in terms of sector. Almost one third of the funded startups in the Netherlands focused on Automation and Transportation while one third of the ones in Finland focused on Sensor, Robotics and Drones.

Netbase Quid then scripted an explicit network to show the explicit relationship between these startups and their investors. Each node represents either an investor or a targeted startup and the nodes are connected if the investor has invested in the companies.

The explicit network allowed us to conduct a deep-dive analysis on the investors. We found that European investors tend to invest in companies from the same countries, especially investors from France, Spain, and Germany. Investors based in the Netherlands and Luxembourg were some of the most diversified while Swiss investors’ investments were divided between Switzerland and German startups.

What are European investors’ areas of focus? British investors are slightly more focused on Financial Services, French investors in SMB Lending Businesses, Belgian investors in Biotech and the Medical/Healthcare industry, Spanish investors in Services, and Italian investors in Logistics.

Among the non-European investors who invested in European startups, the US was the largest investor followed by China (this is also supported by data shown in the State of European Tech report!). While most of the non-European investors showed interest in the UK and German startups, Japanese investors showed the highest level of interest in Finnish companies.

Non-European investors tend to be more focused on specific areas compared to Europeans. While US and Chinese investors were the most diversified, Hong Kong had the highest focus on Real Estate and Properties, Israel on Media, and Canada and Japan on Auto, Insurance and Transportation companies.

Top Investors in terms of the number of companies they have invested in were as following:

If you found this analysis interesting, check out how Netbase Quid helped Atomico map the conversation around techlash in this year’s State of European Tech report or other Netbase Quid projects on our feed.

An Introduction to Netbase Quid’s Quid software and methodologies:

Quid is a big data analytics platform that inspires full picture thinking by drawing connections across massive amounts of unstructured data. The software applies advanced natural language processing technology, semantic analysis, and artificial intelligence algorithms to reveal patterns in large, unstructured datasets, and generate visualizations to allow users to gain actionable insights.

We use Boolean query to search for focus areas, topics, and keywords within the archived news and blogs, companies and patents database, as well as any custom uploaded datasets. We can filter out the search by published date time frame, source regions, source categories, or industry categories on the news; and by regions, investment amount, operating status, organization type (private/public), and founding year within the companies database. Quid then visualizes these data points based on the semantic similarity.

Companies dataset data source:

Quid embeds organizational data from CapIQ and Crunchbase. These companies include all types of companies (private, public, operating, operating as subsidiary, out of business) in the world; The investment data include private investment, M&A, public offering, minority stake made by PE/VCs, corporate venture arms, governments, institutions both in and out of the US. Some data is simply unreachable when the investors are undisclosed, or the funding amounts by investors are undisclosed. We also embed firmographic information such as the founded year, HQ location.

How to read Quid map visualization:

Each node represents a company. Links connect articles sharing similar languages in their business descriptions and websites. Clusters form when many nodes share strong similarity, revealing focus areas. When considering the network, cardinal directions (e.g. North, South, East, West) does not matter — what does matter is proximity. Two clusters which are close together share more common language than the ones that are far away. Centrality also matters — those clusters that are more central to network are more core to the market versus those on the periphery.

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